Fig. 1. Hydraulic jump flow structure.

Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump

낮은 레이놀즈 수 유압 점프의 수치 모델링에서 OpenFOAM 및 FLOW-3D의 성능 평가

ArnauBayona DanielValerob RafaelGarcía-Bartuala Francisco ​JoséVallés-Morána P. AmparoLópez-Jiméneza

Abstract

A comparative performance analysis of the CFD platforms OpenFOAM and FLOW-3D is presented, focusing on a 3D swirling turbulent flow: a steady hydraulic jump at low Reynolds number. Turbulence is treated using RANS approach RNG k-ε. A Volume Of Fluid (VOF) method is used to track the air–water interface, consequently aeration is modeled using an Eulerian–Eulerian approach. Structured meshes of cubic elements are used to discretize the channel geometry. The numerical model accuracy is assessed comparing representative hydraulic jump variables (sequent depth ratio, roller length, mean velocity profiles, velocity decay or free surface profile) to experimental data. The model results are also compared to previous studies to broaden the result validation. Both codes reproduced the phenomenon under study concurring with experimental data, although special care must be taken when swirling flows occur. Both models can be used to reproduce the hydraulic performance of energy dissipation structures at low Reynolds numbers.

CFD 플랫폼 OpenFOAM 및 FLOW-3D의 비교 성능 분석이 3D 소용돌이치는 난류인 낮은 레이놀즈 수에서 안정적인 유압 점프에 초점을 맞춰 제시됩니다. 난류는 RANS 접근법 RNG k-ε을 사용하여 처리됩니다.

VOF(Volume Of Fluid) 방법은 공기-물 계면을 추적하는 데 사용되며 결과적으로 Eulerian-Eulerian 접근 방식을 사용하여 폭기가 모델링됩니다. 입방체 요소의 구조화된 메쉬는 채널 형상을 이산화하는 데 사용됩니다. 수치 모델 정확도는 대표적인 유압 점프 변수(연속 깊이 비율, 롤러 길이, 평균 속도 프로파일, 속도 감쇠 또는 자유 표면 프로파일)를 실험 데이터와 비교하여 평가됩니다.

모델 결과는 또한 결과 검증을 확장하기 위해 이전 연구와 비교됩니다. 소용돌이 흐름이 발생할 때 특별한 주의가 필요하지만 두 코드 모두 실험 데이터와 일치하는 연구 중인 현상을 재현했습니다. 두 모델 모두 낮은 레이놀즈 수에서 에너지 소산 구조의 수리 성능을 재현하는 데 사용할 수 있습니다.

Keywords

CFDRANS, OpenFOAM, FLOW-3D ,Hydraulic jump, Air–water flow, Low Reynolds number

References

Ahmed, F., Rajaratnam, N., 1997. Three-dimensional turbulent boundary layers: a
review. J. Hydraulic Res. 35 (1), 81e98.
Ashgriz, N., Poo, J., 1991. FLAIR: Flux line-segment model for advection and interface
reconstruction. Elsevier J. Comput. Phys. 93 (2), 449e468.
Bakhmeteff, B.A., Matzke, A.E., 1936. .The hydraulic jump in terms dynamic similarity. ASCE Trans. Am. Soc. Civ. Eng. 101 (1), 630e647.
Balachandar, S., Eaton, J.K., 2010. Turbulent dispersed multiphase flow. Annu. Rev.
Fluid Mech. 42 (2010), 111e133.
Bayon, A., Lopez-Jimenez, P.A., 2015. Numerical analysis of hydraulic jumps using

OpenFOAM. J. Hydroinformatics 17 (4), 662e678.
Belanger, J., 1841. Notes surl’Hydraulique, Ecole Royale des Ponts et Chaussees
(Paris, France).
Bennett, N.D., Crok, B.F.W., Guariso, G., Guillaume, J.H.A., Hamilton, S.H.,
Jakeman, A.J., Marsili-Libelli, S., Newhama, L.T.H., Norton, J.P., Perrin, C.,
Pierce, S.A., Robson, B., Seppelt, R., Voinov, A.A., Fath, B.D., Andreassian, V., 2013.
Characterising performance of environmental models. Environ. Model. Softw.
40, 1e20.
Berberovic, E., 2010. Investigation of Free-surface Flow Associated with Drop
Impact: Numerical Simulations and Theoretical Modeling. Imperial College of
Science, Technology and Medicine, UK.
Bidone, G., 1819. Report to Academie Royale des Sciences de Turin, s  eance. Le 
Remou et sur la Propagation des Ondes, 12, pp. 21e112.
Biswas, R., Strawn, R.C., 1998. Tetrahedral and hexahedral mesh adaptation for CFD
problems. Elsevier Appl. Numer. Math. 26 (1), 135e151.
Blocken, B., Gualtieri, C., 2012. Ten iterative steps for model development and
evaluation applied to computational fluid dynamics for environmental fluid
mechanics. Environ. Model. Softw. 33, 1e22.
Bombardelli, F.A., Meireles, I., Matos, J., 2011. Laboratory measurements and multiblock numerical simulations of the mean flow and turbulence in the nonaerated skimming flow region of steep stepped spillways. Springer Environ.
Fluid Mech. 11 (3), 263e288.
Bombardelli, F.A., 2012. Computational multi-phase fluid dynamics to address flows
past hydraulic structures. In: 4th IAHR International Symposium on Hydraulic
Structures, 9e11 February 2012, Porto, Portugal, 978-989-8509-01-7.
Borges, J.E., Pereira, N.H., Matos, J., Frizell, K.H., 2010. Performance of a combined
three-hole conductivity probe for void fraction and velocity measurement in
airewater flows. Exp. fluids 48 (1), 17e31.
Borue, V., Orszag, S., Staroslesky, I., 1995. Interaction of surface waves with turbulence: direct numerical simulations of turbulent open channel flow. J. Fluid
Mech. 286, 1e23.
Boussinesq, J., 1871. Theorie de l’intumescence liquide, applelee onde solitaire ou de
translation, se propageantdans un canal rectangulaire. Comptes Rendus l’Academie Sci. 72, 755e759.
Bradley, J.N., Peterka, A.J., 1957. The hydraulic design of stilling Basins : hydraulic
jumps on a horizontal Apron (Basin I). In: Proceedings ASCE, J. Hydraulics
Division.
Bradshaw, P., 1996. Understanding and prediction of turbulent flow. Elsevier Int. J.
heat fluid flow 18 (1), 45e54.
Bung, D.B., 2013. Non-intrusive detection of airewater surface roughness in selfaerated chute flows. J. Hydraulic Res. 51 (3), 322e329.
Bung, D., Schlenkhoff, A., 2010. Self-aerated Skimming Flow on Embankment
Stepped Spillways-the Effect of Additional Micro-roughness on Energy Dissipation and Oxygen Transfer. IAHR European Congress.
Caisley, M.E., Bombardelli, F.A., Garcia, M.H., 1999. Hydraulic Model Study of a Canoe
Chute for Low-head Dams in Illinois. Civil Engineering Studies, Hydraulic Engineering Series No-63. University of Illinois at Urbana-Champaign.
Carvalho, R., Lemos, C., Ramos, C., 2008. Numerical computation of the flow in
hydraulic jump stilling basins. J. Hydraulic Res. 46 (6), 739e752.
Celik, I.B., Ghia, U., Roache, P.J., 2008. Procedure for estimation and reporting of
uncertainty due to discretization in CFD applications. ASME J. Fluids Eng. 130
(7), 1e4.
Chachereau, Y., Chanson, H., 2011. .Free-surface fluctuations and turbulence in hydraulic jumps. Exp. Therm. Fluid Sci. 35 (6), 896e909.
Chanson, H. (Ed.), 2015. Energy Dissipation in Hydraulic Structures. CRC Press.
Chanson, H., 2007. Bubbly flow structure in hydraulic jump. Eur. J. Mechanics-B/
Fluids 26.3(2007) 367e384.
Chanson, H., Carvalho, R., 2015. Hydraulic jumps and stilling basins. Chapter 4. In:
Chanson, H. (Ed.), Energy Dissipation in Hydraulic Structures. CRC Press, Taylor
& Francis Group, ABalkema Book.
Chanson, H., Gualtieri, C., 2008. Similitude and scale effects of air entrainment in
hydraulic jumps. J. Hydraulic Res. 46 (1), 35e44.
Chanson, H., Lubin, P., 2010. Discussion of “Verification and validation of a
computational fluid dynamics (CFD) model for air entrainment at spillway
aerators” Appears in the Canadian Journal of Civil Engineering 36(5): 826-838.
Can. J. Civ. Eng. 37 (1), 135e138.
Chanson, H., 1994. Drag reduction in open channel flow by aeration and suspended
load. Taylor & Francis J. Hydraulic Res. 32, 87e101.
Chanson, H., Montes, J.S., 1995. Characteristics of undular hydraulic jumps: experimental apparatus and flow patterns. J. hydraulic Eng. 121 (2), 129e144.
Chanson, H., Brattberg, T., 2000. Experimental study of the airewater shear flow in
a hydraulic jump. Int. J. Multiph. Flow 26 (4), 583e607.
Chanson, H., 2013. Hydraulics of aerated flows: qui pro quo? Taylor & Francis
J. Hydraulic Res. 51 (3), 223e243.
Chaudhry, M.H., 2007. Open-channel Flow, Springer Science & Business Media.
Chen, L., Li, Y., 1998. .A numerical method for two-phase flows with an interface.
Environ. Model. Softw. 13 (3), 247e255.
Chow, V.T., 1959. Open Channel Hydraulics. McGraw-Hill Book Company, Inc, New
York.
Daly, B.J., 1969. A technique for including surface tension effects in hydrodynamic
calculations. Elsevier J. Comput. Phys. 4 (1), 97e117.
De Padova, D., Mossa, M., Sibilla, S., Torti, E., 2013. 3D SPH modeling of hydraulic
jump in a very large channel. Taylor & Francis J. Hydraulic Res. 51 (2), 158e173.
Dewals, B., Andre, S., Schleiss, A., Pirotton, M., 2004. Validation of a quasi-2D model 
for aerated flows over stepped spillways for mild and steep slopes. Proc. 6th Int.
Conf. Hydroinformatics 1, 63e70.
Falvey, H.T., 1980. Air-water flow in hydraulic structures. NASA STI Recon Tech. Rep.
N. 81, 26429.
Fawer, C., 1937. Etude de quelquesecoulements permanents 
a filets courbes (‘Study
of some Steady Flows with Curved Streamlines’). Thesis. Imprimerie La Concorde, Lausanne, Switzerland, 127 pages (in French).
Gualtieri, C., Chanson, H., 2007. .Experimental analysis of Froude number effect on
air entrainment in the hydraulic jump. Springer Environ. Fluid Mech. 7 (3),
217e238.
Gualtieri, C., Chanson, H., 2010. Effect of Froude number on bubble clustering in a
hydraulic jump. J. Hydraulic Res. 48 (4), 504e508.
Hager, W., Sinniger, R., 1985. Flow characteristics of the hydraulic jump in a stilling
basin with an abrupt bottom rise. Taylor & Francis J. Hydraulic Res. 23 (2),
101e113.
Hager, W.H., 1992. Energy Dissipators and Hydraulic Jump, Springer.
Hager, W.H., Bremen, R., 1989. Classical hydraulic jump: sequent depths. J. Hydraulic
Res. 27 (5), 565e583.
Hartanto, I.M., Beevers, L., Popescu, I., Wright, N.G., 2011. Application of a coastal
modelling code in fluvial environments. Environ. Model. Softw. 26 (12),
1685e1695.
Hirsch, C., 2007. Numerical Computation of Internal and External Flows: the Fundamentals of Computational Fluid Dynamics. Butterworth-Heinemann, 1.
Hirt, C., Nichols, B., 1981. .Volume of fluid (VOF) method for the dynamics of free
boundaries. J. Comput. Phys. 39 (1), 201e225.
Hyman, J.M., 1984. Numerical methods for tracking interfaces. Elsevier Phys. D.
Nonlinear Phenom. 12 (1), 396e407.
Juez, C., Murillo, J., Garcia-Navarro, P., 2013. Numerical assessment of bed-load
discharge formulations for transient flow in 1D and 2D situations.
J. Hydroinformatics 15 (4).
Keyes, D., Ecer, A., Satofuka, N., Fox, P., Periaux, J., 2000. Parallel Computational Fluid
Dynamics’ 99: towards Teraflops, Optimization and Novel Formulations.
Elsevier.
Kim, J.J., Baik, J.J., 2004. A numerical study of the effects of ambient wind direction
on flow and dispersion in urban street canyons using the RNG keε turbulence
model. Atmos. Environ. 38 (19), 3039e3048.
Kim, S.-E., Boysan, F., 1999. Application of CFD to environmental flows. Elsevier
J. Wind Eng. Industrial Aerodynamics 81 (1), 145e158.
Liu, M., Rajaratnam, N., Zhu, D.Z., 2004. Turbulence structure of hydraulic jumps of
low Froude numbers. J. Hydraulic Eng. 130 (6), 511e520.
Lobosco, R., Schulz, H., Simoes, A., 2011. Analysis of Two Phase Flows on Stepped
Spillways, Hydrodynamics – Optimizing Methods and Tools. Available from. :
http://www.intechopen.com/books/hyd rodynamics-optimizing-methods-andtools/analysis-of-two-phase-flows-on-stepped-spillways. Accessed February
27th 2014.
Long, D., Rajaratnam, N., Steffler, P.M., Smy, P.R., 1991. Structure of flow in hydraulic
jumps. Taylor & Francis J. Hydraulic Res. 29 (2), 207e218.
Ma, J., Oberai, A.A., Lahey Jr., R.T., Drew, D.A., 2011. Modeling air entrainment and
transport in a hydraulic jump using two-fluid RANS and DES turbulence
models. Heat Mass Transf. 47 (8), 911e919.
Matos, J., Frizell, K., Andre, S., Frizell, K., 2002. On the performance of velocity 
measurement techniques in air-water flows. Hydraulic Meas. Exp. Methods
2002, 1e11. http://dx.doi.org/10.1061/40655(2002)58.
Meireles, I.C., Bombardelli, F.A., Matos, J., 2014. .Air entrainment onset in skimming
flows on steep stepped spillways: an analysis. J. Hydraulic Res. 52 (3), 375e385.
McDonald, P., 1971. The Computation of Transonic Flow through Two-dimensional
Gas Turbine Cascades.
Mossa, M., 1999. On the oscillating characteristics of hydraulic jumps, Journal of
Hydraulic Research. Taylor &Francis 37 (4), 541e558.
Murzyn, F., Chanson, H., 2009a. Two-phase Gas-liquid Flow Properties in the Hydraulic Jump: Review and Perspectives. Nova Science Publishers.
Murzyn, F., Chanson, H., 2009b. Experimental investigation of bubbly flow and
turbulence in hydraulic jumps. Environ. Fluid Mech. 2, 143e159.
Murzyn, F., Mouaze, D., Chaplin, J.R., 2007. Airewater interface dynamic and free
surface features in hydraulic jumps. J. Hydraulic Res. 45 (5), 679e685.
Murzyn, F., Mouaze, D., Chaplin, J., 2005. Optical fiber probe measurements of
bubbly flow in hydraulic jumps. Elsevier Int. J. Multiph. Flow 31 (1), 141e154.
Nagosa, R., 1999. Direct numerical simulation of vortex structures and turbulence
scalar transfer across a free surface in a fully developed turbulence. Phys. Fluids
11, 1581e1595.
Noh, W.F., Woodward, P., 1976. SLIC (Simple Line Interface Calculation), Proceedings
of the Fifth International Conference on Numerical Methods in Fluid Dynamics
June 28-July 2. 1976 Twente University, Enschede, pp. 330e340.
Oertel, M., Bung, D.B., 2012. Initial stage of two-dimensional dam-break waves:
laboratory versus VOF. J. Hydraulic Res. 50 (1), 89e97.
Olivari, D., Benocci, C., 2010. Introduction to Mechanics of Turbulence. Von Karman
Institute for Fluid Dynamics.
Omid, M.H., Omid, M., Varaki, M.E., 2005. Modelling hydraulic jumps with artificial
neural networks. Thomas Telford Proc. ICE-Water Manag. 158 (2), 65e70.
OpenFOAM, 2011. OpenFOAM: the Open Source CFD Toolbox User Guide. The Free
Software Foundation Inc.
Peterka, A.J., 1984. Hydraulic design of spillways and energy dissipators. A water
resources technical publication. Eng. Monogr. 25.
Pope, S.B., 2000. Turbulent Flows. Cambridge university press.
Pfister, M., 2011. Chute aerators: steep deflectors and cavity subpressure, Journal of
hydraulic engineering. Am. Soc. Civ. Eng. 137 (10), 1208e1215.
Prosperetti, A., Tryggvason, G., 2007. Computational Methods for Multiphase Flow.
Cambridge University Press.
Rajaratnam, N., 1965. The hydraulic jump as a Wall Jet. Proc. ASCE, J. Hydraul. Div. 91
(HY5), 107e132.
Resch, F., Leutheusser, H., 1972. Reynolds stress measurements in hydraulic jumps.
Taylor & Francis J. Hydraulic Res. 10 (4), 409e430.
Romagnoli, M., Portapila, M., Morvan, H., 2009. Computational simulation of a
hydraulic jump (original title, in Spanish: “Simulacioncomputacional del
resaltohidraulico”), MecanicaComputacional, XXVIII, pp. 1661e1672.
Rouse, H., Siao, T.T., Nagaratnam, S., 1959. Turbulence characteristics of the hydraulic jump. Trans. ASCE 124, 926e966.
Rusche, H., 2002. Computational Fluid Dynamics of Dispersed Two-phase Flows at
High Phase Fractions. Imperial College of Science, Technology and Medicine, UK.
Saint-Venant, A., 1871. Theorie du movement non permanent des eaux, avec
application aux crues des riviereset a l’introduction de mareesdansleurslits.
Comptesrendus des seances de l’Academie des Sciences.
Schlichting, H., Gersten, K., 2000. Boundary-layer Theory. Springer.
Spalart, P.R., 2000. Strategies for turbulence modelling and simulations. Int. J. Heat
Fluid Flow 21 (3), 252e263.
Speziale, C.G., Thangam, S., 1992. Analysis of an RNG based turbulence model for
separated flows. Int. J. Eng. Sci. 30 (10), 1379eIN4.
Toge, G.E., 2012. The Significance of Froude Number in Vertical Pipes: a CFD Study.
University of Stavanger, Norway.
Ubbink, O., 1997. Numerical Prediction of Two Fluid Systems with Sharp Interfaces.
Imperial College of Science, Technology and Medicine, UK.
Valero, D., García-Bartual, R., 2016. Calibration of an air entrainment model for CFD
spillway applications. Adv. Hydroinformatics 571e582. http://dx.doi.org/
10.1007/978-981-287-615-7_38. P. Gourbesville et al. Springer Water.
Valero, D., Bung, D.B., 2015. Hybrid investigations of air transport processes in
moderately sloped stepped spillway flows. In: E-Proceedings of the 36th IAHR
World Congress, 28 June e 3 July, 2015 (The Hague, the Netherlands).
Van Leer, B., 1977. Towards the ultimate conservative difference scheme III. Upstream-centered finite-difference schemes for ideal compressible flow. J.
Comput. Phys 23 (3), 263e275.
Von Karman, T., 1930. MechanischeAhnlichkeit und Turbulenz, Nachrichten von der
Gesellschaft der WissenschaftenzuGottingen. Fachgr. 1 Math. 5, 58 € e76.
Wang, H., Murzyn, F., Chanson, H., 2014a. Total pressure fluctuations and two-phase
flow turbulence in hydraulic jumps. Exp. Fluids 55.11(2014) Pap. 1847, 1e16
(DOI: 10.1007/s00348-014-1847-9).
Wang, H., Felder, S., Chanson, H., 2014b. An experimental study of turbulent twophase flow in hydraulic jumps and application of a triple decomposition
technique. Exp. Fluids 55.7(2014) Pap. 1775, 1e18. http://dx.doi.org/10.1007/
s00348-014-1775-8.
Wang, H., Chanson, H., 2015a. .Experimental study of turbulent fluctuations in
hydraulic jumps. J. Hydraul. Eng. 141 (7) http://dx.doi.org/10.1061/(ASCE)
HY.1943-7900.0001010. Paper 04015010, 10 pages.
Wang, H., Chanson, H., 2015b. Integral turbulent length and time scales in hydraulic
jumps: an experimental investigation at large Reynolds numbers. In: E-Proceedings of the 36th IAHR World Congress 28 June e 3 July, 2015, The
Netherlands.
Weller, H., Tabor, G., Jasak, H., Fureby, C., 1998. A tensorial approach to computational continuum mechanics using object-oriented techniques. Comput. Phys.
12, 620e631.
Wilcox, D., 1998. Turbulence Modeling for CFD, DCW Industries. La Canada, California (USA).
Witt, A., Gulliver, J., Shen, L., June 2015. Simulating air entrainment and vortex
dynamics in a hydraulic jump. Int. J. Multiph. Flow 72, 165e180. ISSN 0301-

  1. http://dx.doi.org/10.1016/j.ijmultiphaseflow.2015.02.012. http://www.
    sciencedirect.com/science/article/pii/S0301932215000336.
    Wood, I.R., 1991. Air Entrainment in Free-surface Flows, IAHR Hydraulic Design
    Manual No.4, Hydraulic Design Considerations. Balkema Publications, Rotterdam, The Netherlands.
    Yakhot, V., Orszag, S., Thangam, S., Gatski, T., Speziale, C., 1992. Development of
    turbulence models for shear flows by a double expansion technique, Physics of
    Fluids A: fluid Dynamics (1989-1993). AIP Publ. 4 (7), 1510e1520.
    Youngs, D.L., 1984. An interface tracking method for a 3D Eulerian hydrodynamics
    code. Tech. Rep. 44 (92), 35e35.
    Zhang, G., Wang, H., Chanson, H., 2013. Turbulence and aeration in hydraulic jumps:
    free-surface fluctuation and integral turbulent scale measurements. Environ.
    fluid Mech. 13 (2), 189e204.
    Zhang, W., Liu, M., Zhu, D.Z., Rajaratnam, N., 2014. Mean and turbulent bubble
    velocities in free hydraulic jumps for small to intermediate froude numbers.
    J. Hydraulic Eng.
Figure 17. Longitudinal turbulent kinetic energy distribution on the smooth and triangular macroroughnesses: (A) Y/2; (B) Y/6.

Numerical Simulations of the Flow Field of a Submerged Hydraulic Jump over Triangular Macroroughnesses

Triangular Macroroughnesses 대한 잠긴 수압 점프의 유동장 수치 시뮬레이션

by Amir Ghaderi 1,2,Mehdi Dasineh 3,Francesco Aristodemo 2 andCostanza Aricò 4,*1Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan 537138791, Iran2Department of Civil Engineering, University of Calabria, Arcavacata, 87036 Rende, Italy3Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh 8311155181, Iran4Department of Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy*Author to whom correspondence should be addressed.Academic Editor: Anis YounesWater202113(5), 674; https://doi.org/10.3390/w13050674

Abstract

The submerged hydraulic jump is a sudden change from the supercritical to subcritical flow, specified by strong turbulence, air entrainment and energy loss. Despite recent studies, hydraulic jump characteristics in smooth and rough beds, the turbulence, the mean velocity and the flow patterns in the cavity region of a submerged hydraulic jump in the rough beds, especially in the case of triangular macroroughnesses, are not completely understood. The objective of this paper was to numerically investigate via the FLOW-3D model the effects of triangular macroroughnesses on the characteristics of submerged jump, including the longitudinal profile of streamlines, flow patterns in the cavity region, horizontal velocity profiles, streamwise velocity distribution, thickness of the inner layer, bed shear stress coefficient, Turbulent Kinetic Energy (TKE) and energy loss, in different macroroughness arrangements and various inlet Froude numbers (1.7 < Fr1 < 9.3). To verify the accuracy and reliability of the present numerical simulations, literature experimental data were considered.

Keywords: submerged hydraulic jumptriangular macroroughnessesTKEbed shear stress coefficientvelocityFLOW-3D model

수중 유압 점프는 강한 난류, 공기 동반 및 에너지 손실로 지정된 초임계에서 아임계 흐름으로의 급격한 변화입니다. 최근 연구에도 불구하고, 특히 삼각형 거시적 거칠기의 경우, 평활 및 거친 베드에서의 수압 점프 특성, 거친 베드에서 잠긴 수압 점프의 공동 영역에서 난류, 평균 속도 및 유동 패턴이 완전히 이해되지 않았습니다.

이 논문의 목적은 유선의 종방향 프로파일, 캐비티 영역의 유동 패턴, 수평 속도 프로파일, 스트림 방향 속도 분포, 두께를 포함하여 서브머지드 점프의 특성에 대한 삼각형 거시 거칠기의 영향을 FLOW-3D 모델을 통해 수치적으로 조사하는 것이었습니다.

내부 층의 층 전단 응력 계수, 난류 운동 에너지(TKE) 및 에너지 손실, 다양한 거시 거칠기 배열 및 다양한 입구 Froude 수(1.7 < Fr1 < 9.3). 현재 수치 시뮬레이션의 정확성과 신뢰성을 검증하기 위해 문헌 실험 데이터를 고려했습니다.

 Introduction

격렬한 난류 혼합과 기포 동반이 있는 수압 점프는 초임계에서 아임계 흐름으로의 변화 과정으로 간주됩니다[1]. 자유 및 수중 유압 점프는 일반적으로 게이트, 배수로 및 둑과 같은 수력 구조 아래의 에너지 손실에 적합합니다. 매끄러운 베드에서 유압 점프의 특성은 널리 연구되었습니다[2,3,4,5,6,7,8,9].

베드의 거칠기 요소가 매끄러운 베드와 비교하여 수압 점프의 특성에 어떻게 영향을 미치는지 예측하기 위해 거시적 거칠기에 대한 자유 및 수중 수력 점프에 대해 여러 실험 및 수치 연구가 수행되었습니다. Ead와 Rajaratnam[10]은 사인파 거대 거칠기에 대한 수리학적 점프의 특성을 조사하고 무차원 분석을 통해 수면 프로파일과 배출을 정규화했습니다.

Tokyayet al. [11]은 두 사인 곡선 거대 거칠기에 대한 점프 길이 비율과 에너지 손실이 매끄러운 베드보다 각각 35% 더 작고 6% 더 높다는 것을 관찰했습니다. Abbaspur et al. [12]는 6개의 사인파형 거대 거칠기에 대한 수력학적 점프의 특성을 연구했습니다. 그 결과, 꼬리수심과 점프길이는 평상보다 낮았고 Froude 수는 점프길이에 큰 영향을 미쳤습니다.

Shafai-Bejestan과 Neisi[13]는 수압 점프에 대한 마름모꼴 거대 거칠기의 영향을 조사했습니다. 결과는 마름모꼴 거시 거칠기를 사용하면 매끄러운 침대와 비교하여 꼬리 수심과 점프 길이를 감소시키는 것으로 나타났습니다. Izadjoo와 Shafai-Bejestan[14]은 다양한 사다리꼴 거시 거칠기에 대한 수압 점프를 연구했습니다.

그들은 전단응력계수가 평활층보다 10배 이상 크고 점프길이가 50% 감소하는 것을 관찰하였습니다. Nikmehr과 Aminpour[15]는 Flow-3D 모델 버전 11.2[16]를 사용하여 사다리꼴 블록이 있는 거시적 거칠기에 대한 수력학적 점프의 특성을 조사했습니다. 결과는 거시 거칠기의 높이와 거리가 증가할수록 전단 응력 계수뿐만 아니라 베드 근처에서 속도가 감소하는 것으로 나타났습니다.

Ghaderi et al. [17]은 다양한 형태의 거시 거칠기(삼각형, 정사각형 및 반 타원형)에 대한 자유 및 수중 수력 점프 특성을 연구했습니다. 결과는 Froude 수의 증가에 따라 자유 및 수중 점프에서 전단 응력 계수, 에너지 손실, 수중 깊이, 미수 깊이 및 상대 점프 길이가 증가함을 나타냅니다.

자유 및 수중 점프에서 가장 높은 전단 응력과 에너지 손실은 삼각형의 거시 거칠기가 존재할 때 발생했습니다. Elsebaie와 Shabayek[18]은 5가지 형태의 거시적 거칠기(삼각형, 사다리꼴, 2개의 측면 경사 및 직사각형이 있는 정현파)에 대한 수력학적 점프의 특성을 연구했습니다. 결과는 모든 거시적 거칠기에 대한 에너지 손실이 매끄러운 베드에서보다 15배 이상이라는 것을 보여주었습니다.

Samadi-Boroujeni et al. [19]는 다양한 각도의 6개의 삼각형 거시 거칠기에 대한 수력 점프를 조사한 결과 삼각형 거시 거칠기가 평활 베드에 비해 점프 길이를 줄이고 에너지 손실과 베드 전단 응력 계수를 증가시키는 것으로 나타났습니다.

Ahmed et al. [20]은 매끄러운 베드와 삼각형 거시 거칠기에서 수중 수력 점프 특성을 조사했습니다. 결과는 부드러운 침대와 비교할 때 잠긴 깊이와 점프 길이가 감소했다고 밝혔습니다. 표 1은 다른 연구자들이 제시한 과거의 유압 점프에 대한 실험 및 수치 연구의 세부 사항을 나열합니다.

Table 1. Main characteristics of some past experimental and numerical studies on hydraulic jumps.

ReferenceShape Bed-Channel Type-
Jump Type
Channel Dimension (m)Roughness (mm)Fr1Investigated Flow
Properties
Ead and Rajaratnam [10]-Smooth and rough beds-Rectangular channel-Free jumpCL1 = 7.60
CW2 = 0.44
CH3 = 0.60
-Corrugated sheets (RH4 = 13 and 22)4–10-Upstream and tailwater depths-Jump length-Roller length-Velocity-Water surface profile
Tokyay et al. [11]-Smooth and rough beds-Rectangular channel-Free jumpCL = 10.50
CW = 0.253
CH = 0.432
-Two sinusoidal corrugated (RH = 10 and 13)5–12-Depth ratio-Jump length-Energy loss
Izadjoo and Shafai-Bejestan [14]-Smooth and rough beds-Two rectangular-channel-Free jumpCL = 1.2, 9
CW = 0.25, 0.50
CH = 0.40
Baffle with trapezoidal cross section
(RH: 13 and 26)
6–12-Upstream and tailwater depths-Jump length-Velocity-Bed shear stress coefficient
Abbaspour et al. [12]-Horizontal bed with slope 0.002-Rectangular channel—smooth and rough beds-Free jumpCL = 10
CW = 0.25
CH = 0.50
-Sinusoidal bed (RH = 15,20, 25 and 35)3.80–8.60-Water surface profile-Depth ratio-Jump length-Energy loss-Velocity profiles-Bed shear stress coefficient
Shafai-Bejestan and Neisi [13]-Smooth and rough beds-Rectangular channel-Free jumpCL = 7.50
CW = 0.35
CH = 0.50
Lozenge bed4.50–12-Sequent depth-Jump length
Elsebaie and Shabayek [18]-Smooth and rough beds-Rectangular channel-With side slopes of 45 degrees for two trapezoidal and triangular macroroughnesses and of 60 degrees for other trapezoidal macroroughnesses-Free jumpCL = 9
CW = 0.295
CH = 0.32
-Sinusoidal-Triangular-Trapezoidal with two side-Rectangular-(RH = 18 and corrugation wavelength = 65)50-Water surface profile-Sequent depth-Jump length-Bed shear stress coefficient
Samadi-Boroujeni et al. [19]-Rectangular channel-Smooth and rough beds-Free jumpCL = 12
CW = 0.40
CH = 0.40
-Six triangular corrugated (RH = 2.5)6.10–13.10-Water surface profile-Sequent depth-Jump length-Energy loss-Velocity profiles-Bed shear stress coefficient
Ahmed et al. [20]-Smooth and rough beds-Rectangular channel-Submerged jumpCL = 24.50
CW = 0.75
CH = 0.70
-Triangular corrugated sheet (RH = 40)1.68–9.29-Conjugated and tailwater depths-Submerged ratio-Deficit depth-Relative jump length-Jump length-Relative roller jump length-Jump efficiency-Bed shear stress coefficient
Nikmehr and Aminpour [15]-Horizontal bed with slope 0.002-Rectangular channel-Rough bed-Free jumpCL = 12
CW = 0.25
CH = 0.50
-Trapezoidal blocks (RH = 2, 3 and 4)5.01–13.70-Water surface profile-Sequent depth-Jump length-Roller length-Velocity
Ghaderi et al. [17]-Smooth and rough beds-Rectangular channel-Free and submerged jumpCL = 4.50
CW = 0.75
CH = 0.70
-Triangular, square and semi-oval macroroughnesses (RH = 40 and distance of roughness of I = 40, 80, 120, 160 and 200)1.70–9.30-Horizontal velocity distributions-Bed shear stress coefficient-Sequent depth ratio and submerged depth ratio-Jump length-Energy loss
Present studyRectangular channel
Smooth and rough beds
Submerged jump
CL = 4.50
CW = 0.75
CH = 0.70
-Triangular macroroughnesses (RH = 40 and distance of roughness of I = 40, 80, 120, 160 and 200)1.70–9.30-Longitudinal profile of streamlines-Flow patterns in the cavity region-Horizontal velocity profiles-Streamwise velocity distribution-Bed shear stress coefficient-TKE-Thickness of the inner layer-Energy loss

CL1: channel length, CW2: channel width, CH3: channel height, RH4: roughness height.

이전에 논의된 조사의 주요 부분은 실험실 접근 방식을 기반으로 하며 사인파, 마름모꼴, 사다리꼴, 정사각형, 직사각형 및 삼각형 매크로 거칠기가 공액 깊이, 잠긴 깊이, 점프 길이, 에너지 손실과 같은 일부 자유 및 수중 유압 점프 특성에 어떻게 영향을 미치는지 조사합니다.

베드 및 전단 응력 계수. 더욱이, 저자[17]에 의해 다양한 형태의 거시적 거칠기에 대한 수력학적 점프에 대한 이전 발표된 논문을 참조하면, 삼각형의 거대조도는 가장 높은 층 전단 응력 계수 및 에너지 손실을 가지며 또한 가장 낮은 잠긴 깊이, tailwater를 갖는 것으로 관찰되었습니다.

다른 거친 모양, 즉 정사각형 및 반 타원형과 부드러운 침대에 비해 깊이와 점프 길이. 따라서 본 논문에서는 삼각형 매크로 거칠기를 사용하여(일정한 거칠기 높이가 T = 4cm이고 삼각형 거칠기의 거리가 I = 4, 8, 12, 16 및 20cm인 다른 T/I 비율에 대해), 특정 캐비티 영역의 유동 패턴, 난류 운동 에너지(TKE) 및 흐름 방향 속도 분포와 같은 연구가 필요합니다.

CFD(Computational Fluid Dynamics) 방법은 자유 및 수중 유압 점프[21]와 같은 복잡한 흐름의 모델링 프로세스를 수행하는 중요한 도구로 등장하며 수중 유압 점프의 특성은 CFD 시뮬레이션을 사용하여 정확하게 예측할 수 있습니다 [22,23 ].

본 논문은 초기에 수중 유압 점프의 주요 특성, 수치 모델에 대한 입력 매개변수 및 Ahmed et al.의 참조 실험 조사를 제시합니다. [20], 검증 목적으로 보고되었습니다. 또한, 본 연구에서는 유선의 종방향 프로파일, 캐비티 영역의 유동 패턴, 수평 속도 프로파일, 내부 층의 두께, 베드 전단 응력 계수, TKE 및 에너지 손실과 같은 특성을 조사할 것입니다.

Figure 1. Definition sketch of a submerged hydraulic jump at triangular macroroughnesses.
Figure 1. Definition sketch of a submerged hydraulic jump at triangular macroroughnesses.

Table 2. Effective parameters in the numerical model.

Bed TypeQ
(l/s)
I
(cm)
T (cm)d (cm)y1
(cm)
y4
(cm)
Fr1= u1/(gy1)0.5SRe1= (u1y1)/υ
Smooth30, 4551.62–3.839.64–32.101.7–9.30.26–0.5039,884–59,825
Triangular macroroughnesses30, 454, 8, 12, 16, 20451.62–3.846.82–30.081.7–9.30.21–0.4439,884–59,825
Figure 2. Longitudinal profile of the experimental flume (Ahmed et al. [20]).
Figure 2. Longitudinal profile of the experimental flume (Ahmed et al. [20]).

Table 3. Main flow variables for the numerical and physical models (Ahmed et al. [20]).

ModelsBed TypeQ (l/s)d (cm)y1 (cm)u1 (m/s)Fr1
Numerical and PhysicalSmooth4551.62–3.831.04–3.701.7–9.3
T/I = 0.54551.61–3.831.05–3.711.7–9.3
T/I = 0.254551.60–3.841.04–3.711.7–9.3
Figure 3. The boundary conditions governing the simulations.
Figure 3. The boundary conditions governing the simulations.
Figure 4. Sketch of mesh setup.
Figure 4. Sketch of mesh setup.

Table 4. Characteristics of the computational grids.

MeshNested Block Cell Size (cm)Containing Block Cell Size (cm)
10.551.10
20.651.30
30.851.70

Table 5. The numerical results of mesh convergence analysis.

ParametersAmounts
fs1 (-)7.15
fs2 (-)6.88
fs3 (-)6.19
K (-)5.61
E32 (%)10.02
E21 (%)3.77
GCI21 (%)3.03
GCI32 (%)3.57
GCI32/rp GCI210.98
Figure 5. Time changes of the flow discharge in the inlet and outlet boundaries conditions (A): Q = 0.03 m3/s (B): Q = 0.045 m3/s.
Figure 5. Time changes of the flow discharge in the inlet and outlet boundaries conditions (A): Q = 0.03 m3/s (B): Q = 0.045 m3/s.
Figure 6. The evolutionary process of a submerged hydraulic jump on the smooth bed—Q = 0.03 m3/s.
Figure 6. The evolutionary process of a submerged hydraulic jump on the smooth bed—Q = 0.03 m3/s.
Figure 7. Numerical versus experimental basic parameters of the submerged hydraulic jump. (A): y3/y1; and (B): y4/y1.
Figure 7. Numerical versus experimental basic parameters of the submerged hydraulic jump. (A): y3/y1; and (B): y4/y1.
Figure 8. Velocity vector field and flow pattern through the gate in a submerged hydraulic jump condition: (A) smooth bed; (B) triangular macroroughnesses.
Figure 8. Velocity vector field and flow pattern through the gate in a submerged hydraulic jump condition: (A) smooth bed; (B) triangular macroroughnesses.
Figure 9. Velocity vector distributions in the x–z plane (y = 0) within the cavity region.
Figure 9. Velocity vector distributions in the x–z plane (y = 0) within the cavity region.
Figure 10. Typical vertical distribution of the mean horizontal velocity in a submerged hydraulic jump [46].
Figure 10. Typical vertical distribution of the mean horizontal velocity in a submerged hydraulic jump [46].
Figure 11. Typical horizontal velocity profiles in a submerged hydraulic jump on smooth bed and triangular macroroughnesses.
Figure 11. Typical horizontal velocity profiles in a submerged hydraulic jump on smooth bed and triangular macroroughnesses.
Figure 12. Horizontal velocity distribution at different distances from the sluice gate for the different T/I for Fr1 = 6.1
Figure 12. Horizontal velocity distribution at different distances from the sluice gate for the different T/I for Fr1 = 6.1
Figure 13. Stream-wise velocity distribution for the triangular macroroughnesses with T/I = 0.5 and 0.25.
Figure 13. Stream-wise velocity distribution for the triangular macroroughnesses with T/I = 0.5 and 0.25.
Figure 14. Dimensionless horizontal velocity distribution in the submerged hydraulic jump for different Froude numbers in triangular macroroughnesses.
Figure 14. Dimensionless horizontal velocity distribution in the submerged hydraulic jump for different Froude numbers in triangular macroroughnesses.
Figure 15. Spatial variations of (umax/u1) and (δ⁄y1).
Figure 15. Spatial variations of (umax/u1) and (δ⁄y1).
Figure 16. The shear stress coefficient (ε) versus the inlet Froude number (Fr1).
Figure 16. The shear stress coefficient (ε) versus the inlet Froude number (Fr1).
Figure 17. Longitudinal turbulent kinetic energy distribution on the smooth and triangular macroroughnesses: (A) Y/2; (B) Y/6.
Figure 17. Longitudinal turbulent kinetic energy distribution on the smooth and triangular macroroughnesses: (A) Y/2; (B) Y/6.
Figure 18. The energy loss (EL/E3) of the submerged jump versus inlet Froude number (Fr1).
Figure 18. The energy loss (EL/E3) of the submerged jump versus inlet Froude number (Fr1).

Conclusions

  • 본 논문에서는 유선의 종방향 프로파일, 공동 영역의 유동 패턴, 수평 속도 프로파일, 스트림 방향 속도 분포, 내부 층의 두께, 베드 전단 응력 계수, 난류 운동 에너지(TKE)를 포함하는 수중 유압 점프의 특성을 제시하고 논의했습니다. ) 및 삼각형 거시적 거칠기에 대한 에너지 손실. 이러한 특성은 FLOW-3D® 모델을 사용하여 수치적으로 조사되었습니다. 자유 표면을 시뮬레이션하기 위한 VOF(Volume of Fluid) 방법과 난류 RNG k-ε 모델이 구현됩니다. 본 모델을 검증하기 위해 평활층과 삼각형 거시 거칠기에 대해 수치 시뮬레이션과 실험 결과를 비교했습니다. 본 연구의 다음과 같은 결과를 도출할 수 있다.
  • 개발 및 개발 지역의 삼각형 거시 거칠기의 흐름 패턴은 수중 유압 점프 조건의 매끄러운 바닥과 비교하여 더 작은 영역에서 동일합니다. 삼각형의 거대 거칠기는 거대 거칠기 사이의 공동 영역에서 또 다른 시계 방향 와류의 형성으로 이어집니다.
  • T/I = 1, 0.5 및 0.33과 같은 거리에 대해 속도 벡터 분포는 캐비티 영역에서 시계 방향 소용돌이를 표시하며, 여기서 속도의 크기는 평균 유속보다 훨씬 작습니다. 삼각형 거대 거칠기(T/I = 0.25 및 0.2) 사이의 거리를 늘리면 캐비티 영역에 크기가 다른 두 개의 소용돌이가 형성됩니다.
  • 삼각형 거시조도 사이의 거리가 충분히 길면 흐름이 다음 조도에 도달할 때까지 속도 분포가 회복됩니다. 그러나 짧은 거리에서 흐름은 속도 분포의 적절한 회복 없이 다음 거칠기에 도달합니다. 따라서 거시 거칠기 사이의 거리가 감소함에 따라 마찰 계수의 증가율이 감소합니다.
  • 삼각형의 거시적 거칠기에서, 잠수 점프의 지정된 섹션에서 최대 속도는 자유 점프보다 높은 값으로 이어집니다. 또한, 수중 점프에서 두 가지 유형의 베드(부드러움 및 거친 베드)에 대해 깊이 및 와류 증가로 인해 베드로부터의 최대 속도 거리는 감소합니다. 잠수 점프에서 경계층 두께는 자유 점프보다 얇습니다.
  • 매끄러운 베드의 난류 영역은 게이트로부터의 거리에 따라 생성되고 자유 표면 롤러 영역 근처에서 발생하는 반면, 거시적 거칠기에서는 난류가 게이트 근처에서 시작되어 더 큰 강도와 제한된 스위프 영역으로 시작됩니다. 이는 반시계 방향 순환의 결과입니다. 거시 거칠기 사이의 공간에서 자유 표면 롤러 및 시계 방향 와류.
  • 삼각 거시 거칠기에서 침지 점프의 베드 전단 응력 계수와 에너지 손실은 유입구 Froude 수의 증가에 따라 증가하는 매끄러운 베드에서 발견된 것보다 더 큽니다. T/I = 0.50 및 0.20에서 최고 및 최저 베드 전단 응력 계수 및 에너지 손실이 평활 베드에 비해 거칠기 요소의 거리가 증가함에 따라 발생합니다.
  • 거의 거칠기 요소가 있는 삼각형 매크로 거칠기의 존재에 의해 주어지는 점프 길이와 잠긴 수심 및 꼬리 수심의 감소는 결과적으로 크기, 즉 길이 및 높이가 감소하는 정수조 설계에 사용될 수 있습니다.
  • 일반적으로 CFD 모델은 다양한 수력 조건 및 기하학적 배열을 고려하여 잠수 점프의 특성 예측을 시뮬레이션할 수 있습니다. 캐비티 영역의 흐름 패턴, 흐름 방향 및 수평 속도 분포, 베드 전단 응력 계수, TKE 및 유압 점프의 에너지 손실은 수치적 방법으로 시뮬레이션할 수 있습니다. 그러나 거시적 차원과 유동장 및 공동 유동의 변화에 ​​대한 다양한 배열에 대한 연구는 향후 과제로 남아 있다.

References

  1. White, F.M. Viscous Fluid Flow, 2nd ed.; McGraw-Hill University of Rhode Island: Montreal, QC, Canada, 1991. [Google Scholar]
  2. Launder, B.E.; Rodi, W. The turbulent wall jet. Prog. Aerosp. Sci. 197919, 81–128. [Google Scholar] [CrossRef]
  3. McCorquodale, J.A. Hydraulic jumps and internal flows. In Encyclopedia of Fluid Mechanics; Cheremisinoff, N.P., Ed.; Golf Publishing: Houston, TX, USA, 1986; pp. 120–173. [Google Scholar]
  4. Federico, I.; Marrone, S.; Colagrossi, A.; Aristodemo, F.; Antuono, M. Simulating 2D open-channel flows through an SPH model. Eur. J. Mech. B Fluids 201234, 35–46. [Google Scholar] [CrossRef]
  5. Khan, S.A. An analytical analysis of hydraulic jump in triangular channel: A proposed model. J. Inst. Eng. India Ser. A 201394, 83–87. [Google Scholar] [CrossRef]
  6. Mortazavi, M.; Le Chenadec, V.; Moin, P.; Mani, A. Direct numerical simulation of a turbulent hydraulic jump: Turbulence statistics and air entrainment. J. Fluid Mech. 2016797, 60–94. [Google Scholar] [CrossRef]
  7. Daneshfaraz, R.; Ghahramanzadeh, A.; Ghaderi, A.; Joudi, A.R.; Abraham, J. Investigation of the effect of edge shape on characteristics of flow under vertical gates. J. Am. Water Works Assoc. 2016108, 425–432. [Google Scholar] [CrossRef]
  8. Azimi, H.; Shabanlou, S.; Kardar, S. Characteristics of hydraulic jump in U-shaped channels. Arab. J. Sci. Eng. 201742, 3751–3760. [Google Scholar] [CrossRef]
  9. De Padova, D.; Mossa, M.; Sibilla, S. SPH numerical investigation of characteristics of hydraulic jumps. Environ. Fluid Mech. 201818, 849–870. [Google Scholar] [CrossRef]
  10. Ead, S.A.; Rajaratnam, N. Hydraulic jumps on corrugated beds. J. Hydraul. Eng. 2002128, 656–663. [Google Scholar] [CrossRef]
  11. Tokyay, N.D. Effect of channel bed corrugations on hydraulic jumps. In Proceedings of the World Water and Environmental Resources Congress 2005, Anchorage, AK, USA, 15–19 May 2005; pp. 1–9. [Google Scholar]
  12. Abbaspour, A.; Dalir, A.H.; Farsadizadeh, D.; Sadraddini, A.A. Effect of sinusoidal corrugated bed on hydraulic jump characteristics. J. Hydro-Environ. Res. 20093, 109–117. [Google Scholar] [CrossRef]
  13. Shafai-Bejestan, M.S.; Neisi, K. A new roughened bed hydraulic jump stilling basin. Asian J. Appl. Sci. 20092, 436–445. [Google Scholar] [CrossRef]
  14. Izadjoo, F.; Shafai-Bejestan, M. Corrugated bed hydraulic jump stilling basin. J. Appl. Sci. 20077, 1164–1169. [Google Scholar] [CrossRef]
  15. Nikmehr, S.; Aminpour, Y. Numerical Simulation of Hydraulic Jump over Rough Beds. Period. Polytech. Civil Eng. 201764, 396–407. [Google Scholar] [CrossRef]
  16. Flow Science Inc. FLOW-3D V 11.2 User’s Manual; Flow Science Inc.: Santa Fe, NM, USA, 2016. [Google Scholar]
  17. Ghaderi, A.; Dasineh, M.; Aristodemo, F.; Ghahramanzadeh, A. Characteristics of free and submerged hydraulic jumps over different macroroughnesses. J. Hydroinform. 202022, 1554–1572. [Google Scholar] [CrossRef]
  18. Elsebaie, I.H.; Shabayek, S. Formation of hydraulic jumps on corrugated beds. Int. J. Civil Environ. Eng. IJCEE–IJENS 201010, 37–47. [Google Scholar]
  19. Samadi-Boroujeni, H.; Ghazali, M.; Gorbani, B.; Nafchi, R.F. Effect of triangular corrugated beds on the hydraulic jump characteristics. Can. J. Civil Eng. 201340, 841–847. [Google Scholar] [CrossRef]
  20. Ahmed, H.M.A.; El Gendy, M.; Mirdan, A.M.H.; Ali, A.A.M.; Haleem, F.S.F.A. Effect of corrugated beds on characteristics of submerged hydraulic jump. Ain Shams Eng. J. 20145, 1033–1042. [Google Scholar] [CrossRef]
  21. Viti, N.; Valero, D.; Gualtieri, C. Numerical simulation of hydraulic jumps. Part 2: Recent results and future outlook. Water 201911, 28. [Google Scholar] [CrossRef]
  22. Gumus, V.; Simsek, O.; Soydan, N.G.; Akoz, M.S.; Kirkgoz, M.S. Numerical modeling of submerged hydraulic jump from a sluice gate. J. Irrig. Drain. Eng. 2016142, 04015037. [Google Scholar] [CrossRef]
  23. Jesudhas, V.; Roussinova, V.; Balachandar, R.; Barron, R. Submerged hydraulic jump study using DES. J. Hydraul. Eng. 2017143, 04016091. [Google Scholar] [CrossRef]
  24. Rajaratnam, N. The hydraulic jump as a wall jet. J. Hydraul. Div. 196591, 107–132. [Google Scholar] [CrossRef]
  25. Hager, W.H. Energy Dissipaters and Hydraulic Jump; Kluwer Academic Publisher: Dordrecht, The Netherlands, 1992; pp. 185–224. [Google Scholar]
  26. Long, D.; Steffler, P.M.; Rajaratnam, N. LDA study of flow structure in submerged Hydraulic jumps. J. Hydraul. Res. 199028, 437–460. [Google Scholar] [CrossRef]
  27. Chow, V.T. Open Channel Hydraulics; McGraw-Hill: New York, NY, USA, 1959. [Google Scholar]
  28. Wilcox, D.C. Turbulence Modeling for CFD, 3rd ed.; DCW Industries, Inc.: La Canada, CA, USA, 2006. [Google Scholar]
  29. Hirt, C.W.; Nichols, B.D. Volume of fluid (VOF) method for the dynamics of free boundaries. J. Comput. Phys. 198139, 201–225. [Google Scholar] [CrossRef]
  30. Pourshahbaz, H.; Abbasi, S.; Pandey, M.; Pu, J.H.; Taghvaei, P.; Tofangdar, N. Morphology and hydrodynamics numerical simulation around groynes. ISH J. Hydraul. Eng. 2020, 1–9. [Google Scholar] [CrossRef]
  31. Choufu, L.; Abbasi, S.; Pourshahbaz, H.; Taghvaei, P.; Tfwala, S. Investigation of flow, erosion, and sedimentation pattern around varied groynes under different hydraulic and geometric conditions: A numerical study. Water 201911, 235. [Google Scholar] [CrossRef]
  32. Zhenwei, Z.; Haixia, L. Experimental investigation on the anisotropic tensorial eddy viscosity model for turbulence flow. Int. J. Heat Technol. 201634, 186–190. [Google Scholar]
  33. Carvalho, R.; Lemos Ramo, C. Numerical computation of the flow in hydraulic jump stilling basins. J. Hydraul. Res. 200846, 739–752. [Google Scholar] [CrossRef]
  34. Bayon, A.; Valero, D.; García-Bartual, R.; López-Jiménez, P.A. Performance assessment of Open FOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environ. Model. Softw. 201680, 322–335. [Google Scholar] [CrossRef]
  35. Daneshfaraz, R.; Ghaderi, A.; Akhtari, A.; Di Francesco, S. On the Effect of Block Roughness in Ogee Spillways with Flip Buckets. Fluids 20205, 182. [Google Scholar] [CrossRef]
  36. Ghaderi, A.; Abbasi, S. CFD simulation of local scouring around airfoil-shaped bridge piers with and without collar. Sādhanā 201944, 216. [Google Scholar] [CrossRef]
  37. Ghaderi, A.; Daneshfaraz, R.; Dasineh, M.; Di Francesco, S. Energy Dissipation and Hydraulics of Flow over Trapezoidal–Triangular Labyrinth Weirs. Water 202012, 1992. [Google Scholar] [CrossRef]
  38. Ghaderi, A.; Abbasi, S.; Abraham, J.; Azamathulla, H.M. Efficiency of trapezoidal labyrinth shaped stepped spillways. Flow Meas. Instrum. 202072, 101711. [Google Scholar] [CrossRef]
  39. Yakhot, V.; Orszag, S.A. Renormalization group analysis of turbulence. I. basic theory. J. Sci. Comput. 19861, 3–51. [Google Scholar] [CrossRef] [PubMed]
  40. Biscarini, C.; Di Francesco, S.; Ridolfi, E.; Manciola, P. On the simulation of floods in a narrow bending valley: The malpasset dam break case study. Water 20168, 545. [Google Scholar] [CrossRef]
  41. Ghaderi, A.; Daneshfaraz, R.; Abbasi, S.; Abraham, J. Numerical analysis of the hydraulic characteristics of modified labyrinth weirs. Int. J. Energy Water Resour. 20204, 425–436. [Google Scholar] [CrossRef]
  42. Alfonsi, G. Reynolds-averaged Navier–Stokes equations for turbulence modeling. Appl. Mech. Rev. 200962. [Google Scholar] [CrossRef]
  43. Abbasi, S.; Fatemi, S.; Ghaderi, A.; Di Francesco, S. The Effect of Geometric Parameters of the Antivortex on a Triangular Labyrinth Side Weir. Water 202113, 14. [Google Scholar] [CrossRef]
  44. Celik, I.B.; Ghia, U.; Roache, P.J. Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. J. Fluids Eng. 2008130, 0780011–0780013. [Google Scholar]
  45. Khan, M.I.; Simons, R.R.; Grass, A.J. Influence of cavity flow regimes on turbulence diffusion coefficient. J. Vis. 20069, 57–68. [Google Scholar] [CrossRef]
  46. Javanappa, S.K.; Narasimhamurthy, V.D. DNS of plane Couette flow with surface roughness. Int. J. Adv. Eng. Sci. Appl. Math. 2020, 1–13. [Google Scholar] [CrossRef]
  47. Nasrabadi, M.; Omid, M.H.; Farhoudi, J. Submerged hydraulic jump with sediment-laden flow. Int. J. Sediment Res. 201227, 100–111. [Google Scholar] [CrossRef]
  48. Pourabdollah, N.; Heidarpour, M.; Abedi Koupai, J. Characteristics of free and submerged hydraulic jumps in different stilling basins. In Water Management; Thomas Telford Ltd.: London, UK, 2019; pp. 1–11. [Google Scholar]
  49. Rajaratnam, N. Turbulent Jets; Elsevier Science: Amsterdam, The Netherlands, 1976. [Google Scholar]
  50. Aristodemo, F.; Marrone, S.; Federico, I. SPH modeling of plane jets into water bodies through an inflow/outflow algorithm. Ocean Eng. 2015105, 160–175. [Google Scholar] [CrossRef]
  51. Shekari, Y.; Javan, M.; Eghbalzadeh, A. Three-dimensional numerical study of submerged hydraulic jumps. Arab. J. Sci. Eng. 201439, 6969–6981. [Google Scholar] [CrossRef]
  52. Khan, A.A.; Steffler, P.M. Physically based hydraulic jump model for depth-averaged computations. J. Hydraul. Eng. 1996122, 540–548. [Google Scholar] [CrossRef]
  53. De Dios, M.; Bombardelli, F.A.; García, C.M.; Liscia, S.O.; Lopardo, R.A.; Parravicini, J.A. Experimental characterization of three-dimensional flow vortical structures in submerged hydraulic jumps. J. Hydro-Environ. Res. 201715, 1–12. [Google Scholar] [CrossRef]
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electromagnetic metal casting computation designs Fig1

A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues

The European Physical Journal Plus volume 136, Article number: 704 (2021) Cite this article

Abstract

Electromagnetic metal casting (EMC) is a casting technique that uses electromagnetic energy to heat metal powders. It is a faster, cleaner, and less time-consuming operation. Solid metals create issues in electromagnetics since they reflect the electromagnetic radiation rather than consume it—electromagnetic energy processing results in sounded pieces with higher-ranking material properties and a more excellent microstructure solution. For the physical production of the electromagnetic casting process, knowledge of electromagnetic material interaction is critical. Even where the heated material is an excellent electromagnetic absorber, the total heating quality is sometimes insufficient. Numerical modelling works on finding the proper coupled effects between properties to bring out the most effective operation. The main parameters influencing the quality of output of the EMC process are: power dissipated per unit volume into the material, penetration depth of electromagnetics, complex magnetic permeability and complex dielectric permittivity. The contact mechanism and interference pattern also, in turn, determines the quality of the process. Only a few parameters, such as the environment’s temperature, the interference pattern, and the rate of metal solidification, can be controlled by AI models. Neural networks are used to achieve exact outcomes by stimulating the neurons in the human brain. Additive manufacturing (AM) is used to design mold and cores for metal casting. The models outperformed the traditional DFA optimization approach, which is susceptible to local minima. The system works only offline, so real-time analysis and corrections are not yet possible.

Korea Abstract

전자기 금속 주조 (EMC)는 전자기 에너지를 사용하여 금속 분말을 가열하는 주조 기술입니다. 더 빠르고 깨끗하며 시간이 덜 소요되는 작업입니다.

고체 금속은 전자기 복사를 소비하는 대신 반사하기 때문에 전자기학에서 문제를 일으킵니다. 전자기 에너지 처리는 더 높은 등급의 재료 특성과 더 우수한 미세 구조 솔루션을 가진 사운드 조각을 만듭니다.

전자기 주조 공정의 물리적 생산을 위해서는 전자기 물질 상호 작용에 대한 지식이 중요합니다. 가열된 물질이 우수한 전자기 흡수재인 경우에도 전체 가열 품질이 때때로 불충분합니다. 수치 모델링은 가장 효과적인 작업을 이끌어 내기 위해 속성 간의 적절한 결합 효과를 찾는데 사용됩니다.

EMC 공정의 출력 품질에 영향을 미치는 주요 매개 변수는 단위 부피당 재료로 분산되는 전력, 전자기의 침투 깊이, 복합 자기 투과성 및 복합 유전율입니다. 접촉 메커니즘과 간섭 패턴 또한 공정의 품질을 결정합니다. 환경 온도, 간섭 패턴 및 금속 응고 속도와 같은 몇 가지 매개 변수 만 AI 모델로 제어 할 수 있습니다.

신경망은 인간 뇌의 뉴런을 자극하여 정확한 결과를 얻기 위해 사용됩니다. 적층 제조 (AM)는 금속 주조용 몰드 및 코어를 설계하는 데 사용됩니다. 모델은 로컬 최소값에 영향을 받기 쉬운 기존 DFA 최적화 접근 방식을 능가했습니다. 이 시스템은 오프라인에서만 작동하므로 실시간 분석 및 수정은 아직 불가능합니다.

electromagnetic metal casting computation designs Fig1
electromagnetic metal casting computation designs Fig1
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electromagnetic metal casting computation designs Fig2
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References

  1. 1.J. Sun, W. Wang, Q. Yue, Review on electromagnetic-matter interaction fundamentals and efficient electromagnetic-associated heating strategies. Materials 9(4), 231 (2016). https://doi.org/10.3390/ma9040231ADS Article Google Scholar 
  2. 2.E. Ghasali, A. Fazili, M. Alizadeh, K. Shirvanimoghaddam, T. Ebadzadeh, Evaluation of microstructure and mechanical properties of Al-TiC metal matrix composite prepared by conventional, electromagnetic and spark plasma sintering methods. Materials 10(11), 1255 (2017). https://doi.org/10.3390/ma10111255ADS Article Google Scholar 
  3. 3.D. Agrawal, Latest global developments in electromagnetic materials processing. Mater. Res. Innov. 14(1), 3–8 (2010). https://doi.org/10.1179/143307510×12599329342926Article Google Scholar 
  4. 4.S. Singh, P. Singh, D. Gupta, V. Jain, R. Kumar, S. Kaushal, Development and characterization of electromagnetic processed cast iron joint. Eng. Sci. Technol. Int. J. (2018). https://doi.org/10.1016/j.jestch.2018.10.012Article Google Scholar 
  5. 5.S. Singh, D. Gupta, V. Jain, Electromagnetic melting and processing of metal–ceramic composite castings. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 232(7), 1235–1243 (2016). https://doi.org/10.1177/0954405416666900Article Google Scholar 
  6. 6.S. Singh, D. Gupta, V. Jain, Novel electromagnetic composite casting process: theory, feasibility and characterization. Mater. Des. 111, 51–59 (2016). https://doi.org/10.1016/j.matdes.2016.08.071Article Google Scholar 
  7. 7.J. Lucas, J, What are electromagnetics? LiveScience. (2018). https://www.livescience.com/50259-Electromagnetics.html
  8. 8.R. Samyal, A.K. Bagha, R. Bedi, the casting of materials using electromagnetic energy: a review. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.02.255Article Google Scholar 
  9. 9.S. Singh, D. Gupta, V. Jain, Processing of Ni-WC-8Co MMC casting through electromagnetic melting. Mater. Manuf. Process. (2017). https://doi.org/10.1080/10426914.2017.1291954Article Google Scholar 
  10. 10.R. Singh, S. Singh, V. Mahajan, Investigations for dimensional accuracy of investment casting process after cycle time reduction by advancements in shell moulding. Procedia Mater. Sci. 6, 859–865 (2014). https://doi.org/10.1016/j.mspro.2014.07.103Article Google Scholar 
  11. 11.R.R. Mishra, A.K. Sharma, On melting characteristics of bulk Al-7039 alloy during in-situ electromagnetic casting. Appl. Therm. Eng. 111, 660–675 (2017). https://doi.org/10.1016/j.applthermaleng.2016.09.122Article Google Scholar 
  12. 12.S. Zhang, 10 Different types of casting process. (2021). MachineMfg.com, https://www.machinemfg.com/types-of-casting/
  13. 13.Envirocare, Foundry health risks. (2013). https://envirocare.org/foundry-health-risks/
  14. 14.S.S. Gajmal, D.N. Raut, A review of opportunities and challenges in electromagnetic assisted casting. Recent Trends Product. Eng. 2(1) (2019)
  15. 15.R.R. Mishra, A.K. Sharma, Electromagnetic-material interaction phenomena: heating mechanisms, challenges and opportunities in material processing. Compos. Part A (2015). https://doi.org/10.1016/j.compositesa.2015.10.035Article Google Scholar 
  16. 16.S. Chandrasekaran, T. Basak, S. Ramanathan, Experimental and theoretical investigation on electromagnetic melting of metals. J. Mater. Process. Technol. 211(3), 482–487 (2011). https://doi.org/10.1016/j.jmatprotec.2010.11.001Article Google Scholar 
  17. 17.C.R. Bird, J.M. Mertz, U.S. Patent No. 4655276. (U.S. Patent and Trademark Office, Washington, DC, 1987)
  18. 18.R.R. Mishra, A.K. Sharma, Experimental investigation on in-situ electromagnetic casting of copper. IOP Conf. Ser. Mater. Sci. Eng. 346, 012052 (2018). https://doi.org/10.1088/1757-899x/346/1/012052Article Google Scholar 
  19. 19.V. Gangwar, S. Kumar, V. Singh, H. Singh, Effect of process parameters on hardness of AA-6063 in-situ electromagnetic casting by using taguchi method, in IOP Conference Series: Materials Science and Engineering, vol. 804(1) (IOP Publishing, 2020), p. 012019
  20. 20.X. Ye, S. Guo, L. Yang, J. Gao, J. Peng, T. Hu, L. Wang, M. Hou, Q. Luo, New utilization approach of electromagnetic thermal energy: preparation of metallic matrix diamond tool bit by electromagnetic hot-press sintering. J. Alloy. Compd. (2018). https://doi.org/10.1016/j.jallcom.2018.03.183Article Google Scholar 
  21. 21.S. Das, A.K. Mukhopadhyay, S. Datta, D. Basu, Prospects of Electromagnetic processing: an overview. Bull. Mater. Sci. 32(1), 1–13 (2009). https://doi.org/10.1007/s12034-009-0001-4Article Google Scholar 
  22. 22.K.L. Glass, D.M. Ashby, U.S. Patent No. 9050656. (U.S. Patent and Trademark Office, Washington, DC, 2015)
  23. 23.S. Verma, P. Gupta, S. Srivastava, S. Kumar, A. Anand, An overview: casting/melting of non ferrous metallic materials using domestic electromagnetic oven. J. Mater. Sci. Mech. Eng. 4(4), (2017). p-ISSN: 2393-9095; e-ISSN: 2393-9109
  24. 24.S.S. Panda, V. Singh, A. Upadhyaya, D. Agrawal, Sintering response of austenitic (316L) and ferritic (434L) stainless steel consolidated in conventional and electromagnetic furnaces. Scripta Mater. 54(12), 2179–2183 (2006). https://doi.org/10.1016/j.scriptamat.2006.02.034Article Google Scholar 
  25. 25.Y. Zhang, S. Yang, S. Wang, X. Liu, L. Li, Microwave/freeze casting assisted fabrication of carbon frameworks derived from embedded upholder in tremella for superior performance supercapacitors. Energy Storage Mater. (2018). https://doi.org/10.1016/j.ensm.2018.08.006Article Google Scholar 
  26. 26.D. Thomas, P. Abhilash, M.T. Sebastian, Casting and characterization of LiMgPO4 glass free LTCC tape for electromagnetic applications. J. Eur. Ceram. Soc. 33(1), 87–93 (2013). https://doi.org/10.1016/j.jeurceramsoc.2012.08.002Article Google Scholar 
  27. 27.M.H. Awida, N. Shah, B. Warren, E. Ripley, A.E. Fathy, Modeling of an industrial Electromagnetic furnace for metal casting applications. 2008 IEEE MTT-S Int. Electromagn. Symp. Digest. (2008). https://doi.org/10.1109/mwsym.2008.4633143Article Google Scholar 
  28. 28.P.K. Loharkar, A. Ingle, S. Jhavar, Parametric review of electromagnetic-based materials processing and its applications. J. Market. Res. 8(3), 3306–3326 (2019). https://doi.org/10.1016/j.jmrt.2019.04.004Article Google Scholar 
  29. 29.E.B. Ripley, J.A. Oberhaus, WWWeb search power page-melting and heat treating metals using electromagnetic heating-the potential of electromagnetic metal processing techniques for a wide variety of metals and alloys is. Ind. Heat. 72(5), 65–70 (2005)Google Scholar 
  30. 30.J. Campbell, Complete Casting Handbook: Metal Casting Processes, Metallurgy, Techniques and Design (Butterworth-Heinemann, 2015)Google Scholar 
  31. 31.B. Ravi, Metal Casting: Computer-Aided Design and Analysis, 1st edn. (PHI Learning Ltd, 2005)Google Scholar 
  32. 32.D.E. Clark, W.H. Sutton, Electromagnetic processing of materials. Annu. Rev. Mater. Sci. 26(1), 299–331 (1996)ADS Article Google Scholar 
  33. 33.A.D. Abdullin, New capabilities of software package ProCAST 2011 for modeling foundry operations. Metallurgist 56(5–6), 323–328 (2012). https://doi.org/10.1007/s11015-012-9578-8Article Google Scholar 
  34. 34.J. Ha, P. Cleary, V. Alguine, T. Nguyen, Simulation of die filling in gravity die casting using SPH and MAGMAsoft, in Proceedings of 2nd International Conference on CFD in Minerals & Process Industries (1999) pp. 423–428
  35. 35.M. Sirviö, M. Woś, Casting directly from a computer model by using advanced simulation software FLOW-3D Cast Ž. Arch. Foundry Eng. 9(1), 79–82 (2009)Google Scholar 
  36. 36.NOVACAST Systems, Nova-Solid/Flow Brochure, NOVACAST, Ronneby (2015)
  37. 37.AutoCAST-X1 Brochure, 3D Foundry Tech, Mumbai
  38. 38.EKK, Inc. Metal Casting Simulation Software and Consulting Services, CAPCAST Brochure
  39. 39.P. Muenprasertdee, Solidification modeling of iron castings using SOLIDCast (2007)
  40. 40.CasCAE, CT-CasTest Inc. Oy, Kerava
  41. 41.E. Dominguez-Tortajada, J. Monzo-Cabrera, A. Diaz-Morcillo, Uniform electric field distribution in electromagnetic heating applicators by means of genetic algorithms optimization of dielectric multilayer structures. IEEE Trans. Electromagn. Theory Tech. 55(1), 85–91 (2007). https://doi.org/10.1109/tmtt.2006.886913ADS Article Google Scholar 
  42. 42.B. Warren, M.H. Awida, A.E. Fathy, Electromagnetic heating of metals. IET Electromagn. Antennas Propag. 6(2), 196–205 (2012)Article Google Scholar 
  43. 43.S. Ashouri, M. Nili-Ahmadabadi, M. Moradi, M. Iranpour, Semi-solid microstructure evolution during reheating of aluminum A356 alloy deformed severely by ECAP. J. Alloy. Compd. 466(1–2), 67–72 (2008). https://doi.org/10.1016/j.jallcom.2007.11.010Article Google Scholar 
  44. 44.Penn State, Metal Parts Made In The Electromagnetic Oven. ScienceDaily. (1999) Retrieved May 8, 2021, from www.sciencedaily.com/releases/1999/06/990622055733.htm
  45. 45.R.R. Mishra, A.K. Sharma, A review of research trends in electromagnetic processing of metal-based materials and opportunities in electromagnetic metal casting. Crit. Rev. Solid State Mater. Sci. 41(3), 217–255 (2016). https://doi.org/10.1080/10408436.2016.1142421ADS Article Google Scholar 
  46. 46.D.K. Ghodgaonkar, V.V. Varadan, V.K. Varadan, Free-space measurement of complex permittivity and complex permeability of magnetic materials at Electromagnetic frequencies. IEEE Trans. Instrum. Meas. 39(2), 387–394 (1990). https://doi.org/10.1109/19.52520Article Google Scholar 
  47. 47.J. Baker-Jarvis, E.J. Vanzura, W.A. Kissick, Improved technique for determining complex permittivity with the transmission/reflection method. Microw. Theory Tech. IEEE Trans. 38, 1096–1103 (1990)ADS Article Google Scholar 
  48. 48.M. Bologna, A. Petri, B. Tellini, C. Zappacosta, Effective magnetic permeability measurementin composite resonator structures. Instrum. Meas. IEEE Trans. 59, 1200–1206 (2010)Article Google Scholar 
  49. 49.B. Ravi, G.L. Datta, Metal casting–back to future, in 52nd Indian Foundry Congress, (2004)
  50. 50.D. El Khaled, N. Novas, J.A. Gazquez, F. Manzano-Agugliaro. Microwave dielectric heating: applications on metals processing. Renew. Sustain. Energy Rev. 82, 2880–2892 (2018). https://doi.org/10.1016/j.rser.2017.10.043Article Google Scholar 
  51. 51.H. Sekiguchi, Y. Mori, Steam plasma reforming using Electromagnetic discharge. Thin Solid Films 435, 44–48 (2003)ADS Article Google Scholar 
  52. 52.J. Sun, W. Wang, C. Zhao, Y. Zhang, C. Ma, Q. Yue, Study on the coupled effect of wave absorption and metal discharge generation under electromagnetic irradiation. Ind. Eng. Chem. Res. 53, 2042–2051 (2014)Article Google Scholar 
  53. 53.K.I. Rybakov, E.A. Olevsky, E.V. Krikun, Electromagnetic sintering: fundamentals and modeling. J. Am. Ceram. Soc. 96(4), 1003–1020 (2013). https://doi.org/10.1111/jace.12278Article Google Scholar 
  54. 54.A.K. Shukla, A. Mondal, A. Upadhyaya, Numerical modeling of electromagnetic heating. Sci. Sinter. 42(1), 99–124 (2010)Article Google Scholar 
  55. 55.M. Chiumenti, C. Agelet de Saracibar, M. Cervera, On the numerical modeling of the thermomechanical contact for metal casting analysis. J. Heat Transf. 130(6), (2008). https://doi.org/10.1115/1.2897923Article MATH Google Scholar 
  56. 56.B. Ravi, Metal Casting: Computer-Aided Design and Analysis. (PHI Learning Pvt. Ltd., 2005)
  57. 57.J.H. Lee, S.D. Noh, H.-J. Kim, Y.-S. Kang, Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors 18, 1428 (2018). https://doi.org/10.3390/s18051428ADS Article Google Scholar 
  58. 58.B. Aksoy, M. Koru, Estimation of casting mold interfacial heat transfer coefficient in pressure die casting process by artificial intelligence methods. Arab. J. Sci. Eng. 45, 8969–8980 (2020). https://doi.org/10.1007/s13369-020-04648-7Article Google Scholar 
  59. 59.S.S. Miriyala, V.R. Subramanian, K. Mitra, TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur. J. Oper. Res. 264(1), 294–309 (2018). https://doi.org/10.1016/j.ejor.2017.05.026MathSciNet Article MATH Google Scholar 
  60. 60.J.K. Kittu, G.C.M. Patel, M. Parappagoudar, Modeling of pressure die casting process: an artificial intelligence approach. Int. J. Metalcast. (2015). https://doi.org/10.1007/s40962-015-0001-7Article Google Scholar 
  61. 61.W. Chen, B. Gutmann, C.O. Kappe, Characterization of electromagnetic-induced electric discharge phenomena in metal-solvent mixtures. ChemistryOpen 1, 39–48 (2012)Article Google Scholar 
  62. 62.J. Walker, A. Prokop, C. Lynagh, B. Vuksanovich, B. Conner, K. Rogers, J. Thiel, E. MacDonald, Real-time process monitoring of core shifts during metal casting with wireless sensing and 3D sand printing. Addit. Manuf. (2019). https://doi.org/10.1016/j.addma.2019.02.018Article Google Scholar 
  63. 63.G.C. Manjunath Patel, A.K. Shettigar, M.B. Parappagoudar, A systematic approach to model and optimize wear behaviour of castings produced by squeeze casting process. J. Manuf. Process. 32, 199–212 (2018). https://doi.org/10.1016/j.jmapro.2018.02.004Article Google Scholar 
  64. 64.G.C. Manjunath Patel, P. Krishna, M.B. Parappagoudar, An intelligent system for squeeze casting process—soft computing based approach. Int. J. Adv. Manuf. Technol. 86, 3051–3065 (2016). https://doi.org/10.1007/s00170-016-8416-8Article Google Scholar 
  65. 65.M. Ferguson, R. Ak, Y.T. Lee, K.H. Law, Automatic localization of casting defects with convolutional neural networks, in 2017 IEEE International Conference on Big Data (Big Data) (Boston, MA, USA, 2017), pp. 1726–1735. https://doi.org/10.1109/BigData.2017.8258115.
  66. 66.P.K.D.V. Yarlagadda, Prediction of die casting process parameters by using an artificial neural network model for zinc alloys. Int. J. Prod. Res. 38(1), 119–139 (2000). https://doi.org/10.1080/002075400189617Article MATH Google Scholar 
  67. 67.G.C. ManjunathPatel, A.K. Shettigar, P. Krishna, M.B. Parappagoudar, Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process. Appl. Soft Comput. 59, 418–437 (2017). https://doi.org/10.1016/j.asoc.2017.06.018Article Google Scholar 
  68. 68.J. Zheng, Q. Wang, P. Zhao et al., Optimization of high-pressure die-casting process parameters using artificial neural network. Int. J. Adv. Manuf. Technol. 44, 667–674 (2009). https://doi.org/10.1007/s00170-008-1886-6Article Google Scholar 
  69. 69.E. Mares, J. Sokolowski, Artificial intelligence-based control system for the analysis of metal casting properties. J. Achiev. Mater. Manuf. Eng. 40, 149–154 (2010)Google Scholar 
  70. 70.K.S. Senthil, S. Muthukumaran, C. Chandrasekhar Reddy, Suitability of friction welding of tube to tube plate using an external tool process for different tube diameters—a study. Exp. Tech. 37(6), 8–14 (2013)Article Google Scholar 
  71. 71.N.K. Bhoi, H. Singh, S. Pratap, P.K. Jain, Electromagnetic material processing: a clean, green, and sustainable approach. Sustain. Eng. Prod. Manuf. Technol. (2019). https://doi.org/10.1016/b978-0-12-816564-5.00001-3Article Google Scholar 
  72. 72.K.S. Senthil, D.A. Daniel, An investigation of boiler grade tube and tube plate without block by using friction welding process. Mater. Today Proc. 5(2), 8567–8576 (2018)Article Google Scholar 
  73. 73.E. Hetmaniok, D. Słota, A. Zielonka, Restoration of the cooling conditions in a three-dimensional continuous casting process using artificial intelligence algorithms. Appl. Math. Modell. 39(16), 4797–4807 (2015). https://doi.org/10.1016/j.apm.2015.03.056Article MATH Google Scholar 
  74. 74.C.V. Kumar, S. Muthukumaran, A. Pradeep, S.S. Kumaran, Optimizational study of friction welding of steel tube to aluminum tube plate using an external tool process. Int. J. Mech. Mater. Eng. 6(2), 300–306 (2011)Google Scholar 
  75. 75.T. Adithiyaa, D. Chandramohan, T. Sathish, Optimal prediction of process parameters by GWO-KNN in stirring-squeeze casting of AA2219 reinforced metal matrix composites. Mater. Today Proc. 150, 1598 (2020). https://doi.org/10.1016/j.matpr.2019.10.051Article Google Scholar 
  76. 76.B.P. Pehrson, A.F. Moore (2014). U.S. Patent No. 8708031 (U.S. Patent and Trademark Office, Washington, DC, 2014)
  77. 77.Liu, J., & Rynerson, M. L. (2008). U.S. Patent No. 7,461,684. Washington, DC: U.S. Patent and Trademark Office.
  78. 78.K. Salonitis, B. Zeng, H.A. Mehrabi, M. Jolly, The challenges for energy efficient casting processes. Procedia CIRP 40, 24–29 (2016). https://doi.org/10.1016/j.procir.2016.01.043Article Google Scholar 
  79. 79.R.R. Mishra, A.K. Sharma, Effect of solidification environment on microstructure and indentation hardness of Al–Zn–Mg alloy casts developed using electromagnetic heating. Int. J. Metal Cast. 10, 1–13 (2017). https://doi.org/10.1007/s40962-017-0176-1Article Google Scholar 
  80. 80.R.R. Mishra, A.K. Sharma, Effect of susceptor and Mold material on microstructure of in-situ electromagnetic casts of Al–Zn–Mg alloy. Mater. Des. 131, 428–440 (2017). https://doi.org/10.1016/j.matdes.2017.06.038Article Google Scholar 
  81. 81.S. Kaushal, S. Bohra, D. Gupta, V. Jain, On processing and characterization of Cu–Mo-based castings through electromagnetic heating. Int. J. Metalcast. (2020). https://doi.org/10.1007/s40962-020-00481-8Article Google Scholar 
  82. 82.S. Nandwani, S. Vardhan, A.K. Bagha, A literature review on the exposure time of electromagnetic based welding of different materials. Mater. Today Proc. (2019). https://doi.org/10.1016/j.matpr.2019.10.056Article Google Scholar 
  83. 83.F.J.B. Brum, S.C. Amico, I. Vedana, J.A. Spim, Electromagnetic dewaxing applied to the investment casting process. J. Mater. Process. Technol. 209(7), 3166–3171 (2009). https://doi.org/10.1016/j.jmatprotec.2008.07.024Article Google Scholar 
  84. 84.M.P. Reddy, R.A. Shakoor, G. Parande, V. Manakari, F. Ubaid, A.M.A. Mohamed, M. Gupta, Enhanced performance of nano-sized SiC reinforced Al metal matrix nanocomposites synthesized through electromagnetic sintering and hot extrusion techniques. Prog. Nat. Sci. Mater. Int. 27(5), 606–614 (2017). https://doi.org/10.1016/j.pnsc.2017.08.015Article Google Scholar 
  85. 85.V.R. Kalamkar, K. Monkova, (Eds.), Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. (2021) https://doi.org/10.1007/978-981-15-3639-7
  86. 86.V. Bist, A.K. Sharma, P. Kumar, Development and microstructural characterisations of the lead casting using electromagnetic technology. Manager’s J. Mech. Eng. 4(4), 6 (2014). https://doi.org/10.26634/jme.4.4.2840Article Google Scholar 
  87. 87.A. Sharma, A. Chouhan, L. Pavithran, U. Chadha, S.K. Selvaraj, Implementation of LSS framework in automotive component manufacturing: a review, current scenario and future directions. Mater Today: Proc. (2021). https://doi.org/10.1016/J.MATPR.2021.02.374Article Google Scholar 
Numerical simulation of energy dissipation in crescent-shaped contraction of the flow path

Numerical simulation of energy dissipation in crescent-shaped contraction of the flow path

Authors

1 Professor, Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Iran.
2 M.sc student, Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Iran.
3 M.sc student, Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Iran

Abstract

One of the methods of controlling and reducing flow energy is the use of energy dissipating structures and the formation of hydraulic jumps. One of these types of structures is the constriction elements in the flow path, which leads to a decrease in the energy of the passing flow. In the present study, the effect of crescent-shaped contraction as an energy dissipating structure in the supercritical flow path has been investigated using FLOW-3D software. Examining the simulation results, the RNG turbulence model due to its higher accuracy and lower relative error and absolute error percentage than other models, among the RNG turbulence models, k-ε, k-ω and LES was selected. In this study, the amplitude of the Froude number after the gate as the most effective dimensionless parameter in energy dissipation varied from 2.8 to 7.5 and the values of stenosis on both sides are 5 and 7.5 cm. The results show that in all cases of using the crescent-shaped contractions, the energy consumption due to the contraction is 5 and 7.5 cm, respectively, based on the energy drop relative to the upstream of 24.62% and 29.84% and compared to the downstream 46.14% and 48.42% more than the classic free jump. Also, by examining the obtained results, it was observed that the crescent-shaped contractions have a better performance in terms of energy loss compared to the sudden contraction, obtained from the studies of previous researchers. Based on the simulation results, with increasing the upstream Froude number, the relative energy dissipation to the upstream and downstream crescent-shaped contraction increased so that the use of contraction elements reduces the downstream Froude number of the contracted section in the range of 1.6 to 3/2.

흐름 에너지를 제어하고 줄이는 방법 중 하나는 에너지 소산 구조를 사용하고 유압 점프를 형성하는 것입니다. 이러한 유형의 구조 중 하나는 흐름 경로의 수축 요소로, 통과하는 흐름의 에너지를 감소시킵니다. 현재 연구에서는 초 임계 유동 경로에서 에너지 소산 구조로서 초승달 모양의 수축 효과가 FLOW-3D 소프트웨어를 사용하여 조사되었습니다. 시뮬레이션 결과를 살펴보면 RNG 난류 모델 중 k-ε, k-ω, LES 중에서 다른 모델보다 정확도가 높고 상대 오차와 절대 오차 비율이 낮은 RNG 난류 모델을 선택했습니다. 이 연구에서 에너지 소산에서 가장 효과적인 무 차원 매개 변수 인 게이트 뒤의 Froude 수의 진폭은 2.8에서 7.5까지 다양했으며 양쪽의 협착 값은 5cm와 7.5cm입니다. 결과는 초승달 모양의 수축을 사용하는 모든 경우에서 수축으로 인한 에너지 소비는 각각 5cm와 7.5cm로 상류에 비해 에너지 강하가 24.62 % 및 29.84 %이고 하류와 비교됩니다. 고전적인 자유 점프보다 46.14 % 및 48.42 % 더 많습니다. 또한 얻어진 결과를 살펴보면 초승달 모양의 수축이 이전 연구자들의 연구에서 얻은 갑작스런 수축에 비해 에너지 손실 측면에서 더 나은 성능을 보이는 것으로 나타났습니다. 시뮬레이션 결과에 따르면 상류 Froude 수를 증가 시키면 상류 및 하류 초승달 모양의 수축에 대한 상대적 에너지 소산이 증가하여 수축 요소를 사용하면 수축 된 부분의 하류 Froude 수가 1.6 ~ 3/2 범위에서 감소합니다. .

Keywords

Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).

Continuous-Flow Separation of Magnetic Particles from Biofluids: How Does the Microdevice Geometry Determine the Separation Performance?

1Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
2William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA
*Author to whom correspondence should be addressed.
Sensors 202020(11), 3030; https://doi.org/10.3390/s20113030
Received: 16 April 2020 / Revised: 21 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Lab-on-a-Chip and Microfluidic Sensors)

Abstract

The use of functionalized magnetic particles for the detection or separation of multiple chemicals and biomolecules from biofluids continues to attract significant attention. After their incubation with the targeted substances, the beads can be magnetically recovered to perform analysis or diagnostic tests. Particle recovery with permanent magnets in continuous-flow microdevices has gathered great attention in the last decade due to the multiple advantages of microfluidics. As such, great efforts have been made to determine the magnetic and fluidic conditions for achieving complete particle capture; however, less attention has been paid to the effect of the channel geometry on the system performance, although it is key for designing systems that simultaneously provide high particle recovery and flow rates. Herein, we address the optimization of Y-Y-shaped microchannels, where magnetic beads are separated from blood and collected into a buffer stream by applying an external magnetic field. The influence of several geometrical features (namely cross section shape, thickness, length, and volume) on both bead recovery and system throughput is studied. For that purpose, we employ an experimentally validated Computational Fluid Dynamics (CFD) numerical model that considers the dominant forces acting on the beads during separation. Our results indicate that rectangular, long devices display the best performance as they deliver high particle recovery and high throughput. Thus, this methodology could be applied to the rational design of lab-on-a-chip devices for any magnetically driven purification, enrichment or isolation.

Keywords: particle magnetophoresisCFDcross sectionchip fabrication

Korea Abstract

생체 유체에서 여러 화학 물질과 생체 분자의 검출 또는 분리를위한 기능화 된 자성 입자의 사용은 계속해서 상당한 관심을 받고 있습니다. 표적 물질과 함께 배양 한 후 비드를 자기 적으로 회수하여 분석 또는 진단 테스트를 수행 할 수 있습니다. 연속 흐름 마이크로 장치에서 영구 자석을 사용한 입자 회수는 마이크로 유체의 여러 장점으로 인해 지난 10 년 동안 큰 관심을 모았습니다. 

따라서 완전한 입자 포획을 달성하기 위한 자기 및 유체 조건을 결정하기 위해 많은 노력을 기울였습니다. 그러나 높은 입자 회수율과 유속을 동시에 제공하는 시스템을 설계하는 데있어 핵심이기는 하지만 시스템 성능에 대한 채널 형상의 영향에 대해서는 덜주의를 기울였습니다. 

여기에서 우리는 자기 비드가 혈액에서 분리되고 외부 자기장을 적용하여 버퍼 스트림으로 수집되는 YY 모양의 마이크로 채널의 최적화를 다룹니다. 비드 회수 및 시스템 처리량에 대한 여러 기하학적 특징 (즉, 단면 형상, 두께, 길이 및 부피)의 영향을 연구합니다. 

이를 위해 분리 중에 비드에 작용하는 지배적인 힘을 고려하는 실험적으로 검증 된 CFD (Computational Fluid Dynamics) 수치 모델을 사용합니다. 우리의 결과는 직사각형의 긴 장치가 높은 입자 회수율과 높은 처리량을 제공하기 때문에 최고의 성능을 보여줍니다. 

따라서 이 방법론은 자기 구동 정제, 농축 또는 분리를 위한 랩온어 칩 장치의 합리적인 설계에 적용될 수 있습니다.

Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and U-shaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and U-shaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.

References

  1. Gómez-Pastora, J.; Xue, X.; Karampelas, I.H.; Bringas, E.; Furlani, E.P.; Ortiz, I. Analysis of separators for magnetic beads recovery: From large systems to multifunctional microdevices. Sep. Purif. Technol. 2017172, 16–31. [Google Scholar] [CrossRef]
  2. Wise, N.; Grob, T.; Morten, K.; Thompson, I.; Sheard, S. Magnetophoretic velocities of superparamagnetic particles, agglomerates and complexes. J. Magn. Magn. Mater. 2015384, 328–334. [Google Scholar] [CrossRef]
  3. Khashan, S.A.; Elnajjar, E.; Haik, Y. CFD simulation of the magnetophoretic separation in a microchannel. J. Magn. Magn. Mater. 2011323, 2960–2967. [Google Scholar] [CrossRef]
  4. Khashan, S.A.; Furlani, E.P. Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field. Sep. Purif. Technol. 2014125, 311–318. [Google Scholar] [CrossRef]
  5. Furlani, E.P. Magnetic biotransport: Analysis and applications. Materials 20103, 2412–2446. [Google Scholar] [CrossRef]
  6. Gómez-Pastora, J.; Bringas, E.; Ortiz, I. Design of novel adsorption processes for the removal of arsenic from polluted groundwater employing functionalized magnetic nanoparticles. Chem. Eng. Trans. 201647, 241–246. [Google Scholar]
  7. Gómez-Pastora, J.; Bringas, E.; Lázaro-Díez, M.; Ramos-Vivas, J.; Ortiz, I. The reverse of controlled release: Controlled sequestration of species and biotoxins into nanoparticles (NPs). In Drug Delivery Systems; Stroeve, P., Mahmoudi, M., Eds.; World Scientific: Hackensack, NJ, USA, 2017; pp. 207–244. ISBN 9789813201057. [Google Scholar]
  8. Ruffert, C. Magnetic bead-magic bullet. Micromachines 20167, 21. [Google Scholar] [CrossRef]
  9. Yáñez-Sedeño, P.; Campuzano, S.; Pingarrón, J.M. Magnetic particles coupled to disposable screen printed transducers for electrochemical biosensing. Sensors 201616, 1585. [Google Scholar] [CrossRef]
  10. Schrittwieser, S.; Pelaz, B.; Parak, W.J.; Lentijo-Mozo, S.; Soulantica, K.; Dieckhoff, J.; Ludwig, F.; Guenther, A.; Tschöpe, A.; Schotter, J. Homogeneous biosensing based on magnetic particle labels. Sensors 201616, 828. [Google Scholar] [CrossRef]
  11. He, J.; Huang, M.; Wang, D.; Zhang, Z.; Li, G. Magnetic separation techniques in sample preparation for biological analysis: A review. J. Pharm. Biomed. Anal. 2014101, 84–101. [Google Scholar] [CrossRef]
  12. Ha, Y.; Ko, S.; Kim, I.; Huang, Y.; Mohanty, K.; Huh, C.; Maynard, J.A. Recent advances incorporating superparamagnetic nanoparticles into immunoassays. ACS Appl. Nano Mater. 20181, 512–521. [Google Scholar] [CrossRef]
  13. Gómez-Pastora, J.; González-Fernández, C.; Fallanza, M.; Bringas, E.; Ortiz, I. Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies. Chem. Eng. J. 2018344, 487–497. [Google Scholar] [CrossRef]
  14. Gale, B.K.; Jafek, A.R.; Lambert, C.J.; Goenner, B.L.; Moghimifam, H.; Nze, U.C.; Kamarapu, S.K. A review of current methods in microfluidic device fabrication and future commercialization prospects. Inventions 20183, 60. [Google Scholar] [CrossRef]
  15. Nanobiotechnology; Concepts, Applications and Perspectives; Niemeyer, C.M.; Mirkin, C.A. (Eds.) Wiley-VCH: Weinheim, Germany, 2004; ISBN 3527305068. [Google Scholar]
  16. Khashan, S.A.; Dagher, S.; Alazzam, A.; Mathew, B.; Hilal-Alnaqbi, A. Microdevice for continuous flow magnetic separation for bioengineering applications. J. Micromech. Microeng. 201727, 055016. [Google Scholar] [CrossRef]
  17. Basauri, A.; Gomez-Pastora, J.; Fallanza, M.; Bringas, E.; Ortiz, I. Predictive model for the design of reactive micro-separations. Sep. Purif. Technol. 2019209, 900–907. [Google Scholar] [CrossRef]
  18. Abdollahi, P.; Karimi-Sabet, J.; Moosavian, M.A.; Amini, Y. Microfluidic solvent extraction of calcium: Modeling and optimization of the process variables. Sep. Purif. Technol. 2020231, 115875. [Google Scholar] [CrossRef]
  19. Khashan, S.A.; Alazzam, A.; Furlani, E. A novel design for a microfluidic magnetophoresis system: Computational study. In Proceedings of the 12th International Symposium on Fluid Control, Measurement and Visualization (FLUCOME2013), Nara, Japan, 18–23 November 2013. [Google Scholar]
  20. Pamme, N. Magnetism and microfluidics. Lab Chip 20066, 24–38. [Google Scholar] [CrossRef]
  21. Gómez-Pastora, J.; Amiri Roodan, V.; Karampelas, I.H.; Alorabi, A.Q.; Tarn, M.D.; Iles, A.; Bringas, E.; Paunov, V.N.; Pamme, N.; Furlani, E.P.; et al. Two-step numerical approach to predict ferrofluid droplet generation and manipulation inside multilaminar flow chambers. J. Phys. Chem. C 2019123, 10065–10080. [Google Scholar] [CrossRef]
  22. Gómez-Pastora, J.; Karampelas, I.H.; Bringas, E.; Furlani, E.P.; Ortiz, I. Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions. Sci. Rep. 20199, 7265. [Google Scholar] [CrossRef]
  23. Tarn, M.D.; Pamme, N. On-Chip Magnetic Particle-Based Immunoassays Using Multilaminar Flow for Clinical Diagnostics. In Microchip Diagnostics Methods and Protocols; Taly, V., Viovy, J.L., Descroix, S., Eds.; Humana Press: New York, NY, USA, 2017; pp. 69–83. [Google Scholar]
  24. Phurimsak, C.; Tarn, M.D.; Peyman, S.A.; Greenman, J.; Pamme, N. On-chip determination of c-reactive protein using magnetic particles in continuous flow. Anal. Chem. 201486, 10552–10559. [Google Scholar] [CrossRef]
  25. Wu, X.; Wu, H.; Hu, Y. Enhancement of separation efficiency on continuous magnetophoresis by utilizing L/T-shaped microchannels. Microfluid. Nanofluid. 201111, 11–24. [Google Scholar] [CrossRef]
  26. Vojtíšek, M.; Tarn, M.D.; Hirota, N.; Pamme, N. Microfluidic devices in superconducting magnets: On-chip free-flow diamagnetophoresis of polymer particles and bubbles. Microfluid. Nanofluid. 201213, 625–635. [Google Scholar] [CrossRef]
  27. Gómez-Pastora, J.; González-Fernández, C.; Real, E.; Iles, A.; Bringas, E.; Furlani, E.P.; Ortiz, I. Computational modeling and fluorescence microscopy characterization of a two-phase magnetophoretic microsystem for continuous-flow blood detoxification. Lab Chip 201818, 1593–1606. [Google Scholar] [CrossRef] [PubMed]
  28. Forbes, T.P.; Forry, S.P. Microfluidic magnetophoretic separations of immunomagnetically labeled rare mammalian cells. Lab Chip 201212, 1471–1479. [Google Scholar] [CrossRef]
  29. Nandy, K.; Chaudhuri, S.; Ganguly, R.; Puri, I.K. Analytical model for the magnetophoretic capture of magnetic microspheres in microfluidic devices. J. Magn. Magn. Mater. 2008320, 1398–1405. [Google Scholar] [CrossRef]
  30. Plouffe, B.D.; Lewis, L.H.; Murthy, S.K. Computational design optimization for microfluidic magnetophoresis. Biomicrofluidics 20115, 013413. [Google Scholar] [CrossRef] [PubMed]
  31. Hale, C.; Darabi, J. Magnetophoretic-based microfluidic device for DNA isolation. Biomicrofluidics 20148, 044118. [Google Scholar] [CrossRef] [PubMed]
  32. Becker, H.; Gärtner, C. Polymer microfabrication methods for microfluidic analytical applications. Electrophoresis 200021, 12–26. [Google Scholar] [CrossRef]
  33. Pekas, N.; Zhang, Q.; Nannini, M.; Juncker, D. Wet-etching of structures with straight facets and adjustable taper into glass substrates. Lab Chip 201010, 494–498. [Google Scholar] [CrossRef]
  34. Wang, T.; Chen, J.; Zhou, T.; Song, L. Fabricating microstructures on glass for microfluidic chips by glass molding process. Micromachines 20189, 269. [Google Scholar] [CrossRef]
  35. Castaño-Álvarez, M.; Pozo Ayuso, D.F.; García Granda, M.; Fernández-Abedul, M.T.; Rodríguez García, J.; Costa-García, A. Critical points in the fabrication of microfluidic devices on glass substrates. Sens. Actuators B Chem. 2008130, 436–448. [Google Scholar] [CrossRef]
  36. Prakash, S.; Kumar, S. Fabrication of microchannels: A review. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2015229, 1273–1288. [Google Scholar] [CrossRef]
  37. Leester-Schädel, M.; Lorenz, T.; Jürgens, F.; Ritcher, C. Fabrication of Microfluidic Devices. In Microsystems for Pharmatechnology: Manipulation of Fluids, Particles, Droplets, and Cells; Dietzel, A., Ed.; Springer: Basel, Switzerland, 2016; pp. 23–57. ISBN 9783319269207. [Google Scholar]
  38. Bartlett, N.W.; Wood, R.J. Comparative analysis of fabrication methods for achieving rounded microchannels in PDMS. J. Micromech. Microeng. 201626, 115013. [Google Scholar] [CrossRef]
  39. Ng, P.F.; Lee, K.I.; Yang, M.; Fei, B. Fabrication of 3D PDMS microchannels of adjustable cross-sections via versatile gel templates. Polymers 201911, 64. [Google Scholar] [CrossRef] [PubMed]
  40. Furlani, E.P.; Sahoo, Y.; Ng, K.C.; Wortman, J.C.; Monk, T.E. A model for predicting magnetic particle capture in a microfluidic bioseparator. Biomed. Microdevices 20079, 451–463. [Google Scholar] [CrossRef]
  41. Tarn, M.D.; Peyman, S.A.; Robert, D.; Iles, A.; Wilhelm, C.; Pamme, N. The importance of particle type selection and temperature control for on-chip free-flow magnetophoresis. J. Magn. Magn. Mater. 2009321, 4115–4122. [Google Scholar] [CrossRef]
  42. Furlani, E.P. Permanent Magnet and Electromechanical Devices; Materials, Analysis and Applications; Academic Press: Waltham, MA, USA, 2001. [Google Scholar]
  43. White, F.M. Viscous Fluid Flow; McGraw-Hill: New York, NY, USA, 1974. [Google Scholar]
  44. Mathew, B.; Alazzam, A.; El-Khasawneh, B.; Maalouf, M.; Destgeer, G.; Sung, H.J. Model for tracing the path of microparticles in continuous flow microfluidic devices for 2D focusing via standing acoustic waves. Sep. Purif. Technol. 2015153, 99–107. [Google Scholar] [CrossRef]
  45. Furlani, E.J.; Furlani, E.P. A model for predicting magnetic targeting of multifunctional particles in the microvasculature. J. Magn. Magn. Mater. 2007312, 187–193. [Google Scholar] [CrossRef]
  46. Furlani, E.P.; Ng, K.C. Analytical model of magnetic nanoparticle transport and capture in the microvasculature. Phys. Rev. E 200673, 061919. [Google Scholar] [CrossRef]
  47. Eibl, R.; Eibl, D.; Pörtner, R.; Catapano, G.; Czermak, P. Cell and Tissue Reaction Engineering; Springer: Berlin/Heidelberg, Germany, 2009. [Google Scholar]
  48. Pamme, N.; Eijkel, J.C.T.; Manz, A. On-chip free-flow magnetophoresis: Separation and detection of mixtures of magnetic particles in continuous flow. J. Magn. Magn. Mater. 2006307, 237–244. [Google Scholar] [CrossRef]
  49. Alorabi, A.Q.; Tarn, M.D.; Gómez-Pastora, J.; Bringas, E.; Ortiz, I.; Paunov, V.N.; Pamme, N. On-chip polyelectrolyte coating onto magnetic droplets-Towards continuous flow assembly of drug delivery capsules. Lab Chip 201717, 3785–3795. [Google Scholar] [CrossRef]
  50. Zhang, H.; Guo, H.; Chen, Z.; Zhang, G.; Li, Z. Application of PECVD SiC in glass micromachining. J. Micromech. Microeng. 200717, 775–780. [Google Scholar] [CrossRef]
  51. Mourzina, Y.; Steffen, A.; Offenhäusser, A. The evaporated metal masks for chemical glass etching for BioMEMS. Microsyst. Technol. 200511, 135–140. [Google Scholar] [CrossRef]
  52. Mata, A.; Fleischman, A.J.; Roy, S. Fabrication of multi-layer SU-8 microstructures. J. Micromech. Microeng. 200616, 276–284. [Google Scholar] [CrossRef]
  53. Su, N. 8 2000 Negative Tone Photoresist Formulations 2002–2025; MicroChem Corporation: Newton, MA, USA, 2002. [Google Scholar]
  54. Su, N. 8 2000 Negative Tone Photoresist Formulations 2035–2100; MicroChem Corporation: Newton, MA, USA, 2002. [Google Scholar]
  55. Fu, C.; Hung, C.; Huang, H. A novel and simple fabrication method of embedded SU-8 micro channels by direct UV lithography. J. Phys. Conf. Ser. 200634, 330–335. [Google Scholar] [CrossRef]
  56. Kazoe, Y.; Yamashiro, I.; Mawatari, K.; Kitamori, T. High-pressure acceleration of nanoliter droplets in the gas phase in a microchannel. Micromachines 20167, 142. [Google Scholar] [CrossRef]
  57. Sharp, K.V.; Adrian, R.J.; Santiago, J.G.; Molho, J.I. Liquid flows in microchannels. In MEMS: Introduction and Fundamentals; Gad-el-Hak, M., Ed.; CRC Press: Boca Raton, FL, USA, 2006; pp. 10-1–10-46. ISBN 9781420036572. [Google Scholar]
  58. Oh, K.W.; Lee, K.; Ahn, B.; Furlani, E.P. Design of pressure-driven microfluidic networks using electric circuit analogy. Lab Chip 201212, 515–545. [Google Scholar] [CrossRef]
  59. Bruus, H. Theoretical Microfluidics; Oxford University Press: New York, NY, USA, 2008; ISBN 9788578110796. [Google Scholar]
  60. Beebe, D.J.; Mensing, G.A.; Walker, G.M. Physics and applications of microfluidics in biology. Annu. Rev. Biomed. Eng. 20024, 261–286. [Google Scholar] [CrossRef] [PubMed]
  61. Yalikun, Y.; Tanaka, Y. Large-scale integration of all-glass valves on a microfluidic device. Micromachines 20167, 83. [Google Scholar] [CrossRef] [PubMed]
  62. Van Heeren, H.; Verhoeven, D.; Atkins, T.; Tzannis, A.; Becker, H.; Beusink, W.; Chen, P. Design Guideline for Microfluidic Device and Component Interfaces (Part 2), Version 3; Available online: http://www.makefluidics.com/en/design-guideline?id=7 (accessed on 9 March 2020).
  63. Scheuble, N.; Iles, A.; Wootton, R.C.R.; Windhab, E.J.; Fischer, P.; Elvira, K.S. Microfluidic technique for the simultaneous quantification of emulsion instabilities and lipid digestion kinetics. Anal. Chem. 201789, 9116–9123. [Google Scholar] [CrossRef] [PubMed]
  64. Lynch, E.C. Red blood cell damage by shear stress. Biophys. J. 197212, 257–273. [Google Scholar]
  65. Paul, R.; Apel, J.; Klaus, S.; Schügner, F.; Schwindke, P.; Reul, H. Shear stress related blood damage in laminar Couette flow. Artif. Organs 200327, 517–529. [Google Scholar] [CrossRef] [PubMed]
  66. Gómez-Pastora, J.; Karampelas, I.H.; Xue, X.; Bringas, E.; Furlani, E.P.; Ortiz, I. Magnetic bead separation from flowing blood in a two-phase continuous-flow magnetophoretic microdevice: Theoretical analysis through computational fluid dynamics simulation. J. Phys. Chem. C 2017121, 7466–7477. [Google Scholar] [CrossRef]
  67. Lim, J.; Yeap, S.P.; Leow, C.H.; Toh, P.Y.; Low, S.C. Magnetophoresis of iron oxide nanoparticles at low field gradient: The role of shape anisotropy. J. Colloid Interface Sci. 2014421, 170–177. [Google Scholar] [CrossRef] [PubMed]
  68. Culbertson, C.T.; Sibbitts, J.; Sellens, K.; Jia, S. Fabrication of Glass Microfluidic Devices. In Microfluidic Electrophoresis: Methods and Protocols; Dutta, D., Ed.; Humana Press: New York, NY, USA, 2019; pp. 1–12. ISBN 978-1-4939-8963-8. [Google Scholar]
Fig. 12. Comparison of simulation results with experimental data for a flow rate of water = Ql=15 ml/hr and a flow rate of air = Qg =3 ml/hr.

Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method

Abstract

This paper demonstrates that the Volume of Fluid (TruVOF) method in FLOW-3D (a general purpose CFD software) is an effective tool for studying droplet dynamics and mixing in microfluidic devices. The first example studied is a T-junction where flow patterns for both droplet generation and passive mixing are analyzed. The second example studied is a co-flowing device where the formation and breakup of bubbles is simulated. The effect of viscosity on bubble formation is also analyzed. For a T-junction the bubble size is corroborated with experimental data. Both the bubble size and frequency are studied and corroborated with experimental data for a co-flowing device. The third example studied is the electrowetting phenomenon observed in a small water droplet resting on a dielectric material. The steady-state contact angle is plotted against the voltage applied. The results are compared with both the Young-Lippmann curve and experimental results. 

이 논문은 FLOW-3D (범용 CFD 소프트웨어)의 유체 부피 (TruVOF) 방법이 미세 유체 장치에서 액적 역학 및 혼합을 연구하는데 효과적인 도구임을 보여줍니다.

연구된 첫 번째 예는 액적 생성 및 수동 혼합에 대한 흐름 패턴이 분석되는 T- 접합입니다. 연구된 두 번째 예는 기포의 형성 및 분해가 시뮬레이션 되는 동시 유동 장치입니다.

기포 형성에 대한 점도의 영향도 분석됩니다. T 접합의 경우 기포 크기는 실험 데이터로 확증됩니다. 기포 크기와 빈도 모두 공동 유동 장치에 대한 실험 데이터로 연구되고 확증됩니다.

연구된 세 번째 예는 유전 물질 위에 놓인 작은 물방울에서 관찰 된 전기 습윤 현상입니다. 정상 상태 접촉각은 적용된 전압에 대해 플롯됩니다. 결과는 Young-Lippmann 곡선 및 실험 결과와 비교됩니다.

Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method Fig 1
Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method Fig 1
Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method Fig 2
Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method Fig 2

References

Formation of bubbles in a simple co-flowing micro-channel

SaveAlertResearch FeedFormation of droplets and bubbles in a microfluidic T-junction-scaling and mechanism of break-up.

SaveAlertResearch FeedCreating, transporting, cutting, and merging liquid droplets by electrowetting-based actuation for digital microfluidic circuits,

SaveAlertResearch FeedFLOW DEVELOPMENT OF CO-FLOWING STREAMS IN RECTANGULAR MICRO-CHANNELS

SaveAlertResearch FeedA microfluidic system for controlling reaction networks in time.

SaveAlertResearch FeedElectrowetting: from basics to applications

SaveAlertResearch FeedVolume of fluid (VOF) method for the dynamics of free boundaries

The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes

The simulation of droplet impact on the super-hydrophobic surface with micro-pillar arrays fabricated by laser irradiation and silanization processes

레이저 조사 및 silanization 공정으로 제작된 micro-pillar arrays를 사용하여 초 소수성 표면에 대한 액적 영향 시뮬레이션

ZhenyanXiaa YangZhaoa ZhenYangabc ChengjuanYangab LinanLia ShibinWanga MengWangab
aSchool of Mechanical Engineering, Tianjin University, Tianjin, 300054, China
bKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin, 300072, Chinac
School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

Received 23 September 2020, Revised 17 November 2020, Accepted 26 November 2020, Available online 11 December 2020.

Abstract

Super-hydrophobicity is one of the significant natural phenomena, which has inspired researchers to fabricate artificial smart materials using advanced manufacturing techniques. In this study, a super-hydrophobic aluminum surface was prepared by nanosecond laser texturing and FAS modification in sequence. The surface wettability turned from original hydrophilicity to super-hydrophilicity immediately after laser treatment. Then it changed to super-hydrophobicity showing a WCA of 157.6 ± 1.2° with a SA of 1.7 ± 0.7° when the laser-induced rough surface being coated with a layer of FAS molecules. The transforming mechanism was further explored from physical and chemical aspects based on the analyses of surface morphology and surface chemistry. Besides, the motion process of droplet impacting super-hydrophobic surface was systematically analyzed via the optimization of simulation calculation grid and the simulation method of volume of fluid (VOF). Based on this simulation method, the morphological changes, the inside pressure distribution and velocity of the droplet were further investigated. And the motion mechanism of the droplet on super-hydrophobic surface was clearly revealed in this paper. The simulation results and the images captured by high-speed camera were highly consistent, which indicated that the computational fluid dynamics (CFD) is an effective method to predict the droplet motion on super- hydrophobic surfaces. This paper can provide an explicit guidance for the selection of suitable methods for functional surfaces with different requirements in the industry.

Korea Abstract

초 소수성은 연구원들이 첨단 제조 기술을 사용하여 인공 스마트 재료를 제작하도록 영감을 준 중요한 자연 현상 중 하나 입니다. 이 연구에서 초 소수성 알루미늄 표면은 나노초 레이저 텍스처링과 FAS 수정에 의해 순서대로 준비되었습니다.

레이저 처리 직후 표면 습윤성은 원래의 친수성에서 초 친수성으로 바뀌 었습니다. 그런 다음 레이저 유도 거친 표면을 FAS 분자 층으로 코팅했을 때 WCA가 157.6 ± 1.2 °이고 SA가 1.7 ± 0.7 ° 인 초 소수성으로 변경되었습니다.

변형 메커니즘은 표면 형태 및 표면 화학 분석을 기반으로 물리적 및 화학적 측면에서 추가로 탐구 되었습니다. 또한, 초 소수성 표면에 영향을 미치는 물방울의 운동 과정은 시뮬레이션 계산 그리드의 최적화와 유체 부피 (VOF) 시뮬레이션 방법을 통해 체계적으로 분석되었습니다.

이 시뮬레이션 방법을 바탕으로 형태학적 변화, 내부 압력 분포 및 액 적의 속도를 추가로 조사했습니다. 그리고 초 소수성 표면에 있는 물방울의 운동 메커니즘이 이 논문에서 분명하게 드러났습니다.

시뮬레이션 결과와 고속 카메라로 캡처한 이미지는 매우 일관적 이었습니다. 이는 전산 유체 역학 (CFD)이 초 소수성 표면에서 액적 움직임을 예측하는 효과적인 방법임을 나타냅니다.

이 백서는 업계의 다양한 요구 사항을 가진 기능 표면에 적합한 방법을 선택하기 위한 명시적인 지침을 제공 할 수 있습니다.

Keywords: Laser irradiation; Wettability; Droplet impact; Simulation; VOF

Introduction

서식지에 적응하기 위해 많은 자연 식물과 동물에서 특별한 습윤 표면이 진화되었습니다 [1-3]. 연잎은 먼지에 의한 오염으로부터 스스로를 보호하기 위해 우수한 자가 청소 특성을 나타냅니다 [4]. 사막 딱정벌레는 공기에서 물을 수확할 수 있는 기능적 표면 때문에 건조한 사막에서 생존 할 수 있습니다 [5].

자연 세계에서 영감을 받아 고체 기질의 표면 습윤성을 수정하는데 더 많은 관심이 집중되었습니다 [6-7]. 기능성 표면의 우수한 성능은 고유 한 표면 습윤성에 기인하며, 이는 고체 표면에서 액체의 확산 능력을 반영하는 중요한 특성 중 하나입니다 [8].

일반적으로 물 접촉각 (WCA) 값에 따라 90 °는 친수성과 소수성의 경계로 간주됩니다. WCA가 90 ° 이상인 소수성 표면, WCA가 90 ° 미만인 친수성 표면 [9 ]. 특히 고체 표면은 WCA가 10 ° 미만의 슬라이딩 각도 (SA)에서 150 °를 초과 할 때 특별한 초 소수성을 나타냅니다 [10-11].

<내용 중략> ……

 The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes
The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes

References

[1] H.W. Chen, P.F. Zhang, L.W. Zhang, Y. Jiang, H.L. Liu, D.Y. Zhang, Z.W. Han, L.
Jiang, Continuous directional water transport on the peristome surface of Nepenthes
alata, Nature 532 (2016) 85-89.
[2] Y. Liu, K.T. Zhang, W.G. Yao, J.A. Liu, Z.W. Han, L.Q. Ren, Bioinspired
structured superhydrophobic and superoleophilic stainless steel mesh for efficient oilwater separation, Colloids Surf., A 500 (2016) 54-63.
[3] Y.X. Liu, W.L. Liu, G.L. Wang, J.C. Hou, H. Kong, W.L. Wang, A facile one-step
approach to superhydrophilic silica film with hierarchical structure using
fluoroalkylsilane, Colloids Surf., A 539 (2018) 109-115.
[4] F.P. Wang, S. Li, L. Wang, Fabrication of artificial super-hydrophobic lotus-leaflike bamboo surfaces through soft lithography, Colloids Surf., A 513 (2017) 389-395.
[5] W. Huang, X.Y. Tang, Z. Qiu, W.X. Zhu, Y.G. Wang, Y.L. Zhu, Z.F. Xiao, H.G.
Wang, D.X. Liang, Jian, L. Y.J Xie, Cellulose-based Superhydrophobic Surface
Decorated with Functional Groups Showing Distinct Wetting Abilities to Manipulate
Water Harvesting, ACS Appl. Mater. Interfaces DOI: 10.1021/acsami.0c12504.
[6] M.Y. Zhang, L.J. Ma, Q. Wang, P. Hao, X. Zheng, Wettability behavior of
nanodroplets on copper surfaces with hierarchical nanostructures, Colloids Surf., A
604 (2020) 125291.
[7] A.F. Pan, W.J. Wang, X.S. Mei, K.D. Wang, X.B. Yang, Rutile TiO2 flocculent
ripples with high antireflectivity and superhydrophobicity on the surface of titanium
under 10 ns laser irradiation without focusing, Langmuir 33 (2017) 9530-9538.
[8] M. Li, X.H. Liu, N. Liu, Z.H. Guo, P.K. Singh, S.Y. Fu, Effect of surface
wettability on the antibacterial activity of nanocellulose-based material with
quaternary ammonium groups, Colloids Surf., A 554 (2018) 122-128.
[9] T.C. Chen, H.T. Liu, H.F. Yang, W. Yan, W. Zhu, H. Liu, Biomimetic fabrication
of robust self-assembly superhydrophobic surfaces with corrosion resistance
properties on stainless steel substrate, RSC Adv. 6 (2016) 43937-43949.
[10] P. Zhang, F.Y. Lv, A review of the recent advances in superhydrophobic surfaces
and the emerging energy-related applications, Energy 82 (2015) 1068-1087.
[11] Z. Yang, X.P. Liu, Y.L. Tian, Novel metal-organic super-hydrophobic surface
fabricated by nanosecond laser irradiation in solution, Colloids Surf., A 587 (2020)
124343.
[12] J.Y. Peng, X.J. Zhao, W.F. Wang, X. Gong, Durable Self-Cleaning Surfaces with
Superhydrophobic and Highly Oleophobic Properties, Langmuir, 35 (2019) 8404-
8412.
[13] Z. Yang, X.P. Liu, Y.L. Tian, A contrastive investigation on anticorrosive
performance of laser-induced super-hydrophobic and oil-infused slippery coatings,
Prog. Org. Coat. 138 (2020) 105313.
[14] J.L. Yong, F. Chen, Q. Yang, J.L. Huo, X. Hou, Superoleophobic Surfaces,
Chem. Soc. Rev. 46 (2017) 4168-4217.
[15] D.W. Li, H.Y. Wang, Y. Liu, D.S. Wei, Z.X. Zhao, Large-Scale Fabrication of
Durable and Robust Super-Hydrophobic Spray Coatings with Excellent Repairable
and Anti-Corrosion Performance, Chem. Eng. J. 367 (2019) 169-179.
[16] R.J. Liao, Z.P. Zuo, C. Guo, Y. Yuan, A.Y. Zhuang, Fabrication of
superhydrophobic surface on aluminum by continuous chemical etching and its antiicing property, Appl. Surf. Sci. 317 (2014) 701-709.
[17] Z. Yang. X.P. Liu, Y.L. Tian, Hybrid laser ablation and chemical modification for
fast fabrication of bio-inspired super-hydrophobic surface with excellent selfcleaning, stability and corrosion resistance, J Bionic Eng 16 (2019) 13-26.
[18] Z. Yang, Y.L. Tian, Y.C. Zhao, C.J. Yang, Study on the fabrication of superhydrophobic surface on Inconel alloy via nanosecond laser ablation, Materials 12
(2019) 278.
[19] Y. Wang, X. Gong, Superhydrophobic Coatings with Periodic Ring Structured
Patterns for Self-Cleaning and Oil-Water Separation, Adv. Mater. Interfaces 4 (2017)
1700190.
[20] N. Chik, W.S.W.M. Zain, A.J. Mohamad, M.Z. Sidek, W.H.W. Ibrahim, A. Reif,
J.H. Rakebrandt, W. Pfleging, X. Liu, Bacterial adhesion on the titanium and
stainless-steel surfaces undergone two different treatment methods: Polishing and ultrafast laser treatment, IOP Conf. Ser.: Mater. Sci. Eng.358 (2018) 012034.
[21] N.K.K. Win, P. Jitareerat, S. Kanlayanarat, S. Sangchote, Effects of cinnamon
extract, chitosan coating, hot water treatment and their combinations on crown rot
disease and quality of banana fruit, Postharvest Biol. Technol. 45 (2007) 333–340.
[22] A. Yarin, Drop impact dynamics: splashing, spreading, receding, bouncing, Annu.
Rev. Fluid Mech. 38 (2006) 159–192.
[23] N. Wang, L.L. Tang, Y.F. Cai, W. Tong, D.S. Xiong, Scalable superhydrophobic
coating with controllable wettability and investigations of its drag reduction, Colloids
Surf. A 555 (2018) 290–295.
[24] R. Fürstner, W. Barthlott, C. Neinhuis, P. Walzel, Wetting and self-cleaning
properties of artificial superhydrophobic surfaces, Langmuir 21 (2005) 956–61.
[25] U. Trdan, M. Hočevar, P. Gregorčič, Transition from superhydrophilic to
superhydrophobic state of laser textured stainless steel surface and its effect on
corrosion resistance, Corros. Sci. 123 (2017) 21–44.
[26] A.L. Biance, C. Clanet, D. Quere, First steps in the spreading of a liquid droplet,
Phys. Rev. E 69 (2004) 016301.
[27] S. Kulju, L. Riegger, P. Koltay et al, Fluid flow simulations meet high-speed
video: computer vision comparison of droplet dynamics, J. Colloid Interface Sci. 522
(2018) 48.
[28] C.J. Yong, B. Bhushan, Dynamic effects of bouncing water droplets on
superhydrophobic surfaces, Langmuir 24.12 (2008) 6262–6269.
[29] G. Karapetsas, N.T. Chamakos, A.G. Papathanasiou, Efficient modelling of
droplet dynamics on complex surfaces, J. Phys.: Condens. Matter 28.8 (2016) 085101.
[30] D. Khojasteh, N.M. Kazerooni, S. Salarian et al, Droplet impact on
superhydrophobic surfaces: a review of recent developments, J. Ind. Eng. Chem. 42
(2016) 1–14.
[31] S.H. Kim, Y. Jiang, H. Kim, Droplet impact and LFP on wettability and
nanostructured surface, Exp. Therm. Fluid Sci. 99 (2018) 85–93.
[32] M. Rudman, Volume‐Tracking Methods for Interfacial Flow Calculations, Int.
J. Numer. Methods Fluids 24.7 (1997) 671-691.

Dam-Break Flows: Comparison between Flow-3D, MIKE 3 FM, and Analytical Solutions with Experimental Data

Dam-Break Flows: Comparison between Flow-3D, MIKE 3 FM, and Analytical Solutions with Experimental Data

by Hui Hu,Jianfeng Zhang andTao Li *
State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
*Author to whom correspondence should be addressed.
Appl. Sci.20188(12), 2456; https://doi.org/10.3390/app8122456Received: 14 October 2018 /
Revised: 20 November 2018 / Accepted: 29 November 2018 / Published: 2 December 2018

Abstract

The objective of this study was to evaluate the applicability of a flow model with different numbers of spatial dimensions in a hydraulic features solution, with parameters such a free surface profile, water depth variations, and averaged velocity evolution in a dam-break under dry and wet bed conditions with different tailwater depths. Two similar three-dimensional (3D) hydrodynamic models (Flow-3D and MIKE 3 FM) were studied in a dam-break simulation by performing a comparison with published experimental data and the one-dimensional (1D) analytical solution. The results indicate that the Flow-3D model better captures the free surface profile of wavefronts for dry and wet beds than other methods. The MIKE 3 FM model also replicated the free surface profiles well, but it underestimated them during the initial stage under wet-bed conditions. However, it provided a better approach to the measurements over time. Measured and simulated water depth variations and velocity variations demonstrate that both of the 3D models predict the dam-break flow with a reasonable estimation and a root mean square error (RMSE) lower than 0.04, while the MIKE 3 FM had a small memory footprint and the computational time of this model was 24 times faster than that of the Flow-3D. Therefore, the MIKE 3 FM model is recommended for computations involving real-life dam-break problems in large domains, leaving the Flow-3D model for fine calculations in which knowledge of the 3D flow structure is required. The 1D analytical solution was only effective for the dam-break wave propagations along the initially dry bed, and its applicability was fairly limited. 

Keywords: dam breakFlow-3DMIKE 3 FM1D Ritter’s analytical solution

이 연구의 목적은 자유 표면 프로파일, 수심 변화 및 건식 및 댐 파괴에서 평균 속도 변화와 같은 매개 변수를 사용하여 유압 기능 솔루션에서 서로 다른 수의 공간 치수를 가진 유동 모델의 적용 가능성을 평가하는 것이었습니다.

테일 워터 깊이가 다른 습식베드 조건. 2 개의 유사한 3 차원 (3D) 유체 역학 모델 (Flow-3D 및 MIKE 3 FM)이 게시된 실험 데이터와 1 차원 (1D) 분석 솔루션과의 비교를 수행하여 댐 브레이크 시뮬레이션에서 연구되었습니다.

결과는 FLOW-3D 모델이 다른 방법보다 건식 및 습식 베드에 대한 파면의 자유 표면 프로파일을 더 잘 포착함을 나타냅니다. MIKE 3 FM 모델도 자유 표면 프로파일을 잘 복제했지만, 습식 조건에서 초기 단계에서 과소 평가했습니다. 그러나 시간이 지남에 따라 측정에 더 나은 접근 방식을 제공했습니다.

측정 및 시뮬레이션 된 수심 변화와 속도 변화는 두 3D 모델 모두 합리적인 추정치와 0.04보다 낮은 RMSE (root mean square error)로 댐 브레이크 흐름을 예측하는 반면 MIKE 3 FM은 메모리 공간이 적고 이 모델의 계산 시간은 Flow-3D보다 24 배 더 빠릅니다.

따라서 MIKE 3 FM 모델은 대규모 도메인의 실제 댐 브레이크 문제와 관련된 계산에 권장되며 3D 흐름 구조에 대한 지식이 필요한 미세 계산을 위해 Flow-3D 모델을 남겨 둡니다. 1D 분석 솔루션은 초기 건조 층을 따라 전파되는 댐 파괴에만 효과적이었으며 그 적용 가능성은 상당히 제한적이었습니다.

1. Introduction

저수지에 저장된 물의 통제되지 않은 방류[1]로 인해 댐 붕괴와 그로 인해 하류에서 발생할 수 있는 잠재적 홍수로 인해 큰 자연 위험이 발생한다. 이러한 영향을 최대한 완화하기 위해서는 홍수[2]로 인한 위험을 관리하고 감소시키기 위해 홍수의 시간적 및 공간적 진화를 모두 포착하여 댐 붕괴 파동의 움직임을 예측하고 댐 붕괴 파동의 전파 과정 효과를 다운스트림[3]으로 예측하는 것이 중요하다. 

그러나 이러한 수량을 예측하는 것은 어려운 일이며, 댐 붕괴 홍수의 움직임을 정확하게 시뮬레이션하고 유동장에 대한 유용한 정보를 제공하기 위한 적절한 모델을 선택하는 것은 그러므로 필수적인 단계[4]이다.

적절한 수학적 및 수치적 모델의 선택은 댐 붕괴 홍수 분석에서 매우 중요한 것으로 나타났다.분석적 해결책에서 행해진 댐 붕괴 흐름에 대한 연구는 100여 년 전에 시작되었다. 

리터[5]는 먼저 건조한 침대 위에 1D de 생베넌트 방정식의 초기 분석 솔루션을 도출했고, 드레슬러[6,7]와 휘담[8]은 마찰저항의 영향을 받은 파동학을 연구했으며, 스토커[9]는 젖은 침대를 위한 1D 댐 붕괴 문제에 리터의 솔루션을 확장했다. 

마샬과 멩데즈[10]는 고두노프가 가스 역학의 오일러 방정식을 위해 개발한 방법론[11]을 적용하여 젖은 침대 조건에서 리만 문제를 해결하기 위한 일반적인 절차를 고안했다. Toro [12]는 습식 및 건식 침대 조건을 모두 해결하기 위해 완전한 1D 정밀 리만 용해제를 실시했다. 

Chanson [13]은 특성 방법을 사용하여 갑작스러운 댐 붕괴로 인한 홍수에 대한 간단한 분석 솔루션을 연구했다. 그러나 이러한 분석 솔루션은 특히 댐 붕괴 초기 단계에서 젖은 침대의 정확한 결과를 도출하지 못했다[14,15].과거 연구의 발전은 이른바 댐 붕괴 홍수 문제 해결을 위한 여러 수치 모델[16]을 제공했으며, 헥-라스, DAMBRK, MIK 11 등과 같은 1차원 모델을 댐 붕괴 홍수를 모델링하는 데 사용하였다.

[17 2차원(2D) 깊이 평균 방정식도 댐 붕괴 흐름 문제를 시뮬레이션하는 데 널리 사용되어 왔으며[18,19,20,21,22] 그 결과 얕은 물 방정식(SWE)이 유체 흐름을 나타내는 데 적합하다는 것을 알 수 있다. 그러나, 경우에 따라 2D 수치해결기가 제공하는 해결책이 특히 근거리 분야에서 실험과 일관되지 않을 수 있다[23,24]. 더욱이, 1차원 및 2차원 모델은 3차원 현상에 대한 일부 세부사항을 포착하는 데 한계가 있다.

[25]. RANS(Reynolds-averageed Navier-Stok크스 방정식)에 기초한 여러 3차원(3D) 모델이 얕은 물 모델의 일부 단점을 극복하기 위해 적용되었으며, 댐 붕괴 초기 단계에서의 복잡한 흐름의 실제 동작을 이해하기 위해 사용되었다 [26,27,28]장애물이나 바닥 실에 대한 파장의 충격으로 인한 튜디 댐 붕괴 흐름 [19,29] 및 근거리 영역의 난류 댐 붕괴 흐름 거동 [4] 최근 상용화된 수치 모델 중 잘 알려진 유체 방식(VOF) 기반 CFD 모델링 소프트웨어 FLOW-3D는 컴퓨터 기술의 진보에 따른 계산력 증가로 인해 불안정한 자유 표면 흐름을 분석하는 데 널리 사용되고 있다. 

이 소프트웨어는 유한 차이 근사치를 사용하여 RANS 방정식에 대한 수치 해결책을 계산하며, 자유 표면을 추적하기 위해 VOF를 사용한다 [30,31]; 댐 붕괴 흐름을 모델링하는 데 성공적으로 사용되었다 [32,33].그러나, 2D 얕은 물 모델을 사용하여 포착할 수 없는 공간과 시간에 걸친 댐 붕괴 흐름의 특정한 유압적 특성이 있다. 

실생활 현장 척도 시뮬레이션을 위한 완전한 3D Navier-Stokes 방정식의 적용은 더 높은 계산 비용[34]을 가지고 있으며, 원하는 결과는 얕은 물 모델[35]보다 더 정확한 결과를 산출하지 못할 수 있다. 따라서, 본 논문은 3D 모델의 기능과 그 계산 효율을 평가하기 위해 댐 붕괴 흐름 시뮬레이션을 위한 단순화된 3D 모델-MIKE 3 FM을 시도한다. 

MIK 3 모델은 자연 용수 분지의 여러 유체 역학 시뮬레이션 조사에 적용되었다. 보치 외 연구진이 사용해 왔다. [36], 니콜라오스 및 게오르기오스 [37], 고얄과 라토드[38] 등 현장 연구에서 유체역학 시뮬레이션을 위한 것이다. 이러한 저자들의 상당한 연구에도 불구하고, MIK 3 FM을 이용한 댐 붕괴의 모델링에 관한 연구는 거의 없었다. 

또한 댐 붕괴 홍수 전파 문제를 해결하기 위한 3D 얕은 물과 완전한 3D RANS 모델의 성능을 비교한 연구도 아직 보고되지 않았다. 이 공백을 메우기 위해 현재 연구의 주요 목표는 댐 붕괴 흐름을 시뮬레이션하기 위한 단순화된 3D SWE, 상세 RANS 모델 및 분석 솔루션을 평가하여 댐 붕괴 문제에 대한 정확도와 적용 가능성을 평가하는 것이다.실제 댐 붕괴 문제를 해결하기 위해 유체역학 시뮬레이션을 시도하기 전에 수치 모델을 검증할 필요가 있다. 

일련의 실험 벤치마크를 사용하여 수치 모델을 확인하는 것은 용인된 관행이다. 현장 데이터 확보가 어려워 최근 몇 년 동안 제한된 측정 데이터를 취득했다. 

본 논문은 Ozmen-Cagatay와 Kocaman[30] 및 Khankandi 외 연구진이 제안한 두 가지 테스트 사례에 의해 제안된 검증에서 인용한 것이다. [39] 오즈멘-카가테이와 코카만[30]이 수행한 첫 번째 실험에서, 다른 미숫물 수위에 걸쳐 초기 단계 동안 댐 붕괴 홍수파가 발생했으며, 자유 지표면 프로파일의 측정치를 제공했다. Ozmen-Cagatay와 Kocaman[30]은 초기 단계에서 Flow-3D 소프트웨어가 포함된 2D SWE와 3D RANS의 숫자 솔루션에 의해 계산된 자유 표면 프로필만 비교했다. 

Khankandi 등이 고안한 두 번째 실험 동안. [39], 이 실험의 측정은 홍수 전파를 시뮬레이션하고 측정된 데이터를 제공하는 것을 목적으로 하는 수치 모델을 검증하기 위해 사용되었으며, 말기 동안의 자유 표면 프로필, 수위의 시간 진화 및 속도 변화를 포함한다. Khankandi 등의 연구. [39] 주로 실험 조사에 초점을 맞추었으며, 초기 단계에서는 리터의 솔루션과의 수위만을 언급하고 있다.

경계 조건(상류 및 하류 모두 무한 채널 길이를 갖는 1D 분석 솔루션에서는 실험 결과를 리터와 비교하는 것이 타당하지 않기 때문이다(건조 be)d) 또는 스토커(웨트 베드) 솔루션은 벽의 반사가 깊이 프로파일에 영향을 미쳤을 때, 그리고 참조 [39]의 실험에 대한 수치 시뮬레이션과의 추가 비교가 불량할 때. 이 논문은 이러한 문제를 직접 겨냥하여 전체 댐 붕괴 과정에서의 자유 표면 프로필, 수심 변화 및 속도 변화에 대한 완전한 비교 연구를 제시한다. 

여기서 댐 붕괴파의 수치 시뮬레이션은 초기에 건조하고 습한 직사각형 채널을 가진 유한 저장소의 순간 댐 붕괴에 대해 두 개의 3D 모델을 사용하여 개발된다.본 논문은 다음과 같이 정리되어 있다. 두 모델에 대한 통치 방정식은 숫자 체계를 설명하기 전에 먼저 도입된다. 

일반적인 단순화된 시험 사례는 3D 수치 모델과 1D 분석 솔루션을 사용하여 시뮬레이션했다. 모델 결과와 이들이 실험실 실험과 비교하는 방법이 논의되고, 서로 다른 수심비에서 시간에 따른 유압 요소의 변동에 대한 시뮬레이션 결과가 결론을 도출하기 전에 제시된다.

2. Materials and Methods

2.1. Data

첫째, 수평 건조 및 습식 침상에 대한 초기 댐 붕괴 단계 동안의 자유 표면 프로필 측정은 Ozmen-Cagatay와 Kocaman에 의해 수행되었다[30]. 이 시험 동안, 매끄럽고 직사각형의 수평 채널은 그림 1에서 표시한 대로 너비 0.30m, 높이 0.30m, 길이 8.9m이었다. 

채널은 채널 입구에서 4.65m 떨어진 수직 플레이트(담) 즉, 저장소의 길이 L0=4.65mL0에 의해 분리되었다., 및 다운스트림 채널 L1=4.25 mL1. m저수지는 댐의 좌측에 위치하고 처음에는 침수된 것으로 간주되었다; 저수지의 초기 상류 수심 h0 0.25m로 일정했다.

오른쪽의 초기 수심 h1h1 건식침대의 경우 0m, 습식침대의 경우 0.025m, 0.1m이므로 수심비 α=h1/h0α으로 세 가지 상황이 있었다. 0, 0.1, 0.4의 습식침대 조건은 플룸 끝에 낮은 보를 사용함으로써 만들어졌다. 물 표면 프로필은 3개의 고속 디지털 카메라(50프레임/s)를 사용하여 초기에 관찰되었으며, 계측 측정의 정확도는 참고문헌 [30]에서 입증되었다. In the following section, the corresponding numerical results refer to positions x = −1 m (P1), −0.5 m (P2), −0.2 m (P3), +0.2 m (P4), +0.5 m (P5), +1 m (P6), +2 m (P7), and +2.85 m (P8), where the origin of the coordinate system x = 0 is at the dam site. 3수심비 ααα 0, 0.1, 0.4의 경우 x,yx의 경우 좌표는 h0.으로 정규화된다.

<중략> ……

Figure 1. Schematic view of the experimental conditions by Ozmen-Cagatay and Kocaman [30]: (a) α = 0; (b) α = 0.1; and (c) α = 0.4.
Figure 1. Schematic view of the experimental conditions by Ozmen-Cagatay and Kocaman [30]: (a) α = 0; (b) α = 0.1; and (c) α = 0.4.

Figure 2. Schematic view of the experimental conditions by Khankandi et al. [39]: (a) α = 0 and (b) α = 0.2.
Figure 2. Schematic view of the experimental conditions by Khankandi et al. [39]: (a) α = 0 and (b) α = 0.2.
Figure 3. Typical profiles of the dam-break flow regimes for Stoker’s analytical solution [9]: Wet-bed downstream
Figure 3. Typical profiles of the dam-break flow regimes for Stoker’s analytical solution [9]: Wet-bed downstream
Figure 4. Sensitivity analysis of the numerical simulation using Flow-3D for the different mesh sizes of the experiments in Reference [30].
Figure 4. Sensitivity analysis of the numerical simulation using Flow-3D for the different mesh sizes of the experiments in Reference [30].
Figure 5. Sensitivity analysis of the numerical simulation using MIKE 3 FM for the different mesh sizes of the experiments in Reference [30].
Figure 5. Sensitivity analysis of the numerical simulation using MIKE 3 FM for the different mesh sizes of the experiments in Reference [30].
Figure 6. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for dry-bed (α=0). The experimental data are from Reference [30].
Figure 6. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for dry-bed (α=0). The experimental data are from Reference [30].
Figure 7. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for a wet-bed (α = 0.1). The experimental data are from Reference [30].
Figure 7. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for a wet-bed (α = 0.1). The experimental data are from Reference [30].
Figure 8. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for the wet-bed (α = 0.4). The experimental data are from Reference [30].
Figure 8. Comparison between observed and simulated free surface profiles at dimensionless times T = t(g/h0)1/2 and for the wet-bed (α = 0.4). The experimental data are from Reference [30].
Figure 9. Experimental and numerical comparison of free surface profiles h/h0(x/h0) during late stages at various dimensionless times T after the failure in the dry-bed by Khankandi et al. [39].
Figure 9. Experimental and numerical comparison of free surface profiles h/h0(x/h0) during late stages at various dimensionless times T after the failure in the dry-bed by Khankandi et al. [39].

Table 2. RMSE values for the free surface profiles observed by Khankandi et al. [39].

Table 2. RMSE values for the free surface profiles observed by Khankandi et al. [39].
Table 2. RMSE values for the free surface profiles observed by Khankandi et al. [39].
Figure 10. Measured and computed water level hydrograph at various positions for dry-bed by Khankandi et al. [39]: (a) G1 (−0.5 m); (b) G2 (−0.1 m); (c) G3 (0.1 m); (d) G4 (0.8 m); (e) G6 (1.2 m); (f) G8 (5.5 m).
Figure 10. Measured and computed water level hydrograph at various positions for dry-bed by Khankandi et al. [39]: (a) G1 (−0.5 m); (b) G2 (−0.1 m); (c) G3 (0.1 m); (d) G4 (0.8 m); (e) G6 (1.2 m); (f) G8 (5.5 m).
Figure 11. Measured and computed water level hydrographs at various positions for the wet-bed by Khankandi et al. [39]: (a) G1 (−0.5 m); (b) G2 (−0.1 m); (c) G4 (0.8 m); and (d) G5 (1.0 m).
Figure 11. Measured and computed water level hydrographs at various positions for the wet-bed by Khankandi et al. [39]: (a) G1 (−0.5 m); (b) G2 (−0.1 m); (c) G4 (0.8 m); and (d) G5 (1.0 m).

Table 3. RMSE values for the water depth variations observed by Khankandi et al. [39] at the late stage.

Table 3. RMSE values for the water depth variations observed by Khankandi et al. [39] at the late stage.
Table 3. RMSE values for the water depth variations observed by Khankandi et al. [39] at the late stage.
Figure 13. Comparison of simulated velocity profiles at various locations upstream and downstream of the dam at t = 0.8 s, 2 s, and 5 s for water depth ratios α = 0.1 by Ozmen-Cagatay and Kocaman [30]: (a) P1(−1 m); (b) P3 (+0.2 m); (c) P5 (+1 m); and (d) P6 (+2 m).
Figure 13. Comparison of simulated velocity profiles at various locations upstream and downstream of the dam at t = 0.8 s, 2 s, and 5 s for water depth ratios α = 0.1 by Ozmen-Cagatay and Kocaman [30]: (a) P1(−1 m); (b) P3 (+0.2 m); (c) P5 (+1 m); and (d) P6 (+2 m).
Table 5. The required computational time for the two models to address dam break flows in all cases
Table 5. The required computational time for the two models to address dam break flows in all cases

References

  1. Gallegos, H.A.; Schubert, J.E.; Sanders, B.F. Two-dimensional high-resolution modeling of urban dam-break flooding: A case study of Baldwin Hills, California. Adv. Water Resour. 200932, 1323–1335. [Google Scholar] [CrossRef]
  2. Kim, K.S. A Mesh-Free Particle Method for Simulation of Mobile-Bed Behavior Induced by Dam Break. Appl. Sci. 20188, 1070. [Google Scholar] [CrossRef]
  3. Robb, D.M.; Vasquez, J.A. Numerical simulation of dam-break flows using depth-averaged hydrodynamic and three-dimensional CFD models. In Proceedings of the Canadian Society for Civil Engineering Hydrotechnical Conference, Québec, QC, Canada, 21–24 July 2015. [Google Scholar]
  4. LaRocque, L.A.; Imran, J.; Chaudhry, M.H. 3D numerical simulation of partial breach dam-break flow using the LES and k-ε. J. Hydraul. Res. 201351, 145–157. [Google Scholar] [CrossRef]
  5. Ritter, A. Die Fortpflanzung der Wasserwellen (The propagation of water waves). Z. Ver. Dtsch. Ing. 189236, 947–954. [Google Scholar]
  6. Dressler, R.F. Hydraulic resistance effect upon the dam-break functions. J. Res. Nat. Bur. Stand. 195249, 217–225. [Google Scholar] [CrossRef]
  7. Dressler, R.F. Comparison of theories and experiments for the hydraulic dam-break wave. Int. Assoc. Sci. Hydrol. 195438, 319–328. [Google Scholar]
  8. Whitham, G.B. The effects of hydraulic resistance in the dam-break problem. Proc. R. Soc. Lond. 1955227A, 399–407. [Google Scholar] [CrossRef]
  9. Stoker, J.J. Water Waves: The Mathematical Theory with Applications; Wiley and Sons: New York, NY, USA, 1957; ISBN 0-471-57034-6. [Google Scholar]
  10. Marshall, G.; Méndez, R. Computational Aspects of the Random Choice Method for Shallow Water Equations. J. Comput. Phys. 198139, 1–21. [Google Scholar] [CrossRef]
  11. Godunov, S.K. Finite Difference Methods for the Computation of Discontinuous Solutions of the Equations of Fluid Dynamics. Math. Sb. 195947, 271–306. [Google Scholar]
  12. Toro, E.F. Shock-Capturing Methods for Free-Surface Shallow Flows; Wiley and Sons Ltd.: New York, NY, USA, 2001. [Google Scholar]
  13. Chanson, H. Application of the method of characteristics to the dam break wave problem. J. Hydraul. Res. 200947, 41–49. [Google Scholar] [CrossRef][Green Version]
  14. Cagatay, H.; Kocaman, S. Experimental Study of Tail Water Level Effects on Dam-Break Flood Wave Propagation; 2008 Kubaba Congress Department and Travel Services: Ankara, Turkey, 2008; pp. 635–644. [Google Scholar]
  15. Stansby, P.K.; Chegini, A.; Barnes, T.C.D. The initial stages of dam-break flow. J. Fluid Mech. 1998374, 407–424. [Google Scholar] [CrossRef]
  16. Soares-Frazao, S.; Zech, Y. Dam Break in Channels with 90° Bend. J. Hydraul. Eng. 2002128, 956–968. [Google Scholar] [CrossRef]
  17. Zolghadr, M.; Hashemi, M.R.; Zomorodian, S.M.A. Assessment of MIKE21 model in dam and dike-break simulation. IJST-Trans. Mech. Eng. 201135, 247–262. [Google Scholar]
  18. Bukreev, V.I.; Gusev, A.V. Initial stage of the generation of dam-break waves. Dokl. Phys. 200550, 200–203. [Google Scholar] [CrossRef]
  19. Soares-Frazao, S.; Noel, B.; Zech, Y. Experiments of dam-break flow in the presence of obstacles. Proc. River Flow 20042, 911–918. [Google Scholar]
  20. Aureli, F.; Maranzoni, A.; Mignosa, P.; Ziveri, C. Dambreak flows: Acquisition of experimental data through an imaging technique and 2D numerical modelling. J. Hydraul. Eng. 2008134, 1089–1101. [Google Scholar] [CrossRef]
  21. Rehman, K.; Cho, Y.S. Bed Evolution under Rapidly Varying Flows by a New Method for Wave Speed Estimation. Water 20168, 212. [Google Scholar] [CrossRef]
  22. Wu, G.F.; Yang, Z.H.; Zhang, K.F.; Dong, P.; Lin, Y.T. A Non-Equilibrium Sediment Transport Model for Dam Break Flow over Moveable Bed Based on Non-Uniform Rectangular Mesh. Water 201810, 616. [Google Scholar] [CrossRef]
  23. Ferrari, A.; Fraccarollo, L.; Dumbser, M.; Toro, E.F.; Armanini, A. Three-dimensional flow evolution after a dam break. J. Fluid Mech. 2010663, 456–477. [Google Scholar] [CrossRef]
  24. Liang, D. Evaluating shallow water assumptions in dam-break flows. Proc. Inst. Civ. Eng. Water Manag. 2010163, 227–237. [Google Scholar] [CrossRef]
  25. Biscarini, C.; Francesco, S.D.; Manciola, P. CFD modelling approach for dam break flow studies. Hydrol. Earth Syst. Sci. 201014, 705–718. [Google Scholar] [CrossRef][Green Version]
  26. Oertel, M.; Bung, D.B. Initial stage of two-dimensional dam-break waves: Laboratory versus VOF. J. Hydraul. Res. 201250, 89–97. [Google Scholar] [CrossRef]
  27. Quecedo, M.; Pastor, M.; Herreros, M.I.; Merodo, J.A.F.; Zhang, Q. Comparison of two mathematical models for solving the dam break problem using the FEM method. Comput. Method Appl. Mech. Eng. 2005194, 3984–4005. [Google Scholar] [CrossRef]
  28. Shigematsu, T.; Liu, P.L.F.; Oda, K. Numerical modeling of the initial stages of dam-break waves. J. Hydraul. Res. 200442, 183–195. [Google Scholar] [CrossRef]
  29. Soares-Frazao, S. Experiments of dam-break wave over a triangular bottom sill. J. Hydraul. Res. 200745, 19–26. [Google Scholar] [CrossRef]
  30. Ozmen-Cagatay, H.; Kocaman, S. Dam-break flows during initial stage using SWE and RANS approaches. J. Hydraul. Res. 201048, 603–611. [Google Scholar] [CrossRef]
  31. Vasquez, J.; Roncal, J. Testing River2D and FLOW-3D for Sudden Dam-Break Flow Simulations. In Proceedings of the Canadian Dam Association’s 2009 Annual Conference: Protecting People, Property and the Environment, Whistler, BC, Canada, 3–8 October 2009. [Google Scholar]
  32. Ozmen-Cagatay, H.; Kocaman, S. Dam-break flow in the presence of obstacle: Experiment and CFD simulation. Eng. Appl. Comput. Fluid 20115, 541–552. [Google Scholar] [CrossRef]
  33. Ozmen-Cagatay, H.; Kocaman, S.; Guzel, H. Investigation of dam-break flood waves in a dry channel with a hump. J. Hydro-Environ. Res. 20148, 304–315. [Google Scholar] [CrossRef]
  34. Gu, S.L.; Zheng, S.P.; Ren, L.Q.; Xie, H.W.; Huang, Y.F.; Wei, J.H.; Shao, S.D. SWE-SPHysics Simulation of Dam Break Flows at South-Gate Gorges Reservoir. Water 20179, 387. [Google Scholar] [CrossRef]
  35. Evangelista, S. Experiments and Numerical Simulations of Dike Erosion due to a Wave Impact. Water 20157, 5831–5848. [Google Scholar] [CrossRef][Green Version]
  36. Bocci, M.; Chiarlo, R.; De Nat, L.; Fanelli, A.; Petersen, O.; Sorensen, J.T.; Friss-Christensen, A. Modelling of impacts from a long sea outfall outside of the Venice Lagoon (Italy). In Proceedings of the MWWD—IEMES 2006 Conference, Antalya, Turkey, 6–10 November 2006; MWWD Organization: Antalya, Turkey, 2006. [Google Scholar]
  37. Nikolaos, T.F.; Georgios, M.H. Three-dimensional numerical simulation of wind-induced barotropic circulation in the Gulf of Patras. Ocean Eng. 201037, 355–364. [Google Scholar]
  38. Goyal, R.; Rathod, P. Hydrodynamic Modelling for Salinity of Singapore Strait and Johor Strait using MIKE 3FM. In Proceedings of the 2011 2nd International Conference on Environmental Science and Development, Singapore, 26–28 February 2011. [Google Scholar]
  39. Khankandi, A.F.; Tahershamsi, A.; Soares-Frazão, S. Experimental investigation of reservoir geometry effect on dam-break flow. J. Hydraul. Res. 201250, 376–387. [Google Scholar] [CrossRef]
  40. Flow Science Inc. FLOW-3D User’s Manuals; Flow Science Inc.: Santa Fe, NM, USA, 2007. [Google Scholar]
  41. Danish Hydraulic Institute (DHI). MIKE 3 Flow Model FM. Hydrodynamic Module-User Guide; DHI: Horsholm, Denmark, 2014. [Google Scholar]
  42. Pilotti, M.; Tomirotti, M.; Valerio, G. Simplified Method for the Characterization of the Hydrograph following a Sudden Partial Dam Break. J. Hydraul. Eng. 2010136, 693–704. [Google Scholar] [CrossRef]
  43. Hooshyaripor, F.; Tahershamsi, A.; Razi, S. Dam break flood wave under different reservoir’s capacities and lengths. Sādhanā 201742, 1557–1569. [Google Scholar] [CrossRef]
  44. Kocaman, S.; Ozmen-Cagatay, H. Investigation of dam-break induced shock waves impact on a vertical Wall. J. Hydrol. 2015525, 1–12. [Google Scholar] [CrossRef]
  45. Liu, H.; Liu, H.J.; Guo, L.H.; Lu, S.X. Experimental Study on the Dam-Break Hydrographs at the Gate Location. J. Ocean Univ. China 201716, 697–702. [Google Scholar] [CrossRef]
  46. Marra, D.; Earl, T.; Ancey, C. Experimental Investigations of Dam Break Flows down an Inclined Channel. In Proceedings of the 34th World Congress of the International Association for Hydro- Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering, Brisbane, Australia, 26 June–1 July 2011. [Google Scholar]
  47. Wang, J.; Liang, D.F.; Zhang, J.X.; Xiao, Y. Comparison between shallow water and Boussinesq models for predicting cascading dam-break flows. Nat. Hazards 201683, 327–343. [Google Scholar] [CrossRef]
  48. Yang, C.; Lin, B.L.; Jiang, C.B.; Liu, Y. Predicting near-field dam-break flow and impact force using a 3D model. J. Hydraul. Res. 201048, 784–792. [Google Scholar] [CrossRef]
Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.

On-chip fabrication and in-flow 3D-printing of cellladen microgel constructs: From chip to scaffold materials in one integral process

cellladen 마이크로 겔 구조의 온칩 제작 및 인플 로우 3D 프린팅 : 하나의 통합 프로세스에서 칩에서 스캐폴드 재료까지

Benjamin Reineke 1,2, Ilona Paulus 3, Jonas Hazur 6, Madita Vollmer 4, Gültekin Tamgüney 4,5, Stephan Hauschild1
, Aldo R. Boccacini 6, Jürgen Groll 3, Stephan Förster *1,2
1 Jülich Centre for Neutron Science (JCNS-1/IBI-8), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
2 Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany
3 Department of Functional Materials in Medicine and Dentistry (FMZ) and Bavarian Polymer Institute (BPI),
University of Würzburg, 97070 Würzburg, Germany
4 Forschungszentrum Jülich GmbH, Institute of Biological Information Processing – Structural Biochemistry (IBI7), Jülich, Germany
5 Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Düsseldorf, Germany
6 Institute of Biomaterials, University of Erlangen-Nuremberg, Cauerstr. 6, 91058, Erlangen, Germany

Summary

Bioprinting has evolved into a thriving technology for the fabrication of cell-laden scaffolds. Bioinks are the most critical component for bioprinting. Recently, microgels have been introduced as a very promising bioink enabling cell protection and the control of the cellular microenvironment. However, their microfluidic fabrication inherently seemed to be a limitation. Here we introduce a direct coupling of microfluidics and 3D-printing for the microfluidic production of cell-laden microgels with direct in-flow bioprinting into stable scaffolds. The methodology enables the continuous on-chip encapsulation of cells into monodisperse microdroplets with subsequent in-flow cross-linking to produce cell-laden microgels, which after exiting a microtubing are automatically jammed into thin continuous microgel filaments. The integration into a 3D printhead allows direct in-flow printing of the filaments into free-standing three-dimensional scaffolds. The method is demonstrated for different cross-linking methods and cell lines. With this advancement, microfluidics is no longer a bottleneck for biofabrication.

Bioprinting은 세포가있는 스캐 폴드 제작을 위한 번성하는 기술로 진화했습니다. 바이오 잉크는 바이오 프린팅에 가장 중요한 구성 요소입니다. 최근 마이크로 젤은 세포 보호 및 세포 미세 환경 제어를 가능하게 하는 매우 유망한 바이오 잉크로 도입되었습니다.

그러나 이들의 미세 유체 제작은 본질적으로 한계로 보였습니다. 여기에서 우리는 안정적인 스캐 폴드에 직접 유입 바이오 프린팅을 사용하여 세포가 실린 마이크로 겔의 미세 유체 생산을 위한 미세 유체 및 3D 프린팅의 직접 결합을 소개합니다.

이 방법론은 세포를 단 분산 미세 방울로 연속 온칩 캡슐화하고 후속 유입 교차 연결을 통해 세포가 가득한 마이크로 겔을 생성 할 수 있으며, 이는 마이크로 튜브를 종료 한 후 얇은 연속 마이크로 겔 필라멘트에 자동으로 걸린다. 3D 프린트 헤드에 통합되어 필라멘트를 독립형 3 차원 스캐 폴드로 직접 유입 인쇄 할 수 있습니다.

이 방법은 다양한 가교 방법 및 세포주에 대해 설명됩니다. 이러한 발전으로 미세 유체 학은 더 이상 바이오 패브리 케이션의 병목 현상이 아닙니다.

Bioprinting은 신체 조직을 모방하거나 대체하기위한 3 차원 세포 실장 구조를 제작하는 새로운 기술입니다.

(1) 조직 공학 및 약물 전달뿐만 아니라 질병 연구 및 치료 개발에 중요한 역할을합니다. 바이오 프린팅에서 세포와 물질은 바이오 잉크 (2,3)로 공식화되어 계층 적으로 구조화 된 3D 스캐 폴드로 직접 인쇄됩니다. 바이오 프린팅의 궁극적 인 목표는 3 차원 적으로 제작 된 구조적 배열이 생물학적 성숙을 촉진하고 가속화한다는 근거를 바탕으로 표적 조직 또는 기관의 전체 또는 부분 기능을 나타내는 세포가있는 스캐 폴드를 생산하는 것입니다.

(4) 따라서 바이오 잉크는 바이오 프린팅 기술의 중요한 구성 요소입니다. 그들은 주로 세포와 생물 활성 분자를 캡슐화 할 수있는 물질, 즉 하이드로 겔에 의존하며 압출 인쇄와 같은 적합한 인쇄 기술에 사용하여 원하는 3 차원 스캐 폴드 또는 구조물을 제작할 수 있습니다. 바이오 잉크의 설계는 유동성 및 탄성 특성을 미세 조정하여 압출 중에 충분히 전단 얇게 만들고,이어서 응고 후 원하는 기계적 안정성과 탄성을 빠르게 개발하여 안정적인 스캐 폴드를 형성해야하기 때문에 까다롭습니다.

또한, 바이오 잉크는 생체 적합성이어야하며 세포 생존력과 적절한 제조 후 행동을 촉진 할 수있을만큼 충분히 생체 기능적이어야하며 충분한 영양분과 산소를 ​​공급할 수 있어야합니다. 바이오 잉크로 가장 두드러진 하이드로 겔 전구체 용액이 사용되며, 때로는 약간 사전 가교된 형태로 사용되며, 프린팅 후 가교되어 구조를 안정화합니다.

종종 발생하는 문제는 세포 침강, 불균일 혼합 및 생체 적합성 제형과 인쇄 사이의 상충 관계이며, 세포가 유동 제형에서 전단력을 직접 경험하기 때문에 결과적인 모양 충실도입니다. 이러한 한계를 극복하기 위해 Highley et al.

(5) 최근 microgel bioinks의 사용을 제안했습니다. 콜로이드 특성으로 인해 마이크로 겔 바이오 잉크는 전단 얇아지고 정지 상태에서 빠르게 응고되는 반면 부드러운 콜로이드에로드 된 세포는 전단 보호됩니다. 인쇄 된 마이크로 겔 스캐 폴드는 계면 중합체 얽힘이 충분하지 않은 경우 2 차 가교에 의해 추가로 안정화 될 수 있습니다.

Microgels는 세포 미세 환경을 조정하는 이점을 더 제공합니다. 따라서, 세포가 가득 찬 마이크로 겔을 제조하는 방법은 이미 개발되었으며, 특히 매우 균일 한 크기의 마이크로 겔을 연속 공정으로 제작할 수있는 마이크로 유체 학 분야에서 이미 개발되었습니다. (6-8) 마이크로 겔은 EDTA- 복합체 (11,12) 또는 열 유도에 의해 조절 될 수있는 알기 네이트 / Ca2 + 이온 복합체 형성 (9,10)과 같은 물리적 가교에 의해 형성 될 수 있음이 입증되었습니다. 젤라틴 용액을 20 ° C 이하로 냉각하는 것과 같은 겔화. (9,13) 화학적 가교 반응은 마이크로 겔의 더 큰 안정성과 더 나은 기계적 특성을 제공합니다.

예를 들면 기능화 된 젤라틴, 히알루 노 레이트, 폴리에틸렌 글리콜 또는 폴리 글리세롤 (12, 14-16)에 대한 마이클 유형 반응, 폴리 글리세롤 (17) 및 광 가교 (18)에 대한 아 지드-알킨 클릭 반응은 다음과 같은 광개시제 및 가교기를 필요로 합니다. 폴리에틸렌 글리콜에 대해 나타났습니다.

캡슐화된 세포에는 줄기 세포 (9,12,14,15), 크립트 및 페 이어 세포 (10), 간 세포 (HepG2) 및 내피 세포 (HUVEC) (18), NIH 3T3 섬유 아세포 (6)가 포함됩니다. 지금까지 Fan et al.에 의해 세포가 실린 마이크로 겔을 기반으로하는 기능성 스캐 폴드의 제작이 보여졌습니다.

(19) 겔 -MA 마이크로 겔의 에멀젼 기반 제조 및 Compaan et al. (20) 젤라틴 마이크로 겔 충전제 입자. 미세 유체 생성 마이크로 겔의 경우 이것은 최근 Highley et al.에 의해 처음으로 입증되었습니다. (5). 마이크로 겔 기반 바이오 잉크 및 스캐 폴드에 대한 바이오 프린팅에 대한 지금까지 제한된 수의 연구에 대한 이유는 소량의 마이크로 겔을 생성하는 마이크로 유체의 필수 조합과 교차 결합, 준비를 포함하는 여러 포스트 칩 배치 공정 단계가 뒤 따르기 때문입니다. bioink의, 그리고 원하는 스캐 폴드에 후속 bioprinting.

이것은 현재 microgel biofabrication을 시간 소모적이고 생산성이 낮은 다단계 공정으로 만듭니다. 따라서 원하는 스캐 폴드의 제조를위한 마이크로 겔 및 바이오 프린팅을위한 미세 유체가 하나의 연속적이고 자동화 가능한 프로세스에 통합 될 수 있다면 매우 바람직 할 것입니다.

여기에서 우리는 미세 유체 칩이 세포를 방울로 온칩 캡슐화하도록 설계 될 수 있음을 보여줍니다. 이는 마이크로 겔을 생성하기 위해 흐름에서 광 가교 결합 된 다음 다운 스트림 마이크로 튜브에서 자동으로 잼되어 얇은 마이크로 겔 필라멘트를 지속적으로 형성합니다. 마이크로 튜브는 3D 프린터의 프린트 헤드에 통합되어 필라멘트를 독립형 3 차원으로 직접 유입 인쇄합니다.

Results and discussion

Microfluidic device and controlled droplet production

우리의 목표는 (i) 낮은 전단 응력 세포 캡슐화, (ii) 물리적 또는 화학적 가교에 대한 가변성, (iii) 미세 액적 직경의 큰 변화, (iv)이를 결합 할 수 있는 기능을 위한 미세 유체 칩을 3D 프린터로 설계하는 것이었습니다.

따라서 디자인은 높은 세포 생존력을 위해 좁은 채널 섹션 내의 세포에 대한 전단력을 최소화해야 합니다. 다양한 물리적 및 화학적 가교 반응을 수행 할 수 있도록 입구 채널 설계는 세포, 폴리머, 가교 및 추가 제제를 포함하는 용액의 순차적 혼합을 허용해야 합니다. 단일 세포 캡슐화가 필요한 경우 미세 방울은 300 µm에서 50 µm까지 제어 가능한 직경을 가져야 106 / ml의 세포 밀도에 도달 할 수 있습니다.

Fig. 1: Three-dimensional schematic view of the multilayer double 3D-focusing microfluidic channel system, (b) control of droplet diameter via the Capiilary number Ca, and accessible hydrodynamic regimes for droplet production: squeezing (c), dripping (d) and jetting (e). The scale bars are 200 µm.
Fig. 1: Three-dimensional schematic view of the multilayer double 3D-focusing microfluidic channel system, (b) control of droplet diameter via the Capiilary number Ca, and accessible hydrodynamic regimes for droplet production: squeezing (c), dripping (d) and jetting (e). The scale bars are 200 µm.

따라서 우리는 두 개의 후속 혼합 교차로 3 차원 흐름 초점을 허용 한 다음 제어 된 액적 형성을위한 하류 좁은 오리피스가 뒤 따르는 채널 설계를 사용했습니다. 디자인은 그림 1에 개략적으로 표시되어 있습니다. 여기에는 세포와 전구체 폴리머를 포함하는 중앙 스트림 용액을위한 입구 채널과 완충 용액, 배양 배지, 생리 활성 물질 또는 가교제를 포함 할 수있는 두 개의 측면 채널이 있습니다. 측면 채널 흐름은 입구 채널 흐름을 세포에 대한 전단력이 최소 인 채널의 중앙에 3 차원 적으로 집중시킵니다. 그 후, 수성 스트림은 액적 형성을 제어하는 ​​좁은 오리피스 섹션으로 들어가기 위해 오일 상으로 3 차원 적으로 집중됩니다. 좁은 섹션은 다양한 유체 역학 체제에 액세스하여 다양한 범위에 걸쳐 액적 크기를 변경할 수 있습니다. 다운 스트림 채널은 방울이 채널 중심 유선에서 안정적인 방울 트레인을 형성하도록 충분히 좁게 유지됩니다. 3D 이중 초점 칩은 다층 기술을 사용하는 소프트 리소그래피로 제작되었으며 지원 정보 (그림 S2-S4, S7)에 설명 된대로 흐름이 시뮬레이션되었습니다. 액적 분해는 외부 유체에 의해 가해지는 점성 전단력 𝐹𝑠ℎ𝑒ar 표면 장력에서 발생하는 고정 계면 력 𝐹𝐹𝛾𝛾을 초과 할 때 발생합니다. 두 힘은 직접 연속 유상 η 평균 유입 흐름 속도 (V)의 점도 환산 수 무차 모세관 수가 CA = 𝐹𝑠ℎ𝑒ar/𝐹γ, 그리고 CA = 𝐹𝑠ℎ𝑒ar/𝐹γ = 같은 표면 장력 γ가 관련 𝜂𝜂 𝛾. 캐 필러 리 수에 따라 액적 생성을위한 다양한 유체 역학 체제를 구별 할 수 있습니다. c) 분사 체제 (Ca> 1). (21-25) 그림 1에서 볼 수 있듯이 가변 3D 수축 설계를 사용하면 액적 생산을위한 세 가지 유체 역학 체제에 모두 액세스 할 수 있으며 모세관 수는 액적 생산을위한 주요 제어 매개 변수입니다. 체적 유량, 오일 점도 및 계면 장력을 조정하여 50 ~ 300 µm 범위의 목표 범위에서 액적 직경을 정밀하게 제어 할 수 있습니다. 각 점도 및 계면 장력은 지원 정보의 표 SI에 요약되어 있습니다.

Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.
Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.
Fig. 3: a) Photograph of a standard meander-shaped layer fabricated by microgel filament deposition printing. The lines have a thickness of 300 µm. b) photograph of a cross-bar pattern obtained by on-top deposition of several microgel filaments. The average linewidth is 1 mm. c) photograph of a donut-shaped microgel construct. The microgels have been fluorescently labelled by FITC-dextran to demonstrate the intrinsic microporosity corresponding to the black non-fluorescent regions, d) light microscopy image of a construct edge showing that fused adhesive microgels form a continuous, three-dimensional selfsupporting scaffold with intrinsic micropores.
Fig. 3: a) Photograph of a standard meander-shaped layer fabricated by microgel filament deposition printing. The lines have a thickness of 300 µm. b) photograph of a cross-bar pattern obtained by on-top deposition of several microgel filaments. The average linewidth is 1 mm. c) photograph of a donut-shaped microgel construct. The microgels have been fluorescently labelled by FITC-dextran to demonstrate the intrinsic microporosity corresponding to the black non-fluorescent regions, d) light microscopy image of a construct edge showing that fused adhesive microgels form a continuous, three-dimensional selfsupporting scaffold with intrinsic micropores.
Fig. 4: a) Scheme of the perfusion chamber consisting of an upstream and downstream chamber, perfusion ports, and removable scaffolds to stabilize the microgel construct during 3D-printing, b) photograph of a microgel construct in the perfusion chamber directly after printing and removal of the scaffolds, c) confocal microscopy image of the permeation front of a fluorescent dye, where the high dye concentration in the micropores can be clearly seen, d) confocal microscopy image of YFP-labelled HEK-cells within a microgel construct.
Fig. 4: a) Scheme of the perfusion chamber consisting of an upstream and downstream chamber, perfusion ports, and removable scaffolds to stabilize the microgel construct during 3D-printing, b) photograph of a microgel construct in the perfusion chamber directly after printing and removal of the scaffolds, c) confocal microscopy image of the permeation front of a fluorescent dye, where the high dye concentration in the micropores can be clearly seen, d) confocal microscopy image of YFP-labelled HEK-cells within a microgel construct.
Fig. 5: a) Layer-by-layer printing of microgel construct with integrated perfusion channel. After printing of the first layer, a hollow perfusion channel is inserted. Subsequently, the second and third layers are printed. b) The construct is directly printed into a perfusion chamber. The perfusion chamber provides whole construct permeation via flows cin and cout, as well as independent flow through the perfusion channel via flows vin and vout. c) Photograph of a perfusion chamber containing the construct directly after printing. The flow of the fluorescein solution through the integrated PVA hollow channel is clearly visible.
Fig. 5: a) Layer-by-layer printing of microgel construct with integrated perfusion channel. After printing of the first layer, a hollow perfusion channel is inserted. Subsequently, the second and third layers are printed. b) The construct is directly printed into a perfusion chamber. The perfusion chamber provides whole construct permeation via flows cin and cout, as well as independent flow through the perfusion channel via flows vin and vout. c) Photograph of a perfusion chamber containing the construct directly after printing. The flow of the fluorescein solution through the integrated PVA hollow channel is clearly visible.
Fig. 6: a) Photograph of an alginate capsule fiber formed after exiting the microtube. b) Confocal fluorescence microscopy image of part of a 3D-printed alginate capsule construct. The fluorescence arises from encapsulated fluorescently labelled polystyrene microbeads to demonstrate the integrity and stability of the alginate capsules.
Fig. 6: a) Photograph of an alginate capsule fiber formed after exiting the microtube. b) Confocal fluorescence microscopy image of part of a 3D-printed alginate capsule construct. The fluorescence arises from encapsulated fluorescently labelled polystyrene microbeads to demonstrate the integrity and stability of the alginate capsules.

  1. A. Atala, Chem. Rev. 2020, 120, 10545-10546.
  2. J. Groll, J. A. Burdick, D. W. Cho, B. Derby, M. Gelinsky, S. C. Heilshorn, T. Jüngst, J. Malda, V. A
    Mironov, K. Nakayama, A. Ovisanikov, W. Sun, S. Takeuchi, J. J. Yoo, T. B. F. Woodfield,
    Biofabrication 2019, 11, 013001.
  3. W. Sun, B. Starly, A. C. Daly, J. A. Burdick, J. Groll, G. Skeldon, W. Shu, Y. Sakai, M. Shinohara,
    M. Nishikawa, J. Jang, D.-W. Cho, M. Nie, S. Takeuchi, S. Ostrovidov, A. Khademhosseini, R. D. Kamm,
    V. Mironov, L. Moroni, I. T. Ozbolat, Biofabrication 2020, 12, 022002.
  4. R. Levato, T. Juengst, R. G. Scheuring, T. Blunk, J. Groll, J. Malda, Adv. Mater. 2020, 32, 1906423.
  5. C. B. Highley, K. H. Song, A. C. Daly, J. A. Burdick, Adv. Sci. 2019, 6, 1801076.
  6. D. Velasco, E. Tumarkin, E. Kumacheva, Small 2012, 8, 1633-1642.
  7. W. Jiang, M. Li, Z. Chen, K. W. Leong, Lab Chip 2016, 16, 4482-4506.
  8. A. C. Daly, L. Riley, T. Segura, J. A. Burdick, Nat. Rev. 2020, 5, 20-43.
  9. A. S. Mao, B. Özkale, N. J. Shah, K. H. Vining, T. Descombes, L. Zhang, C. M. Tringides, S.-W.
    Wong, J.-W. Shin, D. T. Scadden, D. A. Weitz, D. J. Mooney, Proc. Natl. Acad. Sci. 2019, 116, 15392-
    15397.
  10. S. R. Pajoumshariati, M. Azizi, D. Wesner, P. G. Miller, M. L. Shuler, A. Abbaspourrad, ACS Appl.
    Mater. Interfaces 2018, 10, 9235-9246.
  11. A. S. Mao, J.-W. Shin, S. Utech, H. Wang, O. Uzun, W. Li, M. Cooper, Y. Hu, L. Zhang, D. A.
    Weitz, D. J. Mooney, Nat. Mater. 2017, 16, 236-243.
  12. P. S. Lienemann, T. Rossow, A. S. Mao, Q. Vallmajo-Martin, M. Ehrbar, D. J. Mooney, Lab Chip,
    2017, 17, 727.
  13. F. Chen, J. Xue, J. Zhang, M. Bai, X. Yu, X.; C. Fan, Y. Zhao, J. Am. Chem. Soc. 2020, 142, 2889-
    2896.
  14. Q. Feng, Q. Li, H. Wen, J. Chen, M. Liang, H. Huang, D. Lan, H. Dong, X. Cao, Adv. Funct. Mater.,
    2019, 29, 1096690.
  15. L. P. B. Guerzoni, T. Yoshinari, D. B. Gehlen, D. Rommel, T. Haraszti, M. Akashi, L. De Laporte,
    Biomacromolecules 2019, 20, 3746-3754
  16. T. Rossow, J. A. Heyman, A. J. Ehrlicher, A. Langhoff, D. A. Weitz, R. Haag, S. Seiffert, J. Am.
    Chem. Soc. 2012, 134, 4983-4989.
  17. E. Kapourani, F. Neumann, K. Achazi, J. Dernedde, R. Haag, Macromol. Bioscience 2018, 18,
    1800116
  18. H. Wang, H. Liu, H. Liu, W. Su, W. Chen, J. Qin, Adv. Mater. Technol. 2019, 4, 1800632.
  19. C. Fan, S.-H. Zhan, Z.-X. Dong, W. Yang, W.-S. Deng, X. Liu, P. Suna, D.-A. Wang, Mater. Sci.
    Eng. C 2019, 108, 110399.
  20. A. M. Compaan, K. Song, W. Chai, Y. Huang, ACS Appl. Mater. Interfaces 2020, 12, 7855-7868.
  21. S. L. Anna, H. C. Mayer, Phys. Fluids 2006, 18, 121512.
  22. T. Ward, M. Faivre, M. Abkarian, H. A. Stone, Electrophoresis 2005, 26, 3716-3724.
  23. F. Lapierre, N. Wu, Y. Zhu, Proc. SPIE 2011, 8204, 82040H-1.
  24. C. A. Stan, S. K. Y. Tang, G. M. Whitesides, Anal. Chem. 2009, 81, 2399-2402.
  25. J. Tan, J. H. Xu, S. W. Li, G. S. Luo, Chem. Eng. J. 2008, 136, 306-311.
  26. R.-C. Luo, C.-H. Chen, Soft 2012, 1, 1-23.
  27. C. H. Choi, J. H. Jung, T. S. Hwang, C. S. Lee, Macromol. Res. 2009, 17, 163-167.
  28. A. J. D. Krüger, O. Bakirman, P. B. Guerzoni, A. Jans, D. B. Gehlen, D. Rommel, T. Haraszti, A. J.
    C. Kuehne, L. De Laporte, Adv. Mater. 2019, 31, 1903668.
  29. D. B. Kolesky, K. A. Homan, M. A. Skylar-Scott, J. A. Lewis, Proc. Natl. Acad. Sci. 2016, 113,
    3179-3184
  30. A. K. Miri, I. Mirzaee, S. Hassan, S. M. Oskui, D. Nieto, A. Khademhosseini, Y. S. Zhang, Lab Chip
    2019, 19, 2019.
  31. F. A. Plamper, W. Richtering Acc. Chem. Res. 2017, 50, 131-140.
  32. S. Sun, M. Li, A. Liu, Int. J. Adhesion Adhesives 2013, 41, 98-106.
Modeling of contactless bubble–bubble interactions in microchannels with integrated inertial pumps

Modeling of contactless bubble–bubble interactions in microchannels with integrated inertial pumps

통합 관성 펌프를 사용하여 마이크로 채널에서 비접촉식 기포-기포 상호 작용 모델링

Physics of Fluids 33, 042002 (2021); https://doi.org/10.1063/5.0041924 B. Hayesa) G. L. Whitingb), and  R. MacCurdyc)

ABSTRACT

In this study, the nonlinear effect of contactless bubble–bubble interactions in inertial micropumps is characterized via reduced parameter one-dimensional and three-dimensional computational fluid dynamics (3D CFD) modeling. A one-dimensional pump model is developed to account for contactless bubble-bubble interactions, and the accuracy of the developed one-dimensional model is assessed via the commercial volume of fluid CFD software, FLOW-3D. The FLOW-3D CFD model is validated against experimental bubble dynamics images as well as experimental pump data. Precollapse and postcollapse bubble and flow dynamics for two resistors in a channel have been successfully explained by the modified one-dimensional model. The net pumping effect design space is characterized as a function of resistor placement and firing time delay. The one-dimensional model accurately predicts cumulative flow for simultaneous resistor firing with inner-channel resistor placements (0.2L < x < 0.8L where L is the channel length) as well as delayed resistor firing with inner-channel resistor placements when the time delay is greater than the time required for the vapor bubble to fill the channel cross section. In general, one-dimensional model accuracy suffers at near-reservoir resistor placements and short time delays which we propose is a result of 3D bubble-reservoir interactions and transverse bubble growth interactions, respectively, that are not captured by the one-dimensional model. We find that the one-dimensional model accuracy improves for smaller channel heights. We envision the developed one-dimensional model as a first-order rapid design tool for inertial pump-based microfluidic systems operating in the contactless bubble–bubble interaction nonlinear regime

이 연구에서 관성 마이크로 펌프에서 비접촉 기포-기포 상호 작용의 비선형 효과는 감소 된 매개 변수 1 차원 및 3 차원 전산 유체 역학 (3D CFD) 모델링을 통해 특성화됩니다. 비접촉식 기포-버블 상호 작용을 설명하기 위해 1 차원 펌프 모델이 개발되었으며, 개발 된 1 차원 모델의 정확도는 유체 CFD 소프트웨어 인 FLOW-3D의 상용 볼륨을 통해 평가됩니다.

FLOW-3D CFD 모델은 실험적인 거품 역학 이미지와 실험적인 펌프 데이터에 대해 검증되었습니다. 채널에 있는 두 저항기의 붕괴 전 및 붕괴 후 기포 및 유동 역학은 수정 된 1 차원 모델에 의해 성공적으로 설명되었습니다. 순 펌핑 효과 설계 공간은 저항 배치 및 발사 시간 지연의 기능으로 특징 지어집니다.

1 차원 모델은 내부 채널 저항 배치 (0.2L <x <0.8L, 여기서 L은 채널 길이)로 동시 저항 발생에 대한 누적 흐름과 시간 지연시 내부 채널 저항 배치로 지연된 저항 발생을 정확하게 예측합니다. 증기 방울이 채널 단면을 채우는 데 필요한 시간보다 큽니다.

일반적으로 1 차원 모델 정확도는 저수지 근처의 저항 배치와 1 차원 모델에 의해 포착되지 않는 3D 기포-저수지 상호 작용 및 가로 기포 성장 상호 작용의 결과 인 짧은 시간 지연에서 어려움을 겪습니다. 채널 높이가 작을수록 1 차원 모델 정확도가 향상됩니다. 우리는 개발 된 1 차원 모델을 비접촉 기포-기포 상호 작용 비선형 영역에서 작동하는 관성 펌프 기반 미세 유체 시스템을 위한 1 차 빠른 설계 도구로 생각합니다.

REFERENCES

1.S. Hassan and X. Zhang, “ Design and fabrication of capillary-driven flow device for point-of-care diagnostics,” Biosensors 10, 39 (2020). https://doi.org/10.3390/bios10040039, Google ScholarCrossref
2.Q. Shizhi and H. Bau, “ Magneto-hydrodynamics based microfluidics,” Mech. Res. Commun. 36, 10 (2009). https://doi.org/10.1016/j.mechrescom.2008.06.013, Google ScholarCrossref
3.N. Mishchuk, T. Heldal, T. Volden, J. Auerswald, and H. Knapp, “ Micropump based on electroosmosis of the second kind,” Electrophoresis 30, 3499 (2009). https://doi.org/10.1002/elps.200900271, Google ScholarCrossref
4.J. Snyder, J. Getpreecharsawas, D. Fang, T. Gaborski, C. Striemer, P. Fauchet, D. Borkholder, and J. McGrath, “ High-performance, low-voltage electroosmotic pumps with molecularly thin silicon nanomembranes,” Proc. Nat. Acad. Sci. U. S. A. 110, 18425–18430 (2013). https://doi.org/10.1073/pnas.1308109110, Google ScholarCrossref
5.K. Vinayakumar, G. Nadiger, V. Shetty, S. Dinesh, M. Nayak, and K. Rajanna, “ Packaged peristaltic micropump for controlled drug delivery application,” Rev. Sci. Instrum. 88, 015102 (2017). https://doi.org/10.1063/1.4973513, Google ScholarScitation, ISI
6.D. Duffy, H. Gillis, J. Lin, N. Sheppard, and G. Kellogg, “ Microfabricated centrifugal microfluidic systems: Characterization and multiple enzymatic assays,” Anal. Chem. 71, 4669 (1999). https://doi.org/10.1021/ac990682c, Google ScholarCrossref
7.V. Gnyawali, M. Saremi, M. Kolios, and S. Tsai, “ Stable microfluidic flow focusing using hydrostatics,” Biomicrofluidics 11, 034104 (2017). https://doi.org/10.1063/1.4983147, Google ScholarScitation, ISI
8.J. Lake, K. Heyde, and W. Ruder, “ Low-cost feedback-controlled syringe pressure pumps for microfluidics applications,” PLoS One 12, e0175089 (2017). https://doi.org/10.1371/journal.pone.0175089, Google ScholarCrossref
9.M. I. Mohammed, S. Haswell, and I. Gibson, “ Lab-on-a-chip or chip-in-a-lab: Challenges of commercialization lost in translation,” Procedia Technology 20, 54–59 (2015), proceedings of The 1st International Design Technology Conference, DESTECH2015, Geelong. Google ScholarCrossref
10.E. Torniainen, A. Govyadinov, D. Markel, and P. Kornilovitch, “ Bubble-driven inertial micropump,” Phys. Fluids 24, 122003 (2012). https://doi.org/10.1063/1.4769755, Google ScholarScitation, ISI
11.H. Hoefemann, S. Wadle, N. Bakhtina, V. Kondrashov, N. Wangler, and R. Zengerle, “ Sorting and lysis of single cells by bubblejet technology,” Sens. Actuators, B 168, 442–445 (2012). https://doi.org/10.1016/j.snb.2012.04.005, Google ScholarCrossref
12.B. Hayes, A. Hayes, M. Rolleston, A. Ferreira, and J. Kirsher, “ Pulsatory mixing of laminar flow using bubble-driven micro-pumps,” in Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition (2018), Vol. 7. Google ScholarCrossref
13.E. Ory, H. Yuan, A. Prosperetti, S. Popinet, and S. Zaleski, “ Growth and collapse of a vapor bubble in a narrow tube,” Phys. Fluids 12, 1268 (2000). https://doi.org/10.1063/1.870381, Google ScholarScitation, ISI
14.Z. Yin and A. Prosperetti, “‘ Blinking bubble’ micropump with microfabricated heaters,” J. Micromech. Microeng. 15, 1683 (2005). https://doi.org/10.1088/0960-1317/15/9/010, Google ScholarCrossref
15.M. Einat and M. Grajower, “ Microboiling measurements of thermal-inkjet heaters,” J. Microelectromech. Syst. 19, 391 (2010). https://doi.org/10.1109/JMEMS.2010.2040946, Google ScholarCrossref
16.A. Govyadinov, P. Kornilovitch, D. Markel, and E. Torniainen, “ Single-pulse dynamics and flow rates of inertial micropumps,” Microfluid. Nanofluid. 20, 73 (2016). https://doi.org/10.1007/s10404-016-1738-x, Google ScholarCrossref
17.E. Sourtiji and Y. Peles, “ A micro-synthetic jet in a microchannel using bubble growth and collapse,” Appl. Therm. Eng. 160, 114084 (2019). https://doi.org/10.1016/j.applthermaleng.2019.114084, Google ScholarCrossref
18.B. Hayes, A. Govyadinov, and P. Kornilovitch, “ Microfluidic switchboards with integrated inertial pumps,” Microfluid. Nanofluid. 22, 15 (2018). https://doi.org/10.1007/s10404-017-2032-2, Google ScholarCrossref
19.P. Kornilovitch, A. Govyadinov, D. Markel, and E. Torniainen, “ One-dimensional model of inertial pumping,” Phys. Rev. E 87, 023012 (2013). https://doi.org/10.1103/PhysRevE.87.023012, Google ScholarCrossref
20.H. Yuan and A. Prosperetti, “ The pumping effect of growing and collapsing bubbles in a tube,” J. Micromech. Microeng. 9, 402–413 (1999). https://doi.org/10.1088/0960-1317/9/4/318, Google ScholarCrossref
21.J. Zou, B. Li, and C. Ji, “ Interactions between two oscillating bubbles in a rigid tube,” Exp. Therm. Fluid Sci. 61, 105 (2015). https://doi.org/10.1016/j.expthermflusci.2014.10.021, Google ScholarCrossref
22.C. Hirt and B. Nichols, “ Volume of fluid (vof) method for the dynamics of free boundaries,” J. Comput. Phys. 39, 201–225 (1981). https://doi.org/10.1016/0021-9991(81)90145-5, Google ScholarCrossref
23.C. Borgnakke and R. E. Sonntag, Fundamentals of Thermodynamics, 8th ed. ( Wiley, 1999). Google Scholar
24.O. E. Ruiz, “ CFD model of the thermal inkjet droplet ejection process,” in Proceeding of Heat Transfer Summer Conference (2007), Vol. 3. Google ScholarCrossref
25.T. Theofanous, L. Biasi, H. Isbin, and H. Fauske, “ A theoretical study on bubble growth in constant and time-dependent pressure fields,” Chem. Eng. Sci. 24, 885–897 (1969). https://doi.org/10.1016/0009-2509(69)85008-6, Google ScholarCrossref
26.S. Timoshenko and J. Goodier, Theory of Elasticity, 3rd ed. ( McGaw-Hill, Inc., 1970). Google Scholar

Figure 2.1. Test Setup.The test setup consists of a clear plastic scale model tank attached to a rigid aluminum frame by three multi-axis load cells driven by a position-controlled servo hydraulic system.(Data acquisition cabling removed for clarity).

Coupled Simulation of Vehicle Dynamics and Tank Slosh. Phase 1 Report. Testing and Validation of Tank Slosh Analysis

Prepared byGlenn R. WendelSteven T. GreenRussell C. Burkey

Abstract:

차량 동력학의 컴퓨터 시뮬레이션은 차량 설계에서 귀중한 도구가 되었다. 그러나 그들은 차량의 탱크에서 유체 슬로싱의 복잡한 역학을 정확하게 시뮬레이션할 수 없다. 

유체 슬로쉬를 예측할 수 있는 컴퓨터 유체역학 CFD 분석 소프트웨어를 이용할 수 있지만, 군용 차량 애플리케이션용 유체 슬로쉬를 정확하게 예측하는데 이 소프트웨어의 사용은 입증되지 않았다. 이것은 차량 역학 분석과 결합된 CFD 분석의 사용을 개발 및 입증하여 유체 수송 시스템의 역학을 보다 정확하게 예측하는 다중 효소 프로그램의 첫 번째 단계다. 

이 단계의 목적은 일반적인 기동에 직면한 차량의 움직임에 따른 탱크에서 슬로시 역학을 예측하는 CFD 분석을 검증하는 것이다. 이를 위해, 5톤 FMTV 트럭을 시뮬레이션하는 시험 설비뿐만 아니라, 1/4 규모의 TOD 탱크 모델이 건설되었다. CFD 분석과 실험실 시험의 반응력과 유동 운동을 차선 변경과 요철을 포함한 6가지 모의 차량 기동에서 비교했다. 

CFD 분석은 상용 소프트웨어 패키지인 FLOW-3D-로 수행되었다. 테스트 탱크의 해당 측정값과 비교하기 위해 빈 탱크의 강체 동적 해석의 힘과 모멘트 예측에 순유체 힘과 모멘트 예측이 추가되었다. 

전반적으로, 그 결과는 CFD가 트럭에 탑재된 수상 수송 탱크의 유체 운동 및 유체 구조 상호작용 연구에 성공적으로 적용될 수 있음을 보여준다. 예측된 롤 모멘트와 측정된 롤 모멘트 사이에는 좋은 상관관계가 있다. 

여기에 제시된 CFD 시뮬레이션의 빠른 전환 시간을 감안할 때, 전술에 대한 전체 차량 반응의 높은 충실도 시뮬레이션을 위해 차량 강체 차체 동적 분석을 유체 역학 분석과 결합하는 것이 바람직하다는 전망이 나온다.

Computer simulation of vehicle dynamics has become a valuable tool in the design of vehicles. They are, however, unable to accurately simulate the complex dynamics of fluid sloshing in a tank on the vehicle. Computational Fluid Dynamics CFD analysis software is available that can predict fluid slosh, however, the use of this software in accurately predicting fluid slosh for a military vehicle application has not been demonstrated. This is the first phase of a multiphase program to develop and demonstrate the use of CFD analysis, coupled with vehicle dynamics analysis, to more accurately predict the dynamics of a fluid transport system. The objective of this phase is to validate the CFD analysis in predicting slosh dynamics on a tank subjected to motions of a vehicle encountering typical maneuvers. To accomplish this, a one-quarter-scale model of a TOLD tank was constructed, as well as a test fixture to simulate a five-ton FMTV truck. The reaction forces and the fluid motions of the CFD analysis and the laboratory test were compared for six simulated vehicle maneuvers including lane changes and bumps. The CFD analysis was conducted with the commercially available software package, FLOW-3D-. The net fluid force and moment predictions were added to the force and moment predictions of a rigid body dynamic analysis of the empty tank alone to compare to the corresponding measured values for the test tank. Overall, the results show that CFD can successfully be applied to the study of fluid motions and the fluid- structure interactions in truck-mounted water transport tanks. There is good correlation between the predicted and measured roll moment. Given the rapid turnaround time for the CFD simulations presented here, the outlook is encouraging for coupling a vehicle rigid body dynamics analysis to a fluid dynamics analysis for a high fidelity simulation of the complete vehicle response to maneuvers.

Keywords

Keywords: COMPUTATIONAL,FLUID,DYNAMICS,VEHICLES,*SLOSHING,TEST,AND,EVALUATION,COMPUTER,PROGRAMS,COMPUTERIZED,SIMULATION,COUPLING(INTERACTION),SIMULATION,ROLL,LABORATORY,TESTS,PREDICTIONS,VALIDATION,INTERACTIONS,MILITARY,VEHICLES,REACTION,TIME,MOTION,RESPONSE,TRANSPORT,MILITARY,APPLICATIONS,FLUIDS,TRUCKS,MANEUVERS,RIGIDITY,TEST,FIXTURES,WATER,TANKS

CFD 분석과 실험실 테스트의 작용력과 유체 운동은 다음과 같은 시뮬레이션 된 차량 기동에 대해 비교되었습니다.

  • AVTP Lane Change at 20 mph
  • AVTP Lane Change at 40 mph
  • 9” Half-Round Symmetric Bump at 10 mph
  • 12” Half-Round Symmetric Bump at 5 mph
  • 9” Trapezoidal Asymmetric Bump at 15 mph
  • 12” Trapezoidal Asymmetric Bump at 10 mph

CFD 분석은 상용 소프트웨어 패키지 FLOW-3D를 사용하여 수행되었습니다.

Rear Axle Roll Moment, 40-mph Lane Change.
Rear Axle Roll Moment, 40-mph Lane Change.
Figure 2.1.  Test Setup.The test setup consists of a clear plastic scale model tank attached to a rigid aluminum frame by three multi-axis load cells driven by a position-controlled servo hydraulic system.(Data acquisition cabling removed for clarity).
Figure 2.1. Test Setup.The test setup consists of a clear plastic scale model tank attached to a rigid aluminum frame by three multi-axis load cells driven by a position-controlled servo hydraulic system.(Data acquisition cabling removed for clarity).
Figure 2.2.  Test Setup Drawing.The load cell locations and the coordinate systems used in the testing and analysis are defined as shown.
Figure 2.2. Test Setup Drawing.The load cell locations and the coordinate systems used in the testing and analysis are defined as shown.
Figure 3.1.  Computational Mesh Definition
Figure 3.1. Computational Mesh Definition
Figure 3.2.  Rear Axle Roll Moment, 20-mph Lane Change
Figure 3.2. Rear Axle Roll Moment, 20-mph Lane Change
Figure 3.3.  Rear Axle Roll Moment, 40-mph Lane Change
Figure 3.3. Rear Axle Roll Moment, 40-mph Lane Change
Figure 3.4.  Rear Axle Roll Moment, 9” Trapezoidal Bump at 15 mph
Figure 3.4. Rear Axle Roll Moment, 9” Trapezoidal Bump at 15 mph
Figure 3.5.  Rear Axle Roll Moment, 12” Trapezoidal Bump at 10 mph
Figure 3.5. Rear Axle Roll Moment, 12” Trapezoidal Bump at 10 mph
Figure 3.8.  Fluid Configuration for 20-mph Lane Change.The viewpoint in these images is from the front of the vehicle looking in the negative y-direction.  Theinset in the video image is viewing the tank from the left side of the vehicle.
Figure 3.8. Fluid Configuration for 20-mph Lane Change.The viewpoint in these images is from the front of the vehicle looking in the negative y-direction. Theinset in the video image is viewing the tank from the left side of the vehicle.
Figure 3.9.  Fluid Configuration for 12” Trapezoidal Bump at 10 mph.The viewpoint in these images is from the front of the vehicle looking in the negative y-direction.  Theinset in the video image is viewing the tank from the left side of the vehicle.
Figure 3.9. Fluid Configuration for 12” Trapezoidal Bump at 10 mph.The viewpoint in these images is from the front of the vehicle looking in the negative y-direction. Theinset in the video image is viewing the tank from the left side of the vehicle.

REFERENCES

Abramson, H.N. [1966], The Dynamic Behavior of Liquids in Moving Containers,NASA SP-106.Flow Science, Inc. [2001], FLOW-3D, Version 8.0.1, Santa Fe, New Mexico.Working Model, Inc. [1997], Working Model 3D, Version 2.0, San Mateo, California.Coleman, H.W., Steele, W.G. [1989], Experimentation and Uncertainty Analysis forEngineers, John Wiley and Sons, New York, 1989

Fig. 9 (a) Velocity field, keyhole profile, and breakage of the keyhole to form bubble and (b) 2D temperature and velocity field along the longitudinal section

A Numerical Study on the Keyhole Formation During Laser Powder Bed Fusion Process

Keyhole에 대한 수치적 연구 : 레이저 분말 중 형성 베드 퓨전 공정

Subin Shrestha1
J.B. Speed School of Engineering,University of Louisville,Louisville, KY 40292
e-mail: subin.shrestha@louisville.edu

Y. Kevin Chou
J.B. Speed School of Engineering,University of Louisville,Louisville, KY 40292
e-mail: kevin.chou@louisville.edu

LPBF (Laser Powder Bed fusion) 공정 중 용융 풀의 동적 현상은 복잡하고 공정 매개 변수에 민감합니다. 에너지 밀도 입력이 특정 임계 값을 초과하면 키홀이라고 하는 거대한 증기 함몰이 형성 될 수 있습니다.

이 연구는 수치 분석을 통해 LPBF 과정에서 키홀 거동 및 관련 기공 형성을 이해하는 데 중점을 둡니다. 이를 위해 이산 분말 입자가 있는 열 유동 모델이 개발되었습니다.

이산 요소 방법 (DEM)에서 얻은 분말 분포는 계산 영역에 통합되어 FLOW-3D를 사용하는 3D 프로세스 물리학 모델을 개발합니다.

전도 모드 중 용융 풀 형성과 용융의 키홀 모드가 식별되고 설명되었습니다. 높은 에너지 밀도는 증기 기둥의 형성으로 이어지고 결과적으로 레이저 스캔 트랙 아래에 구멍이 생깁니다.

또한 다양한 레이저 출력과 스캔 속도로 인한 Keyhole 모양을 조사합니다. 수치 결과는 동일한 에너지 밀도에서도 레이저 출력이 증가함에 따라 Keyhole크기가 증가 함을 나타냅니다. Keyhole은 더 높은 출력에서 ​​안정되어 레이저 스캔 중 Keyhole 발생을 줄일 수 있습니다.

The dynamic phenomenon of a melt pool during the laser powder bed fusion (LPBF) process is complex and sensitive to process parameters. As the energy density input exceeds a certain threshold, a huge vapor depression may form, known as the keyhole. This study focuses on understanding the keyhole behavior and related pore formation during the LPBF process through numerical analysis. For this purpose, a thermo-fluid model with discrete powder particles is developed. The powder distribution, obtained from a discrete element method (DEM), is incorporated into the computational domain to develop a 3D process physics model using flow-3d. The melt pool formation during the conduction mode and the keyhole mode of melting has been discerned and explained. The high energy density leads to the formation of a vapor column and consequently pores under the laser scan track. Further, the keyhole shape resulted from different laser powers and scan speeds is investigated. The numerical results indicated that the keyhole size increases with the increase in the laser power even with the same energy density. The keyhole becomes stable at a higher power, which may reduce the occurrence of pores during laser scanning.

Keywords: additive manufacturing, keyhole, laser powder bed fusion, porosity

Fig. 1 (a) Powder added to the dispenser platform and (b) powder particles settled over build plate after the recoating process
Fig. 1 (a) Powder added to the dispenser platform and (b) powder particles settled over build plate after the recoating process
Fig. 2 3D computational domain used for single-track simulation
Fig. 2 3D computational domain used for single-track simulation
Fig. 3 Temperature-dependent material properties of Ti-6Al-4V
Fig. 3 Temperature-dependent material properties of Ti-6Al-4V
Fig. 4 Powder and substrate melting during laser application
Fig. 4 Powder and substrate melting during laser application
Fig. 5 Melt region formed after complete melting and solidification
Fig. 5 Melt region formed after complete melting and solidification
Fig. 6 Melt pool boundary comparison between the experiment [25] and the simulation
Fig. 6 Melt pool boundary comparison between the experiment [25] and the simulation
Fig. 7 Equilibrium points during the formation of vapor column [27]
Fig. 7 Equilibrium points during the formation of vapor column [27]
Fig. 8 Multiple reflection vectors from the keyhole wall
Fig. 8 Multiple reflection vectors from the keyhole wall
Fig. 9 (a) Velocity field, keyhole profile, and breakage of the keyhole to form bubble and (b) 2D temperature and velocity field along the longitudinal section
Fig. 9 (a) Velocity field, keyhole profile, and breakage of the keyhole to form bubble and (b) 2D temperature and velocity field along the longitudinal section
Fig. 10 Fluid flow in the transverse direction during keyhole melting
Fig. 10 Fluid flow in the transverse direction during keyhole melting
Fig. 11 Melt pool boundary compared with the experiment [21] for 195 W laser power and 400 mm/s scan speed
Fig. 11 Melt pool boundary compared with the experiment [21] for 195 W laser power and 400 mm/s scan speed
Fig. 12 Melt region formed after complete melting and solidification
Fig. 12 Melt region formed after complete melting and solidification
Fig. 13 2D images of the pores formed at the beginning of the single track and their 3D-rendered morphology
Fig. 13 2D images of the pores formed at the beginning of the single track and their 3D-rendered morphology
Fig. 14 Pore number and volume from a different level of power with LED = 0.4 J/mm [29]
Fig. 14 Pore number and volume from a different level of power with LED = 0.4 J/mm [29]
Fig. 15 Keyhole shape at different time steps from different parameters: (a) P = 100 W, v = 250 mm/s, (b) P = 200 W, v = 500 mm/s, (c) P = 300 W, v = 750 mm/s, and (d) P = 400 W, v = 1000 mm/s
Fig. 15 Keyhole shape at different time steps from different parameters: (a) P = 100 W, v = 250 mm/s, (b) P = 200 W, v = 500 mm/s, (c) P = 300 W, v = 750 mm/s, and (d) P = 400 W, v = 1000 mm/s
Fig. 16 Intensity dependence in the relationship between vapor column and evaporation pressure [27]
Fig. 16 Intensity dependence in the relationship between vapor column and evaporation pressure [27]
Fig. 17 Temperature distribution when laser has moved 0.8 mm with P = 300 W, v = 750 mm/s and P = 400 W, v = 1000 mm/s
Fig. 17 Temperature distribution when laser has moved 0.8 mm with P = 300 W, v = 750 mm/s and P = 400 W, v = 1000 mm/s
Fig. 18 Melt region with different level of power with LED of 0.4 J/mm
Fig. 18 Melt region with different level of power with LED of 0.4 J/mm

References

[1] Bauereiß, A., Scharowsky, T., and Körner, C., 2014, “Defect Generation and
Propagation Mechanism During Additive Manufacturing by Selective Beam
Melting,” J. Mater. Process. Technol., 214(11), pp. 2522–2528.
[2] Gong, H., Rafi, K., Gu, H., Starr, T., and Stucker, B., 2014, “Analysis of Defect
Generation in Ti–6Al–4V Parts Made Using Powder Bed Fusion Additive
Manufacturing Processes,” Add. Manuf., 1(2014), pp. 87–98.
[3] Wang, Y., Kamath, C., Voisin, T., and Li, Z., 2018, “A Processing Diagram for
High-Density Ti-6Al-4V by Selective Laser Melting,” Rapid Prototyping J., 24
(9), pp. 1469–1478.
[4] Khairallah, S. A., and Anderson, A., 2014, “Mesoscopic Simulation Model of
Selective Laser Melting of Stainless Steel Powder,” J. Mater. Process. Technol.,
214(11), pp. 2627–2636.
[5] Yadroitsev, I., Gusarov, A., Yadroitsava, I., and Smurov, I., 2010, “Single Track
Formation in Selective Laser Melting of Metal Powders,” J. Mater. Process.
Technol., 210(12), pp. 1624–1631.
[6] Xia, M., Gu, D., Yu, G., Dai, D., Chen, H., and Shi, Q., 2016, “Influence of Hatch
Spacing on Heat and Mass Transfer, Thermodynamics and Laser Processability
During Additive Manufacturing of Inconel 718 Alloy,” Int. J. Mach. Tools
Manuf., 109(2016), pp. 147–157.
[7] Lee, Y., and Zhang, W., 2016, “Modeling of Heat Transfer, Fluid Flow and
Solidification Microstructure of Nickel-Base Superalloy Fabricated by Laser
Powder bed Fusion,” Add. Manuf., 12(2016), pp. 178–188.
[8] Wu, Y.-C., San, C.-H., Chang, C.-H., Lin, H.-J., Marwan, R., Baba, S., and
Hwang, W.-S., 2018, “Numerical Modeling of Melt-Pool Behavior in Selective
Laser Melting with Random Powder Distribution and Experimental
Validation,” J. Mater. Process. Technol., 254(2018), pp. 72–78.
[9] Khairallah, S. A., Anderson, A. T., Rubenchik, A., and King, W. E., 2016, “Laser
Powder-bed Fusion Additive Manufacturing: Physics of Complex Melt Flow and
Formation Mechanisms of Pores, Spatter, and Denudation Zones,” Acta
Materialia, 108(2016), pp. 36–45.
[10] Tan, J., Tang, C., and Wong, C., 2018, “A Computational Study on Porosity
Evolution in Parts Produced by Selective Laser Melting,” Metall. Mater. Trans.
A, 49A(8), pp. 3663–3673.
[11] Leitz, K.-H., Singer, P., Plankensteiner, A., Tabernig, B., Kestler, H., and Sigl,
L. J. M. P. R., 2017, “Multi-Physical Simulation of Selective Laser Melting,”
Metal Powder Report, 72(5), pp. 331–338.
[12] Zhao, C., Fezzaa, K., Cunningham, R. W., Wen, H., Carlo, F., Chen, L., Rollett,
A. D., and Sun, T., 2017, “Real-time Monitoring of Laser Powder Bed Fusion
Process Using High-Speed X-ray Imaging and Diffraction,” Sci. Rep., 7(1),
p. 3602.
[13] Parab, N. D., Zhao, C., Cunningham, R., Escano, L. I., Fezzaa, K., Everhart, W.,
Rollett, A. D., Chen, L., and Sun, T., 2018, “Ultrafast X-ray Imaging of Laser–
Metal Additive Manufacturing Processes,” J. Synchrotron Radiat., 25(5),
pp. 1467–1477.
[14] Cunningham, R., Zhao, C., Parab, N., Kantzos, C., Pauza, J., Fezzaa, K., Sun, T.,
and Rollett, A. D., 2019, “Keyhole Threshold and Morphology in Laser Melting
Revealed by Ultrahigh-Speed X-Ray Imaging,” Science, 363(6429), pp. 849–852.
[15] Shrestha, S., Starr, T., and Chou, K., 2019, “A Study of Keyhole Porosity in
Selective Laser Melting: Single Track Scanning With Micro-CT Analysis,”
ASME J. Manuf. Sci. Eng., 141(7), pp. 1–23.
[16] Ye, J., Rubenchik, A. M., Crumb, M. F., Guss, G., and Matthews, M. J., 2018,
“Laser Absorption and Scaling Behavior in Powder Bed Fusion Additive
Manufacturing of Metals,” Proceedings of the CLEO: Science and Innovations,
Optical Society of America, San Jose, CA, May 13–18, Optical Society of
America, p. JW2A.117.
[17] Mishra, B., and Rajamani, R. K., 1992, “The Discrete Element Method for the
Simulation of Ball Mills,” Appl. Math. Modell., 16(11), pp. 598–604.
[18] Yan, W., Qian, Y., Ge, W., Lin, S., Liu, W. K., Lin, F., and Wagner, G. J., 2018,
“Meso-Scale Modeling of Multiple-Layer Fabrication Process in Selective
Electron Beam Melting: Inter-Layer/Track Voids Formation,” Materials and
Design, 141(2018), pp. 210–219.
[19] Kloss, C., Goniva, C., Hager, A., Amberger, S., and Pirker, S., 2012, “Models,
Algorithms and Validation for Opensource DEM and CFD–DEM,” Prog.
Comput. Fluid Dynam. Int. J., 12(2–3), pp. 140–152.
[20] Escano, L. I., Parab, N. D., Xiong, L., Guo, Q., Zhao, C., Fezzaa, K., Everhart,
W., Sun, T., and Chen, L., 2018, “Revealing Particle-Scale Powder Spreading
Dynamics in Powder-Bed-Based Additive Manufacturing Process by
High-Speed X-Ray Imaging,” Sci. Rep., 8(1), p. 15079.
[21] Gong, H., Gu, H., Zeng, K., Dilip, J., Pal, D., Stucker, B., Christiansen, D., Beuth,
J., and Lewandowski, J. J., 2014, “Melt Pool Characterization for Selective Laser
Melting of Ti-6Al-4V Pre-Alloyed Powder,” Proceedings of the Solid Freeform
Fabrication Symposium, Austin, TX, Aug. 4–6, pp. 256–267.
[22] Mills, K. C., 2002, Recommended Values of Thermophysical Properties for
Selected Commercial Alloys, Woodhead Publishing, Cambridge, UK.
[23] Shrestha, S., and Chou, K., 2017, “A Build Surface Study of Powder-Bed
Electron Beam Additive Manufacturing by 3D Thermo-Fluid Simulation and
White-Light Interferometry,” Int. J. Mach. Tools Manuf., 121(2017), pp. 37–49.
[24] Cho, J.-H., and Na, S.-J., 2006, “Implementation of Real-Time Multiple
Reflection and Fresnel Absorption of Laser Beam in Keyhole,” J. Phys. D:
Appl. Phys., 39(24), p. 5372.
[25] Dilip, J., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., and Stucker, B.,
2017, “Influence of Processing Parameters on the Evolution of Melt Pool,
Porosity, and Microstructures in Ti-6Al-4V Alloy Parts Fabricated by Selective
Laser Melting,” Prog. Add. Manuf., 2(3), pp. 157–167.
[26] Bertoli, U. S., Wolfer, A. J., Matthews, M. J., Delplanque, J.-P. R., and Schoenung,
J. M., 2017, “On the Limitations of Volumetric Energy Density as a Design
Parameter for Selective Laser Melting,” Mater. Des., 113(2017), pp. 331–340.
[27] Kroos, J., Gratzke, U., and Simon, G., 1993, “Towards a Self-Consistent Model of
the Keyhole in Penetration Laser Beam Welding,” J. Phys. D: Appl. Phys., 26(3),
p. 474.
[28] Martin, A., Calta, N., Hammons, J., Khairallah, S., Nielsen, M., Shuttlesworth, R.,
Sinclair, N., Matthews, M., Jeffries, J., and Willey, T., 2019, “Ultrafast Dynamics
of Laser-Metal Interactions in Additive Manufacturing Alloys Captured by In Situ
X-Ray Imaging,” Mater. Today Adv., 1(2019), p. 100002.
[29] Shrestha, S., Starr, T., and Chou, K., 2018, “Individual and coupled contributions
of laser power and scanning speed towards process-induced porosity in selective
laser melting,” Proceedings of the Solid Freeform Fabrication Symposium,
Austin, TX, Aug. 13–15, pp. 1400–1409.
[30] Hann, D., Iammi, J., and Folkes, J., 2011, “A Simple Methodology for Predicting
Laser-Weld Properties From Material and Laser Parameters,” J. Phys. D: Appl.
Phys., 44(44), p. 445401.
[31] Trapp, J., Rubenchik, A. M., Guss, G., and Matthews, M. J., 2017, “In Situ
Absorptivity Measurements of Metallic Powders During Laser Powder-bed
Fusion Additive Manufacturing,” Appl. Mat. Today, 9(2017), pp. 341–349.

Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.

Flow-induced Shear Stress Confers Resistance to Carboplatin in an Adherent Three-Dimensional Model for Ovarian Cancer: A Role for EGFR-Targeted Photoimmunotherapy Informed by Physical Stress

난소암에 대한 일관된 3차원 모델에서 카보플라틴에 대한 유동에 의한 전단응력변화에 관한 연구

Abstract

A key reason for the persistently grim statistics associated with metastatic ovarian cancer is resistance to conventional agents, including platinum-based chemotherapies. A major source of treatment failure is the high degree of genetic and molecular heterogeneity, which results from significant underlying genomic instability, as well as stromal and physical cues in the microenvironment. Ovarian cancer commonly disseminates via transcoelomic routes to distant sites, which is associated with the frequent production of malignant ascites, as well as the poorest prognosis. In addition to providing a cell and protein-rich environment for cancer growth and progression, ascitic fluid also confers physical stress on tumors. An understudied area in ovarian cancer research is the impact of fluid shear stress on treatment failure. Here, we investigate the effect of fluid shear stress on response to platinum-based chemotherapy and the modulation of molecular pathways associated with aggressive disease in a perfusion model for adherent 3D ovarian cancer nodules. Resistance to carboplatin is observed under flow with a concomitant increase in the expression and activation of the epidermal growth factor receptor (EGFR) as well as downstream signaling members mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK) and extracellular signal-regulated kinase (ERK). The uptake of platinum by the 3D ovarian cancer nodules was significantly higher in flow cultures compared to static cultures. A downregulation of phospho-focal adhesion kinase (p-FAK), vinculin, and phospho-paxillin was observed following carboplatin treatment in both flow and static cultures. Interestingly, low-dose anti-EGFR photoimmunotherapy (PIT), a targeted photochemical modality, was found to be equally effective in ovarian tumors grown under flow and static conditions. These findings highlight the need to further develop PIT-based combinations that target the EGFR, and sensitize ovarian cancers to chemotherapy in the context of flow-induced shear stress.

전이성 난소 암과 관련된 지속적으로 암울한 통계의 주요 이유는 백금 기반 화학 요법을 포함한 기존 약제에 대한 내성 때문입니다. 치료 실패의 주요 원인은 높은 수준의 유전적 및 분자적 이질성이며, 이는 중요한 기본 게놈 불안정성과 미세 환경의 기질 및 물리적 단서로 인해 발생합니다.

난소 암은 흔히 transcoelomic 경로를 통해 먼 부위로 전파되며, 이는 악성 복수의 빈번한 생산과 가장 나쁜 예후와 관련이 있습니다. 암 성장 및 진행을위한 세포 및 단백질이 풍부한 환경을 제공하는 것 외에도 복수 액은 종양에 물리적 스트레스를 부여합니다. 난소 암 연구에서 잘 연구되지 않은 분야는 유체 전단 응력이 치료 실패에 미치는 영향입니다.

여기, 우리는 백금 기반 화학 요법에 대한 반응과 부착 3D 난소 암 결절에 대한 관류 모델에서 공격적인 질병과 관련된 분자 경로의 변조에 대한 유체 전단 응력의 효과를 조사합니다.

카르보플라틴에 대한 내성은 상피 성장 인자 수용체 (EGFR)의 발현 및 활성화의 수반되는 증가 뿐만 아니라 다운 스트림 신호 구성원인 미토겐 활성화 단백질 키나제/세포 외 신호 조절 키나제 (MEK) 및 세포 외 신호 조절과 함께 관찰됩니다. 키나아제 (ERK). 3D 난소 암 결절에 의한 백금 흡수는 정적 배양에 비해 유동 배양에서 상당히 높았습니다.

포스 포-포컬 접착 키나제 (p-FAK), 빈 쿨린 및 포스 포-팍 실린의 하향 조절은 유동 및 정적 배양 모두에서 카보 플 라틴 처리 후 관찰되었습니다. 흥미롭게도, 표적 광 화학적 양식 인 저용량 항 EGFR 광 면역 요법 (PIT)은 유동 및 정적 조건에서 성장한 난소 종양에서 똑같이 효과적인 것으로 밝혀졌습니다.

이러한 발견은 EGFR을 표적으로하는 PIT 기반 조합을 추가로 개발하고 흐름 유도 전단 응력의 맥락에서 화학 요법에 난소 암을 민감하게 할 필요성을 강조합니다.

Keywords: ovarian cancer, epidermal growth factor receptor (EGFR), mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK), extracellular signal-regulated kinase (ERK), chemoresistance, fluid shear stress, ascites, perfusion model, photoimmunotherapy (PIT), photodynamic therapy (PDT), carboplatin

Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.
Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.
Figure 2 (A) Geometry of the micronodule located at the center of the microchannel. The flow velocity is in the X-direction. The nodule is modeled as an ellipse with a semi-minor axis of 40 μm in the Z-direction. The semi-major axis varies from 40-100 μm in the X-direction. The section over which the fluid dynamics are studied is the middle part of the channel with dimensions 4 mm along the Y-axis and 250 μm along the Z-axis. The nodule is located at (0, 20 μm). The black dotted line shows the centerline of the largest nodule. (B) Shear stress distribution over the surface of the solid micro-nodule on the XZ-plane. (C) Shear stress distribution over the surface of the porous micro-nodule on the XZ-plane. (D) Flow flux distribution over the centerline of the porous micro-nodule on the XZ-plane. The flux enters the surface at the left and leaves at the right.
Figure 2 (A) Geometry of the micronodule located at the center of the microchannel. The flow velocity is in the X-direction. The nodule is modeled as an ellipse with a semi-minor axis of 40 μm in the Z-direction. The semi-major axis varies from 40-100 μm in the X-direction. The section over which the fluid dynamics are studied is the middle part of the channel with dimensions 4 mm along the Y-axis and 250 μm along the Z-axis. The nodule is located at (0, 20 μm). The black dotted line shows the centerline of the largest nodule. (B) Shear stress distribution over the surface of the solid micro-nodule on the XZ-plane. (C) Shear stress distribution over the surface of the porous micro-nodule on the XZ-plane. (D) Flow flux distribution over the centerline of the porous micro-nodule on the XZ-plane. The flux enters the surface at the left and leaves at the right.
Figure 3 Cytotoxic response in carboplatin-treated 3D OVCAR-5 cultures under static conditions. (A) Representative confocal images of 3D tumors treated with carboplatin (0-500 μM) for 96 h showing a dose-dependent reduction in viable tumor (calcein signal). (B) Image-based quantification of normalized viable tumor area in 3D OVCAR-5 cultures following treatment with increasing doses of carboplatin. A minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was applied to the fluorescence images for quantitative analysis of the normalized viable tumor area. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; * p < 0.05; *** p < 0.001; N = 9) (C) Inductively coupled plasma mass spectrometry (ICP-MS)-based quantification of carboplatin uptake in static 3D OVCAR-5 tumors shows a dose-dependent increase in platinum levels, up to 9774 ± 3,052 ng/mg protein at an incubation concentration of 500 μM carboplatin. (One-way ANOVA with Dunn’s multiple comparisons test; n.s., not significant; * p < 0.05; ** p < 0.01; N = 3). Results are expressed as mean ± standard error of mean (SEM). Scale bars: 500 μm.
Figure 3 Cytotoxic response in carboplatin-treated 3D OVCAR-5 cultures under static conditions. (A) Representative confocal images of 3D tumors treated with carboplatin (0-500 μM) for 96 h showing a dose-dependent reduction in viable tumor (calcein signal). (B) Image-based quantification of normalized viable tumor area in 3D OVCAR-5 cultures following treatment with increasing doses of carboplatin. A minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was applied to the fluorescence images for quantitative analysis of the normalized viable tumor area. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; * p < 0.05; *** p < 0.001; N = 9) (C) Inductively coupled plasma mass spectrometry (ICP-MS)-based quantification of carboplatin uptake in static 3D OVCAR-5 tumors shows a dose-dependent increase in platinum levels, up to 9774 ± 3,052 ng/mg protein at an incubation concentration of 500 μM carboplatin. (One-way ANOVA with Dunn’s multiple comparisons test; n.s., not significant; * p < 0.05; ** p < 0.01; N = 3). Results are expressed as mean ± standard error of mean (SEM). Scale bars: 500 μm.
Figure 4 flow-induced chemo-resistance
Figure 4 flow-induced chemo-resistance
Figure 5 The effects of flow-induced shear stress on 3D ovarian cancer biology. (A) Western blot analysis of OVCAR-5 tumors was performed 7 days after culture under static or flow conditions. A flow-induced increase in EGFR and p-ERK, compared to static cultures, was observed. Conversely, a reduction in p-FAK, p-Paxillin, and Vinculin was observed under flow, relative to static conditions. (B) Western blot analysis of 3D OVCAR-5 tumors was performed 11 days after culture under static or flow conditions, including 4 days of treatment with 500 µM carboplatin, and respective controls. In both static and flow 3D cultures, carboplatin treatment resulted in downregulation of EGFR, FAK, p-Paxillin, Paxillin, and Vinculin. Upregulation of p-ERK was observed after carboplatin treatment in both static and flow 3D cultures. (C) Baseline levels of EGFR activity and expression are maintained by a complex array of factors, including recycling and degradation of the activated receptor complex. Flow-induced shear stress has been shown to cause a posttranslational up-regulation of EGFR expression and activation, likely resulting from increased receptor recycling and decreased EGFR degradation. Activation of EGFR results in ERK phosphorylation to induce gene expression, ultimately leading to cell proliferation, survival, and chemoresistance. FAK and other tyrosine kinases are activated by the engagement of integrins with the ECM. Subsequent phosphorylation of paxillin by FAK not only influences the remodeling of the actin cytoskeleton, but also modulates vinculin activation to regulate mitogen-activated protein kinase (MAPK) cascades, thereby stimulating pro-survival gene expression.
Figure 5 The effects of flow-induced shear stress on 3D ovarian cancer biology. (A) Western blot analysis of OVCAR-5 tumors was performed 7 days after culture under static or flow conditions. A flow-induced increase in EGFR and p-ERK, compared to static cultures, was observed. Conversely, a reduction in p-FAK, p-Paxillin, and Vinculin was observed under flow, relative to static conditions. (B) Western blot analysis of 3D OVCAR-5 tumors was performed 11 days after culture under static or flow conditions, including 4 days of treatment with 500 µM carboplatin, and respective controls. In both static and flow 3D cultures, carboplatin treatment resulted in downregulation of EGFR, FAK, p-Paxillin, Paxillin, and Vinculin. Upregulation of p-ERK was observed after carboplatin treatment in both static and flow 3D cultures. (C) Baseline levels of EGFR activity and expression are maintained by a complex array of factors, including recycling and degradation of the activated receptor complex. Flow-induced shear stress has been shown to cause a posttranslational up-regulation of EGFR expression and activation, likely resulting from increased receptor recycling and decreased EGFR degradation. Activation of EGFR results in ERK phosphorylation to induce gene expression, ultimately leading to cell proliferation, survival, and chemoresistance. FAK and other tyrosine kinases are activated by the engagement of integrins with the ECM. Subsequent phosphorylation of paxillin by FAK not only influences the remodeling of the actin cytoskeleton, but also modulates vinculin activation to regulate mitogen-activated protein kinase (MAPK) cascades, thereby stimulating pro-survival gene expression.
Figure 6 PIT efficacy in 3D tumors. (A) Dose-dependent change in normalized viable tumor area in static 3D cultures treated with PIC (1 μM BPD equivalent) and increasing energy densities (10–50 J/cm2 @ 50 mW/cm2). Significant tumoricidal efficacy is observed in a light-dose-dependent manner, starting at 15 J/cm2. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; ** p < 0.01, *** p < 0.001, N = 9) (B) Comparison of cytotoxic response in PIT-treated 3D cultures under static and flow conditions. For quantitative analysis of fluorescence images, a minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was used to establish normalized viable tumor area. PIT is equally effective in 3D tumors grown in static cultures (green) and under flow-induced shear stress (blue) (in contrast to flow-induced chemo-resistance shown in Figure 4) (Two-tailed t test; n.s., not significant; N = 9).
Figure 6 PIT efficacy in 3D tumors. (A) Dose-dependent change in normalized viable tumor area in static 3D cultures treated with PIC (1 μM BPD equivalent) and increasing energy densities (10–50 J/cm2 @ 50 mW/cm2). Significant tumoricidal efficacy is observed in a light-dose-dependent manner, starting at 15 J/cm2. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; ** p < 0.01, *** p < 0.001, N = 9) (B) Comparison of cytotoxic response in PIT-treated 3D cultures under static and flow conditions. For quantitative analysis of fluorescence images, a minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was used to establish normalized viable tumor area. PIT is equally effective in 3D tumors grown in static cultures (green) and under flow-induced shear stress (blue) (in contrast to flow-induced chemo-resistance shown in Figure 4) (Two-tailed t test; n.s., not significant; N = 9).

References

  1. Siegel R.L., Miller K.D., Jemal A. Cancer statistics, 2019. CA Cancer J. Clin. 2019;69:7–34. doi: 10.3322/caac.21551. [PubMed] [CrossRef] [Google Scholar]
  2. Foley O.W., Rauh-Hain J.A., Del Carmen M.G. Recurrent epithelial ovarian cancer: An update on treatment. Oncology. 2013;27:288–294, 298. [PubMed] [Google Scholar]
  3. Kipps E., Tan D.S., Kaye S.B. Meeting the challenge of ascites in ovarian cancer: New avenues for therapy and research. Nat. Rev. Cancer. 2013;13:273–282. doi: 10.1038/nrc3432. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  4. Tan D.S., Agarwal R., Kaye S.B. Mechanisms of transcoelomic metastasis in ovarian cancer. Lancet Oncol. 2006;7:925–934. doi: 10.1016/S1470-2045(06)70939-1. [PubMed] [CrossRef] [Google Scholar]
  5. Ahmed N., Stenvers K.L. Getting to know ovarian cancer ascites: Opportunities for targeted therapy-based translational research. Front. Oncol. 2013;3:256. doi: 10.3389/fonc.2013.00256. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  6. Shield K., Ackland M.L., Ahmed N., Rice G.E. Multicellular spheroids in ovarian cancer metastases: Biology and pathology. Gynecol. Oncol. 2009;113:143–148. doi: 10.1016/j.ygyno.2008.11.032. [PubMed] [CrossRef] [Google Scholar]
  7. Naora H., Montell D.J. Ovarian cancer metastasis: Integrating insights from disparate model organisms. Nat. Rev. Cancer. 2005;5:355–366. doi: 10.1038/nrc1611. [PubMed] [CrossRef] [Google Scholar]
  8. Lengyel E. Ovarian cancer development and metastasis. Am. J. Pathol. 2010;177:1053–1064. doi: 10.2353/ajpath.2010.100105. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  9. Javellana M., Hoppenot C., Lengyel E. The road to long-term survival: Surgical approach and longitudinal treatments of long-term survivors of advanced-stage serous ovarian cancer. Gynecol. Oncol. 2019;152:228–234. doi: 10.1016/j.ygyno.2018.11.007. [PubMed] [CrossRef] [Google Scholar]
  10. Al Habyan S., Kalos C., Szymborski J., McCaffrey L. Multicellular detachment generates metastatic spheroids during intra-abdominal dissemination in epithelial ovarian cancer. Oncogene. 2018;37:5127–5135. doi: 10.1038/s41388-018-0317-x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  11. Kim S., Kim B., Song Y.S. Ascites modulates cancer cell behavior, contributing to tumor heterogeneity in ovarian cancer. Cancer Sci. 2016;107:1173–1178. doi: 10.1111/cas.12987. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  12. Bowtell D.D., Bohm S., Ahmed A.A., Aspuria P.J., Bast R.C., Beral V., Berek J.S., Birrer M.J., Blagden S., Bookman M.A., et al. Rethinking ovarian cancer II: Reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer. 2015;15:668–679. doi: 10.1038/nrc4019. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  13. Hoppenot C., Eckert M.A., Tienda S.M., Lengyel E. Who are the long-term survivors of high grade serous ovarian cancer? Gynecol. Oncol. 2018;148:204–212. doi: 10.1016/j.ygyno.2017.10.032. [PubMed] [CrossRef] [Google Scholar]
  14. Zhao Y., Cao J., Melamed A., Worley M., Gockley A., Jones D., Nia H.T., Zhang Y., Stylianopoulos T., Kumar A.S., et al. Losartan treatment enhances chemotherapy efficacy and reduces ascites in ovarian cancer models by normalizing the tumor stroma. Proc. Natl. Acad. Sci. USA. 2019;116:2210–2219. doi: 10.1073/pnas.1818357116. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  15. Ayantunde A.A., Parsons S.L. Pattern and prognostic factors in patients with malignant ascites: A retrospective study. Ann. Oncol. 2007;18:945–949. doi: 10.1093/annonc/mdl499. [PubMed] [CrossRef] [Google Scholar]
  16. Latifi A., Luwor R.B., Bilandzic M., Nazaretian S., Stenvers K., Pyman J., Zhu H., Thompson E.W., Quinn M.A., Findlay J.K., et al. Isolation and characterization of tumor cells from the ascites of ovarian cancer patients: Molecular phenotype of chemoresistant ovarian tumors. PLoS ONE. 2012;7:e46858. doi: 10.1371/journal.pone.0046858. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  17. Ahmed N., Greening D., Samardzija C., Escalona R.M., Chen M., Findlay J.K., Kannourakis G. Unique proteome signature of post-chemotherapy ovarian cancer ascites-derived tumor cells. Sci. Rep. 2016;6:30061. doi: 10.1038/srep30061. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  18. Gjorevski N., Boghaert E., Nelson C.M. Regulation of Epithelial-Mesenchymal Transition by Transmission of Mechanical Stress through Epithelial Tissues. Cancer Microenviron. 2012;5:29–38. doi: 10.1007/s12307-011-0076-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  19. Polacheck W.J., Charest J.L., Kamm R.D. Interstitial flow influences direction of tumor cell migration through competing mechanisms. Proc. Natl. Acad. Sci. USA. 2011;108:11115–11120. doi: 10.1073/pnas.1103581108. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  20. Polacheck W.J., German A.E., Mammoto A., Ingber D.E., Kamm R.D. Mechanotransduction of fluid stresses governs 3D cell migration. Proc. Natl. Acad. Sci. USA. 2014;111:2447–2452. doi: 10.1073/pnas.1316848111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  21. Polacheck W.J., Zervantonakis I.K., Kamm R.D. Tumor cell migration in complex microenvironments. Cell Mol. Life Sci. 2013;70:1335–1356. doi: 10.1007/s00018-012-1115-1. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  22. Swartz M.A., Lund A.W. Lymphatic and interstitial flow in the tumour microenvironment: Linking mechanobiology with immunity. Nat. Rev. Cancer. 2012;12:210–219. doi: 10.1038/nrc3186. [PubMed] [CrossRef] [Google Scholar]
  23. Pisano M., Triacca V., Barbee K.A., Swartz M.A. An in vitro model of the tumor-lymphatic microenvironment with simultaneous transendothelial and luminal flows reveals mechanisms of flow enhanced invasion. Integr. Biol. 2015;7:525–533. doi: 10.1039/C5IB00085H. [PubMed] [CrossRef] [Google Scholar]
  24. Follain G., Herrmann D., Harlepp S., Hyenne V., Osmani N., Warren S.C., Timpson P., Goetz J.G. Fluids and their mechanics in tumour transit: Shaping metastasis. Nat. Rev. Cancer. 2020;20:107–124. doi: 10.1038/s41568-019-0221-x. [PubMed] [CrossRef] [Google Scholar]
  25. Rizvi I., Gurkan U.A., Tasoglu S., Alagic N., Celli J.P., Mensah L.B., Mai Z., Demirci U., Hasan T. Flow induces epithelial-mesenchymal transition, cellular heterogeneity and biomarker modulation in 3D ovarian cancer nodules. Proc. Natl. Acad. Sci. USA. 2013;110:E1974–E1983. doi: 10.1073/pnas.1216989110. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  26. Novak C., Horst E., Mehta G. Mechanotransduction in ovarian cancer: Shearing into the unknown. APL Bioeng. 2018;2 doi: 10.1063/1.5024386. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  27. Carmignani C.P., Sugarbaker T.A., Bromley C.M., Sugarbaker P.H. Intraperitoneal cancer dissemination: Mechanisms of the patterns of spread. Cancer Metastasis Rev. 2003;22:465–472. doi: 10.1023/A:1023791229361. [PubMed] [CrossRef] [Google Scholar]
  28. Sugarbaker P.H. Observations concerning cancer spread within the peritoneal cavity and concepts supporting an ordered pathophysiology. Cancer Treatment Res. 1996;82:79–100. [PubMed] [Google Scholar]
  29. Feki A., Berardi P., Bellingan G., Major A., Krause K.H., Petignat P., Zehra R., Pervaiz S., Irminger-Finger I. Dissemination of intraperitoneal ovarian cancer: Discussion of mechanisms and demonstration of lymphatic spreading in ovarian cancer model. Crit. Rev. Oncol./Hematol. 2009;72:1–9. doi: 10.1016/j.critrevonc.2008.12.003. [PubMed] [CrossRef] [Google Scholar]
  30. Holm-Nielsen P. Pathogenesis of ascites in peritoneal carcinomatosis. Acta Pathol. Microbiol. Scand. 1953;33:10–21. doi: 10.1111/j.1699-0463.1953.tb04805.x. [PubMed] [CrossRef] [Google Scholar]
  31. Ahmed N., Riley C., Oliva K., Rice G., Quinn M. Ascites induces modulation of alpha6beta1 integrin and urokinase plasminogen activator receptor expression and associated functions in ovarian carcinoma. Br. J. Cancer. 2005;92:1475–1485. doi: 10.1038/sj.bjc.6602495. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  32. Woodburn J.R. The epidermal growth factor receptor and its inhibition in cancer therapy. Pharmacol. Ther. 1999;82:241–250. doi: 10.1016/S0163-7258(98)00045-X. [PubMed] [CrossRef] [Google Scholar]
  33. Servidei T., Riccardi A., Mozzetti S., Ferlini C., Riccardi R. Chemoresistant tumor cell lines display altered epidermal growth factor receptor and HER3 signaling and enhanced sensitivity to gefitinib. Int. J. Cancer J. Int. Cancer. 2008;123:2939–2949. doi: 10.1002/ijc.23902. [PubMed] [CrossRef] [Google Scholar]
  34. Chen A.P., Zhang J., Liu H., Zhao S.P., Dai S.Z., Sun X.L. Association of EGFR expression with angiogenesis and chemoresistance in ovarian carcinoma. Zhonghua zhong liu za zhi [Chinese journal of oncology] 2009;31:48–52. [PubMed] [Google Scholar]
  35. Alper O., Bergmann-Leitner E.S., Bennett T.A., Hacker N.F., Stromberg K., Stetler-Stevenson W.G. Epidermal growth factor receptor signaling and the invasive phenotype of ovarian carcinoma cells. J. Natl. Cancer Inst. 2001;93:1375–1384. doi: 10.1093/jnci/93.18.1375. [PubMed] [CrossRef] [Google Scholar]
  36. Zeineldin R., Muller C.Y., Stack M.S., Hudson L.G. Targeting the EGF receptor for ovarian cancer therapy. J. Oncol. 2010;2010:414676. doi: 10.1155/2010/414676. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  37. Alper O., De Santis M.L., Stromberg K., Hacker N.F., Cho-Chung Y.S., Salomon D.S. Anti-sense suppression of epidermal growth factor receptor expression alters cellular proliferation, cell-adhesion and tumorigenicity in ovarian cancer cells. Int. J. Cancer. 2000;88:566–574. doi: 10.1002/1097-0215(20001115)88:4<566::AID-IJC8>3.0.CO;2-D. [PubMed] [CrossRef] [Google Scholar]
  38. Posadas E.M., Liel M.S., Kwitkowski V., Minasian L., Godwin A.K., Hussain M.M., Espina V., Wood B.J., Steinberg S.M., Kohn E.C. A phase II and pharmacodynamic study of gefitinib in patients with refractory or recurrent epithelial ovarian cancer. Cancer. 2007;109:1323–1330. doi: 10.1002/cncr.22545. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  39. Psyrri A., Kassar M., Yu Z., Bamias A., Weinberger P.M., Markakis S., Kowalski D., Camp R.L., Rimm D.L., Dimopoulos M.A. Effect of epidermal growth factor receptor expression level on survival in patients with epithelial ovarian cancer. Clin. Cancer Res. 2005;11:8637–8643. doi: 10.1158/1078-0432.CCR-05-1436. [PubMed] [CrossRef] [Google Scholar]
  40. Dimou A., Agarwal S., Anagnostou V., Viray H., Christensen S., Gould Rothberg B., Zolota V., Syrigos K., Rimm D. Standardization of epidermal growth factor receptor (EGFR) measurement by quantitative immunofluorescence and impact on antibody-based mutation detection in non-small cell lung cancer. Am. J. Pathol. 2011;179:580–589. doi: 10.1016/j.ajpath.2011.04.031. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  41. Anagnostou V.K., Welsh A.W., Giltnane J.M., Siddiqui S., Liceaga C., Gustavson M., Syrigos K.N., Reiter J.L., Rimm D.L. Analytic variability in immunohistochemistry biomarker studies. Cancer Epidemiol Biomarkers Prev. 2010;19:982–991. doi: 10.1158/1055-9965.EPI-10-0097. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  42. Del Carmen M.G., Rizvi I., Chang Y., Moor A.C., Oliva E., Sherwood M., Pogue B., Hasan T. Synergism of epidermal growth factor receptor-targeted immunotherapy with photodynamic treatment of ovarian cancer in vivo. J. Natl. Cancer Inst. 2005;97:1516–1524. doi: 10.1093/jnci/dji314. [PubMed] [CrossRef] [Google Scholar]
  43. Armstrong D.K., Bundy B., Wenzel L., Huang H.Q., Baergen R., Lele S., Copeland L.J., Walker J.L., Burger R.A., Gynecologic Oncology G. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N. Engl. J. Med. 2006;354:34–43. doi: 10.1056/NEJMoa052985. [PubMed] [CrossRef] [Google Scholar]
  44. Verwaal V.J., Van Ruth S., De Bree E., Van Sloothen G.W., Van Tinteren H., Boot H., Zoetmulder F.A. Randomized trial of cytoreduction and hyperthermic intraperitoneal chemotherapy versus systemic chemotherapy and palliative surgery in patients with peritoneal carcinomatosis of colorectal cancer. J. Clin. Oncol. 2003;21:3737–3743. doi: 10.1200/JCO.2003.04.187. [PubMed] [CrossRef] [Google Scholar]
  45. Van Driel W.J., Koole S.N., Sikorska K., Schagen van Leeuwen J.H., Schreuder H.W.R., Hermans R.H.M., De Hingh I., Van der Velden J., Arts H.J., Massuger L., et al. Hyperthermic Intraperitoneal Chemotherapy in Ovarian Cancer. N. Engl. J. Med. 2018;378:230–240. doi: 10.1056/NEJMoa1708618. [PubMed] [CrossRef] [Google Scholar]
  46. Verwaal V.J., Bruin S., Boot H., Van Slooten G., Van Tinteren H. 8-year follow-up of randomized trial: Cytoreduction and hyperthermic intraperitoneal chemotherapy versus systemic chemotherapy in patients with peritoneal carcinomatosis of colorectal cancer. Ann. Surg. Oncol. 2008;15:2426–2432. doi: 10.1245/s10434-008-9966-2. [PubMed] [CrossRef] [Google Scholar]
  47. DeLaney T.F., Sindelar W.F., Tochner Z., Smith P.D., Friauf W.S., Thomas G., Dachowski L., Cole J.W., Steinberg S.M., Glatstein E. Phase I study of debulking surgery and photodynamic therapy for disseminated intraperitoneal tumors. Int. J. Radiat. Oncol. Biol. Phys. 1993;25:445–457. doi: 10.1016/0360-3016(93)90066-5. [PubMed] [CrossRef] [Google Scholar]
  48. Celli J.P., Spring B.Q., Rizvi I., Evans C.L., Samkoe K.S., Verma S., Pogue B.W., Hasan T. Imaging and photodynamic therapy: Mechanisms, monitoring, and optimization. Chem. Rev. 2010;110:2795–2838. doi: 10.1021/cr900300p. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  49. Spring B.Q., Rizvi I., Xu N., Hasan T. The role of photodynamic therapy in overcoming cancer drug resistance. Photochem. Photobiol. Sci. 2015;14:1476–1491. doi: 10.1039/C4PP00495G. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  50. Liang B.J., Pigula M., Baglo Y., Najafali D., Hasan T., Huang H.C. Breaking the Selectivity-Uptake Trade-Off of Photoimmunoconjugates with Nanoliposomal Irinotecan for Synergistic Multi-Tier Cancer Targeting. J. Nanobiotechnol. 2020;18:1. doi: 10.1186/s12951-019-0560-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  51. Huang H.C., Rizvi I., Liu J., Anbil S., Kalra A., Lee H., Baglo Y., Paz N., Hayden D., Pereira S., et al. Photodynamic Priming Mitigates Chemotherapeutic Selection Pressures and Improves Drug Delivery. Cancer Res. 2018;78:558–571. doi: 10.1158/0008-5472.CAN-17-1700. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  52. Huang H.C., Mallidi S., Liu J., Chiang C.T., Mai Z., Goldschmidt R., Ebrahim-Zadeh N., Rizvi I., Hasan T. Photodynamic Therapy Synergizes with Irinotecan to Overcome Compensatory Mechanisms and Improve Treatment Outcomes in Pancreatic Cancer. Cancer Res. 2016;76:1066–1077. doi: 10.1158/0008-5472.CAN-15-0391. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  53. Cengel K.A., Glatstein E., Hahn S.M. Intraperitoneal photodynamic therapy. Cancer Treat. Res. 2007;134:493–514. [PubMed] [Google Scholar]
  54. Obaid G., Broekgaarden M., Bulin A.-L., Huang H.-C., Kuriakose J., Liu J., Hasan T. Photonanomedicine: A convergence of photodynamic therapy and nanotechnology. Nanoscale. 2016;8:12471–12503. doi: 10.1039/C5NR08691D. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  55. Ogata F., Nagaya T., Nakamura Y., Sato K., Okuyama S., Maruoka Y., Choyke P.L., Kobayashi H. Near-infrared photoimmunotherapy: A comparison of light dosing schedules. Oncotarget. 2017;8:35069–35075. doi: 10.18632/oncotarget.17047. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  56. Mitsunaga M., Ogawa M., Kosaka N., Rosenblum L.T., Choyke P.L., Kobayashi H. Cancer cell-selective in vivo near infrared photoimmunotherapy targeting specific membrane molecules. Nat. Med. 2011;17:1685–1691. doi: 10.1038/nm.2554. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  57. Inglut C.T., Baglo Y., Liang B.J., Cheema Y., Stabile J., Woodworth G.F., Huang H.-C. Systematic Evaluation of Light-Activatable Biohybrids for Anti-Glioma Photodynamic Therapy. J. Clin. Med. 2019;8:1269. doi: 10.3390/jcm8091269. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  58. Huang H.C., Pigula M., Fang Y., Hasan T. Immobilization of Photo-Immunoconjugates on Nanoparticles Leads to Enhanced Light-Activated Biological Effects. Small. 2018:e1800236. doi: 10.1002/smll.201800236. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  59. Spring B.Q., Abu-Yousif A.O., Palanisami A., Rizvi I., Zheng X., Mai Z., Anbil S., Sears R.B., Mensah L.B., Goldschmidt R., et al. Selective treatment and monitoring of disseminated cancer micrometastases in vivo using dual-function, activatable immunoconjugates. Proc. Natl. Acad. Sci. USA. 2014;111:E933–E942. doi: 10.1073/pnas.1319493111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  60. Abu-Yousif A.O., Moor A.C., Zheng X., Savellano M.D., Yu W., Selbo P.K., Hasan T. Epidermal growth factor receptor-targeted photosensitizer selectively inhibits EGFR signaling and induces targeted phototoxicity in ovarian cancer cells. Cancer Lett. 2012;321:120–127. doi: 10.1016/j.canlet.2012.01.014. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  61. Rizvi I., Dinh T.A., Yu W., Chang Y., Sherwood M.E., Hasan T. Photoimmunotherapy and irradiance modulation reduce chemotherapy cycles and toxicity in a murine model for ovarian carcinomatosis: Perspective and results. Israel J. Chem. 2012;52:776–787. doi: 10.1002/ijch.201200016. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  62. Quirk B.J., Brandal G., Donlon S., Vera J.C., Mang T.S., Foy A.B., Lew S.M., Girotti A.W., Jogal S., LaViolette P.S., et al. Photodynamic therapy (PDT) for malignant brain tumors–where do we stand? Photodiagnosis Photodyn. Ther. 2015;12:530–544. doi: 10.1016/j.pdpdt.2015.04.009. [PubMed] [CrossRef] [Google Scholar]
  63. Eljamel M.S., Goodman C., Moseley H. ALA and Photofrin fluorescence-guided resection and repetitive PDT in glioblastoma multiforme: A single centre Phase III randomised controlled trial. Lasers Med. Sci. 2008;23:361–367. doi: 10.1007/s10103-007-0494-2. [PubMed] [CrossRef] [Google Scholar]
  64. Varma A.K., Muller P.J. Cranial neuropathies after intracranial Photofrin-photodynamic therapy for malignant supratentorial gliomas-a report on 3 cases. Surg. Neurol. 2008;70:190–193. doi: 10.1016/j.surneu.2007.01.060. [PubMed] [CrossRef] [Google Scholar]
  65. Akimoto J. Photodynamic Therapy for Malignant Brain Tumors. Neurol. Medico-Chirurgica. 2016;56:151–157. doi: 10.2176/nmc.ra.2015-0296. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  66. Kercher E.M., Nath S., Rizvi I., Spring B.Q. Cancer Cell-targeted and Activatable Photoimmunotherapy Spares T Cells in a 3D Coculture Model. Photochem. Photobiol. 2019 doi: 10.1111/php.13153. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  67. Savellano M.D., Hasan T. Targeting cells that overexpress the epidermal growth factor receptor with polyethylene glycolated BPD verteporfin photosensitizer immunoconjugates. Photochem. Photobiol. 2003;77:431–439. doi: 10.1562/0031-8655(2003)077<0431:TCTOTE>2.0.CO;2. [PubMed] [CrossRef] [Google Scholar]
  68. Molpus K.L., Hamblin M.R., Rizvi I., Hasan T. Intraperitoneal photoimmunotherapy of ovarian carcinoma xenografts in nude mice using charged photoimmunoconjugates. Gynecol. Oncol. 2000;76:397–404. doi: 10.1006/gyno.1999.5705. [PubMed] [CrossRef] [Google Scholar]
  69. Savellano M.D., Hasan T. Photochemical targeting of epidermal growth factor receptor: A mechanistic study. Clin. Cancer Res. 2005;11:1658–1668. doi: 10.1158/1078-0432.CCR-04-1902. [PubMed] [CrossRef] [Google Scholar]
  70. Nath S., Saad M.A., Pigula M., Swain J.W.R., Hasan T. Photoimmunotherapy of Ovarian Cancer: A Unique Niche in the Management of Advanced Disease. Cancers. 2019;11:1887. doi: 10.3390/cancers11121887. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  71. Calibasi Kocal G., Guven S., Foygel K., Goldman A., Chen P., Sengupta S., Paulmurugan R., Baskin Y., Demirci U. Dynamic Microenvironment Induces Phenotypic Plasticity of Esophageal Cancer Cells Under Flow. Sci. Rep. 2016;6:38221. doi: 10.1038/srep38221. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  72. Tasoglu S., Gurkan U.A., Wang S., Demirci U. Manipulating biological agents and cells in micro-scale volumes for applications in medicine. Chem. Soc. Rev. 2013;42:5788–5808. doi: 10.1039/c3cs60042d. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  73. Moon S., Gurkan U.A., Blander J., Fawzi W.W., Aboud S., Mugusi F., Kuritzkes D.R., Demirci U. Enumeration of CD4+ T-cells using a portable microchip count platform in Tanzanian HIV-infected patients. PLoS ONE. 2011;6:e21409. doi: 10.1371/journal.pone.0021409. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  74. White F.M. Fluid Mechanics. McGraw-Hill; Boston, MA, USA: 2011. [Google Scholar]
  75. Luo Q., Kuang D., Zhang B., Song G. Cell stiffness determined by atomic force microscopy and its correlation with cell motility. Biochim Biophys Acta. 2016;1860:1953–1960. doi: 10.1016/j.bbagen.2016.06.010. [PubMed] [CrossRef] [Google Scholar]
  76. Sarntinoranont M., Rooney F., Ferrari M. Interstitial Stress and Fluid Pressure Within a Growing Tumor. Ann. Biomed. Eng. 2003;31:327–335. doi: 10.1114/1.1554923. [PubMed] [CrossRef] [Google Scholar]
  77. Baxter L.T., Jain R.K. Transport of fluid and macromolecules in tumors. I. Role of interstitial pressure and convection. Microvasc. Res. 1989;37:77–104. doi: 10.1016/0026-2862(89)90074-5. [PubMed] [CrossRef] [Google Scholar]
  78. Malik R., Khan A.P., Asangani I.A., Cieślik M., Prensner J.R., Wang X., Iyer M.K., Jiang X., Borkin D., Escara-Wilke J., et al. Targeting the MLL complex in castration-resistant prostate cancer. Nat. Med. 2015;21:344. doi: 10.1038/nm.3830. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  79. Nath S., Christian L., Tan S.Y., Ki S., Ehrlich L.I., Poenie M. Dynein Separately Partners with NDE1 and Dynactin To Orchestrate T Cell Focused Secretion. J. Immunol. 2016;197:2090–2101. doi: 10.4049/jimmunol.1600180. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  80. Celli J.P., Rizvi I., Evans C.L., Abu-Yousif A.O., Hasan T. Quantitative imaging reveals heterogeneous growth dynamics and treatment-dependent residual tumor distributions in a three-dimensional ovarian cancer model. J. Biomed. Opt. 2010;15:051603. doi: 10.1117/1.3483903. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  81. Rizvi I., Celli J.P., Evans C.L., Abu-Yousif A.O., Muzikansky A., Pogue B.W., Finkelstein D., Hasan T. Synergistic Enhancement of Carboplatin Efficacy with Photodynamic Therapy in a Three-Dimensional Model for Micrometastatic Ovarian Cancer. Cancer Res. 2010;70:9319–9328. doi: 10.1158/0008-5472.CAN-10-1783. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  82. Glidden M.D., Celli J.P., Massodi I., Rizvi I., Pogue B.W., Hasan T. Image-Based Quantification of Benzoporphyrin Derivative Uptake, Localization, and Photobleaching in 3D Tumor Models, for Optimization of PDT Parameters. Theranostics. 2012;2:827–839. doi: 10.7150/thno.4334. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  83. Celli J.P., Rizvi I., Blanden A.R., Massodi I., Glidden M.D., Pogue B.W., Hasan T. An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models. Sci. Rep. 2014;4:3751. doi: 10.1038/srep03751. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  84. Bulin A.L., Broekgaarden M., Hasan T. Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids. Sci. Rep. 2017;7:16645. doi: 10.1038/s41598-017-16622-9. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  85. Rahmanzadeh R., Rai P., Celli J.P., Rizvi I., Baron-Luhr B., Gerdes J., Hasan T. Ki-67 as a molecular target for therapy in an in vitro three-dimensional model for ovarian cancer. Cancer Res. 2010;70:9234–9242. doi: 10.1158/0008-5472.CAN-10-1190. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  86. Anbil S., Rizvi I., Celli J.P., Alagic N., Pogue B.W., Hasan T. Impact of treatment response metrics on photodynamic therapy planning and outcomes in a three-dimensional model of ovarian cancer. J. Biomed. Opt. 2013;18:098004. doi: 10.1117/1.JBO.18.9.098004. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  87. Di Pasqua A.J., Goodisman J., Dabrowiak J.C. Understanding how the platinum anticancer drug carboplatin works: From the bottle to the cell. Inorg. Chim. Acta. 2012;389:29–35. doi: 10.1016/j.ica.2012.01.028. [CrossRef] [Google Scholar]
  88. Rabik C.A., Dolan M.E. Molecular mechanisms of resistance and toxicity associated with platinating agents. Cancer Treat. Rev. 2007;33:9–23. doi: 10.1016/j.ctrv.2006.09.006. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  89. Ozols R.F. Carboplatin and paclitaxel in ovarian cancer. Semin. Oncol. 1995;22:78–83. [PubMed] [Google Scholar]
  90. Neijt J.P., Lund B. Paclitaxel with carboplatin for the treatment of ovarian cancer. Semin. Oncol. 1996;23:2–4. [PubMed] [Google Scholar]
  91. Subauste C.M., Pertz O., Adamson E.D., Turner C.E., Junger S., Hahn K.M. Vinculin modulation of paxillin–FAK interactions regulates ERK to control survival and motility. J. Cell Biol. 2004;165:371–381. doi: 10.1083/jcb.200308011. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  92. Eke I., Cordes N. Focal adhesion signaling and therapy resistance in cancer. Semin. Cancer Biol. 2015;31:65–75. [PubMed] [Google Scholar]
  93. McCubrey J.A., Steelman L.S., Chappell W.H., Abrams S.L., Wong E.W., Chang F., Lehmann B., Terrian D.M., Milella M., Tafuri A., et al. Roles of the Raf/MEK/ERK pathway in cell growth, malignant transformation and drug resistance. Biochim. Biophys. Acta. 2007;1773:1263–1284. doi: 10.1016/j.bbamcr.2006.10.001. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  94. Duska L.R., Hamblin M.R., Miller J.L., Hasan T. Combination photoimmunotherapy and cisplatin: Effects on human ovarian cancer ex vivo. J. Natl. Cancer Inst. 1999;91:1557–1563. doi: 10.1093/jnci/91.18.1557. [PubMed] [CrossRef] [Google Scholar]
  95. Spring B., Mai Z., Rai P., Chang S., Hasan T. Theranostic nanocells for simultaneous imaging and photodynamic therapy of pancreatic cancer. Proc. SPIE. 2010;7551:755104. [Google Scholar]
  96. Kessel D., Oleinick N.L. Photodynamic therapy and cell death pathways. Methods Mol. Biol. 2010;635:35–46. doi: 10.1007/978-1-60761-697-9_3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  97. Van Dongen G.A., Visser G.W., Vrouenraets M.B. Photosensitizer-antibody conjugates for detection and therapy of cancer. Adv. Drug Deliv. Rev. 2004;56:31–52. doi: 10.1016/j.addr.2003.09.003. [PubMed] [CrossRef] [Google Scholar]
  98. Ayhan A., Gultekin M., Taskiran C., Dursun P., Firat P., Bozdag G., Celik N.Y., Yuce K. Ascites and epithelial ovarian cancers: A reappraisal with respect to different aspects. Int. J. Gynecol. Cancer. 2007;17:68–75. doi: 10.1111/j.1525-1438.2006.00777.x. [PubMed] [CrossRef] [Google Scholar]
  99. Shen-Gunther J., Mannel R.S. Ascites as a predictor of ovarian malignancy. Gynecol. Oncol. 2002;87:77–83. doi: 10.1006/gyno.2002.6800. [PubMed] [CrossRef] [Google Scholar]
  100. Pourgholami M.H., Ataie-Kachoie P., Badar S., Morris D.L. Minocycline inhibits malignant ascites of ovarian cancer through targeting multiple signaling pathways. Gynecol. Oncol. 2013;129:113–119. doi: 10.1016/j.ygyno.2012.12.031. [PubMed] [CrossRef] [Google Scholar]
  101. Shender V., Arapidi G., Butenko I., Anikanov N., Ivanova O., Govorun V. Peptidome profiling dataset of ovarian cancer and non-cancer proximal fluids: Ascites and blood sera. Data Brief. 2019;22:557–562. doi: 10.1016/j.dib.2018.12.056. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  102. Parsons S.L., Watson S.A., Steele R.J.C. Malignant ascites. Br. J. Surg. 1996;83:6–14. doi: 10.1002/bjs.1800830104. [PubMed] [CrossRef] [Google Scholar]
  103. Becker G., Galandi D., Blum H.E. Malignant ascites: Systematic review and guideline for treatment. Eur. J. Cancer. 2006;42:589–597. doi: 10.1016/j.ejca.2005.11.018. [PubMed] [CrossRef] [Google Scholar]
  104. Huang H., Li Y.J., Lan C.Y., Huang Q.D., Feng Y.L., Huang Y.W., Liu J.H. Clinical significance of ascites in epithelial ovarian cancer. Neoplasma. 2013;60:546–552. doi: 10.4149/neo_2013_071. [PubMed] [CrossRef] [Google Scholar]
  105. Blagden S.P. Harnessing Pandemonium: The Clinical Implications of Tumor Heterogeneity in Ovarian Cancer. Front. Oncol. 2015;5:149. doi: 10.3389/fonc.2015.00149. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  106. Ahmed N., Latifi A., Riley C.B., Findlay J.K., Quinn M.A. Neuronal transcription factor Brn-3a(l) is over expressed in high-grade ovarian carcinomas and tumor cells from ascites of patients with advanced-stage ovarian cancer. J. Ovarian Res. 2010;3:17. doi: 10.1186/1757-2215-3-17. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  107. Mahmood N., Mihalcioiu C., Rabbani S.A. Multifaceted Role of the Urokinase-Type Plasminogen Activator (uPA) and Its Receptor (uPAR): Diagnostic, Prognostic, and Therapeutic Applications. Front. Oncol. 2018;8:24. doi: 10.3389/fonc.2018.00024. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  108. Jeffrey B., Udaykumar H.S., Schulze K.S. Flow fields generated by peristaltic reflex in isolated guinea pig ileum: Impact of contraction depth and shoulders. Am. J. Physiol. Gastrointest. Liver Physiol. 2003;285:G907–G918. doi: 10.1152/ajpgi.00062.2003. [PubMed] [CrossRef] [Google Scholar]
  109. Nagy J.A., Herzberg K.T., Dvorak J.M., Dvorak H.F. Pathogenesis of malignant ascites formation: Initiating events that lead to fluid accumulation. Cancer Res. 1993;53:2631–2643. [PubMed] [Google Scholar]
  110. Ahmed N., Abubaker K., Findlay J., Quinn M. Epithelial mesenchymal transition and cancer stem cell-like phenotypes facilitate chemoresistance in recurrent ovarian cancer. Curr. Cancer Drug Targets. 2010;10:268–278. doi: 10.2174/156800910791190175. [PubMed] [CrossRef] [Google Scholar]
  111. Latifi A., Abubaker K., Castrechini N., Ward A.C., Liongue C., Dobill F., Kumar J., Thompson E.W., Quinn M.A., Findlay J.K., et al. Cisplatin treatment of primary and metastatic epithelial ovarian carcinomas generates residual cells with mesenchymal stem cell-like profile. J. Cell Biochem. 2011;112:2850–2864. doi: 10.1002/jcb.23199. [PubMed] [CrossRef] [Google Scholar]
  112. Chan D.W., Hui W.W., Cai P.C., Liu M.X., Yung M.M., Mak C.S., Leung T.H., Chan K.K., Ngan H.Y. Targeting GRB7/ERK/FOXM1 signaling pathway impairs aggressiveness of ovarian cancer cells. PLoS ONE. 2012;7:e52578. doi: 10.1371/journal.pone.0052578. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  113. Mebratu Y., Tesfaigzi Y. How ERK1/2 activation controls cell proliferation and cell death: Is subcellular localization the answer? Cell Cycle. 2009;8:1168–1175. doi: 10.4161/cc.8.8.8147. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  114. Zebisch A., Czernilofsky A.P., Keri G., Smigelskaite J., Sill H., Troppmair J. Signaling through RAS-RAF-MEK-ERK: From basics to bedside. Curr. Med. Chem. 2007;14:601–623. doi: 10.2174/092986707780059670. [PubMed] [CrossRef] [Google Scholar]
  115. Jo H., Sipos K., Go Y.M., Law R., Rong J., McDonald J.M. Differential effect of shear stress on extracellular signal-regulated kinase and N-terminal Jun kinase in endothelial cells. Gi2- and Gbeta/gamma-dependent signaling pathways. J. Biol. Chem. 1997;272:1395–1401. doi: 10.1074/jbc.272.2.1395. [PubMed] [CrossRef] [Google Scholar]
  116. Surapisitchat J., Hoefen R.J., Pi X., Yoshizumi M., Yan C., Berk B.C. Fluid shear stress inhibits TNF-alpha activation of JNK but not ERK1/2 or p38 in human umbilical vein endothelial cells: Inhibitory crosstalk among MAPK family members. Proc. Natl. Acad. Sci. USA. 2001;98:6476–6481. doi: 10.1073/pnas.101134098. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  117. Kim C.H., Jeung E.B., Yoo Y.M. Combined Fluid Shear Stress and Melatonin Enhances the ERK/Akt/mTOR Signal in Cilia-Less MC3T3-E1 Preosteoblast Cells. Int. J. Mol. Sci. 2018;19:2929. doi: 10.3390/ijms19102929. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  118. Persons D.L., Yazlovitskaya E.M., Cui W., Pelling J.C. Cisplatin-induced activation of mitogen-activated protein kinases in ovarian carcinoma cells: Inhibition of extracellular signal-regulated kinase activity increases sensitivity to cisplatin. Clin. Cancer Res. 1999;5:1007–1014. [PubMed] [Google Scholar]
  119. Hayakawa J., Ohmichi M., Kurachi H., Ikegami H., Kimura A., Matsuoka T., Jikihara H., Mercola D., Murata Y. Inhibition of extracellular signal-regulated protein kinase or c-Jun N-terminal protein kinase cascade, differentially activated by cisplatin, sensitizes human ovarian cancer cell line. J. Biol. Chem. 1999;274:31648–31654. doi: 10.1074/jbc.274.44.31648. [PubMed] [CrossRef] [Google Scholar]
  120. Yeh P.Y., Chuang S.E., Yeh K.H., Song Y.C., Ea C.K., Cheng A.L. Increase of the resistance of human cervical carcinoma cells to cisplatin by inhibition of the MEK to ERK signaling pathway partly via enhancement of anticancer drug-induced NF kappa B activation. Biochem. Pharmacol. 2002;63:1423–1430. doi: 10.1016/S0006-2952(02)00908-5. [PubMed] [CrossRef] [Google Scholar]
  121. Wang X., Martindale J.L., Holbrook N.J. Requirement for ERK activation in cisplatin-induced apoptosis. J. Biol. Chem. 2000;275:39435–39443. doi: 10.1074/jbc.M004583200. [PubMed] [CrossRef] [Google Scholar]
  122. Qin X., Liu C., Zhou Y., Wang G. Cisplatin induces programmed death-1-ligand 1(PD-L1) over-expression in hepatoma H22 cells via Erk /MAPK signaling pathway. Cell Mol. Biol. 2010;56:OL1366-72. doi: 10.1170/156. [PubMed] [CrossRef] [Google Scholar]
  123. Basu A., Tu H. Activation of ERK during DNA damage-induced apoptosis involves protein kinase Cdelta. Biochem. Biophys. Res. Commun. 2005;334:1068–1073. doi: 10.1016/j.bbrc.2005.06.199. [PubMed] [CrossRef] [Google Scholar]
  124. Nowak G. Protein kinase C-alpha and ERK1/2 mediate mitochondrial dysfunction, decreases in active Na+ transport, and cisplatin-induced apoptosis in renal cells. J. Biol. Chem. 2002;277:43377–43388. doi: 10.1074/jbc.M206373200. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  125. Chaudhury A., Tan B.J., Das S., Chiu G.N. Increased ERK activation and cellular drug accumulation in the enhanced cytotoxicity of folate receptor-targeted liposomal carboplatin. Int. J. Oncol. 2012;40:703–710. doi: 10.3892/ijo.2011.1262. [PubMed] [CrossRef] [Google Scholar]
  126. Lok G.T., Chan D.W., Liu V.W., Hui W.W., Leung T.H., Yao K.M., Ngan H.Y. Aberrant activation of ERK/FOXM1 signaling cascade triggers the cell migration/invasion in ovarian cancer cells. PLoS ONE. 2011;6:e23790. doi: 10.1371/journal.pone.0023790. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  127. Lafky J.M., Wilken J.A., Baron A.T., Maihle N.J. Clinical implications of the ErbB/epidermal growth factor (EGF) receptor family and its ligands in ovarian cancer. Biochim. Biophys. Acta. 2008;1785:232–265. doi: 10.1016/j.bbcan.2008.01.001. [PubMed] [CrossRef] [Google Scholar]
  128. Secord A.A., Blessing J.A., Armstrong D.K., Rodgers W.H., Miner Z., Barnes M.N., Lewandowski G., Mannel R.S., Gynecologic Oncology G. Phase II trial of cetuximab and carboplatin in relapsed platinum-sensitive ovarian cancer and evaluation of epidermal growth factor receptor expression: A Gynecologic Oncology Group study. Gynecol. Oncol. 2008;108:493–499. doi: 10.1016/j.ygyno.2007.11.029. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  129. Bae G.-Y., Choi S.-J., Lee J.-S., Jo J., Lee J., Kim J., Cha H.-J. Loss of E-cadherin activates EGFR-MEK/ERK signaling, which promotes invasion via the ZEB1/MMP2 axis in non-small cell lung cancer. Oncotarget. 2013;4:2512. doi: 10.18632/oncotarget.1463. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  130. Pece S., Gutkind J.S. Signaling from E-cadherins to the MAPK pathway by the recruitment and activation of epidermal growth factor receptors upon cell-cell contact formation. J. Biol. Chem. 2000;275:41227–41233. doi: 10.1074/jbc.M006578200. [PubMed] [CrossRef] [Google Scholar]
  131. Lifschitz-Mercer B., Czernobilsky B., Feldberg E., Geiger B. Expression of the adherens junction protein vinculin in human basal and squamous cell tumors: Relationship to invasiveness and metastatic potential. Hum. Pathol. 1997;28:1230–1236. doi: 10.1016/S0046-8177(97)90195-7. [PubMed] [CrossRef] [Google Scholar]
  132. Raz A., Geiger B. Altered organization of cell-substrate contacts and membrane-associated cytoskeleton in tumor cell variants exhibiting different metastatic capabilities. Cancer Res. 1982;42:5183–5190. [PubMed] [Google Scholar]
  133. Fukada T., Sakajiri H., Kuroda M., Kioka N., Sugimoto K. Fluid shear stress applied by orbital shaking induces MG-63 osteosarcoma cells to activate ERK in two phases through distinct signaling pathways. Biochem. Biophys. Rep. 2017;9:257–265. doi: 10.1016/j.bbrep.2017.01.004. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  134. Wu D.W., Wu T.C., Wu J.Y., Cheng Y.W., Chen Y.C., Lee M.C., Chen C.Y., Lee H. Phosphorylation of paxillin confers cisplatin resistance in non-small cell lung cancer via activating ERK-mediated Bcl-2 expression. Oncogene. 2014;33:4385–4395. doi: 10.1038/onc.2013.389. [PubMed] [CrossRef] [Google Scholar]
  135. Kessel D. Apoptosis and associated phenomena as a determinants of the efficacy of photodynamic therapy. Photochem. Photobiol. Sci. 2015;14:1397–1402. doi: 10.1039/C4PP00413B. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  136. Agostinis P., Berg K., Cengel K.A., Foster T.H., Girotti A.W., Gollnick S.O., Hahn S.M., Hamblin M.R., Juzeniene A., Kessel D., et al. Photodynamic therapy of cancer: An update. CA Cancer J. Clin. 2011;61:250–281. doi: 10.3322/caac.20114. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  137. Sorrin A.J., Ruhi M.K., Ferlic N.A., Karimnia V., Polacheck W.J., Celli J.P., Huang H.C., Rizvi I. Photodynamic Therapy and the Biophysics of the Tumor Microenvironment. Photochem. Photobiol. 2020 doi: 10.1111/php.13209. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  138. Niu C.J., Fisher C., Scheffler K., Wan R., Maleki H., Liu H., Sun Y., C A.S., Birngruber R., Lilge L. Polyacrylamide gel substrates that simulate the mechanical stiffness of normal and malignant neuronal tissues increase protoporphyin IX synthesis in glioma cells. J. Biomed. Opt. 2015;20:098002. doi: 10.1117/1.JBO.20.9.098002. [PubMed] [CrossRef] [Google Scholar]
  139. Perentes J.Y., Wang Y., Wang X., Abdelnour E., Gonzalez M., Decosterd L., Wagnieres G., Van den Bergh H., Peters S., Ris H.B., et al. Low-Dose Vascular Photodynamic Therapy Decreases Tumor Interstitial Fluid Pressure, which Promotes Liposomal Doxorubicin Distribution in a Murine Sarcoma Metastasis Model. Transl. Oncol. 2014;7 doi: 10.1016/j.tranon.2014.04.010. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  140. Leunig M., Goetz A.E., Gamarra F., Zetterer G., Messmer K., Jain R.K. Photodynamic therapy-induced alterations in interstitial fluid pressure, volume and water content of an amelanotic melanoma in the hamster. Br. J. Cancer. 1994;69:101–103. doi: 10.1038/bjc.1994.15. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  141. Foster T.H., Murant R.S., Bryant R.G., Knox R.S., Gibson S.L., Hilf R. Oxygen consumption and diffusion effects in photodynamic therapy. Radiat Res. 1991;126:296–303. doi: 10.2307/3577919. [PubMed] [CrossRef] [Google Scholar]
  142. Foster T.H., Hartley D.F., Nichols M.G., Hilf R. Fluence rate effects in photodynamic therapy of multicell tumor spheroids. Cancer Res. 1993;53:1249–1254. [PubMed] [Google Scholar]
  143. Nichols M.G., Foster T.H. Oxygen diffusion and reaction kinetics in the photodynamic therapy of multicell tumour spheroids. Phys. Med. Biol. 1994;39:2161–2181. doi: 10.1088/0031-9155/39/12/003. [PubMed] [CrossRef] [Google Scholar]
  144. Cavin S., Wang X., Zellweger M., Gonzalez M., Bensimon M., Wagnieres G., Krueger T., Ris H.B., Gronchi F., Perentes J.Y. Interstitial fluid pressure: A novel biomarker to monitor photo-induced drug uptake in tumor and normal tissues. Lasers Surg. Med. 2017;49:773–780. doi: 10.1002/lsm.22687. [PubMed] [CrossRef] [Google Scholar]
  145. Garcia Calavia P., Chambrier I., Cook M.J., Haines A.H., Field R.A., Russell D.A. Targeted photodynamic therapy of breast cancer cells using lactose-phthalocyanine functionalized gold nanoparticles. J. Colloid Interface Sci. 2018;512:249–259. doi: 10.1016/j.jcis.2017.10.030. [PubMed] [CrossRef] [Google Scholar]
  146. Kato T., Jin C.S., Ujiie H., Lee D., Fujino K., Wada H., Hu H.P., Weersink R.A., Chen J., Kaji M., et al. Nanoparticle targeted folate receptor 1-enhanced photodynamic therapy for lung cancer. Lung Cancer. 2017;113:59–68. doi: 10.1016/j.lungcan.2017.09.002. [PubMed] [CrossRef] [Google Scholar]
  147. Sebak A.A., Gomaa I.E.O., ElMeshad A.N., AbdelKader M.H. Targeted photodynamic-induced singlet oxygen production by peptide-conjugated biodegradable nanoparticles for treatment of skin melanoma. Photodiagnosis Photodyn. Ther. 2018;23:181–189. doi: 10.1016/j.pdpdt.2018.05.017. [PubMed] [CrossRef] [Google Scholar]
  148. Fernandes S.R.G., Fernandes R., Sarmento B., Pereira P.M.R., Tome J.P.C. Photoimmunoconjugates: Novel synthetic strategies to target and treat cancer by photodynamic therapy. Org. Biomol. Chem. 2019;17:2579–2593. doi: 10.1039/C8OB02902D. [PubMed] [CrossRef] [Google Scholar]
  149. Hamblin M.R., Miller J.L., Hasan T. Effect of charge on the interaction of site-specific photoimmunoconjugates with human ovarian cancer cells. Cancer Res. 1996;56:5205–5210. [PubMed] [Google Scholar]
  150. Flont M., Jastrzebska E., Brzozka Z. Synergistic effect of the combination therapy on ovarian cancer cells under microfluidic conditions. Anal. Chim. Acta. 2020;1100:138–148. doi: 10.1016/j.aca.2019.11.047. [PubMed] [CrossRef] [Google Scholar]
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.

Continuous-Flow Separation of Magnetic Particles from Biofluids: How Does the Microdevice Geometry Determine the Separation Performance?

Cristina González Fernández,1 Jenifer Gómez Pastora,2 Arantza Basauri,1 Marcos Fallanza,1 Eugenio Bringas,1 Jeffrey J. Chalmers,2 and Inmaculada Ortiz1,*
Author information Article notes Copyright and License information Disclaimer

생체 유체에서 자성 입자의 연속 흐름 분리 : 마이크로 장치 형상이 분리 성능을 어떻게 결정합니까?

Abstract

The use of functionalized magnetic particles for the detection or separation of multiple chemicals and biomolecules from biofluids continues to attract significant attention. After their incubation with the targeted substances, the beads can be magnetically recovered to perform analysis or diagnostic tests. Particle recovery with permanent magnets in continuous-flow microdevices has gathered great attention in the last decade due to the multiple advantages of microfluidics. As such, great efforts have been made to determine the magnetic and fluidic conditions for achieving complete particle capture; however, less attention has been paid to the effect of the channel geometry on the system performance, although it is key for designing systems that simultaneously provide high particle recovery and flow rates. Herein, we address the optimization of Y-Y-shaped microchannels, where magnetic beads are separated from blood and collected into a buffer stream by applying an external magnetic field. The influence of several geometrical features (namely cross section shape, thickness, length, and volume) on both bead recovery and system throughput is studied. For that purpose, we employ an experimentally validated Computational Fluid Dynamics (CFD) numerical model that considers the dominant forces acting on the beads during separation. Our results indicate that rectangular, long devices display the best performance as they deliver high particle recovery and high throughput. Thus, this methodology could be applied to the rational design of lab-on-a-chip devices for any magnetically driven purification, enrichment or isolation.

생체 유체에서 여러 화학 물질과 생체 분자의 검출 또는 분리를 위한 기능화된 자성 입자의 사용은 계속해서 상당한 관심을 받고 있습니다. 표적 물질과 함께 배양 한 후 비드는 자기적으로 회수되어 분석 또는 진단 테스트를 수행 할 수 있습니다.

연속 흐름 마이크로 장치에서 영구 자석을 사용한 입자 회수는 마이크로 유체의 여러 장점으로 인해 지난 10 년 동안 큰 관심을 모았습니다. 따라서 완전한 입자 포획을 달성하기 위한 자기 및 유체 조건을 결정하기 위해 많은 노력을 기울였습니다.

그러나 높은 입자 회수율과 유속을 동시에 제공하는 시스템을 설계하는데 있어 핵심이기는 하지만 시스템 성능에 대한 채널 형상의 영향에 대해서는 덜 주의를 기울였습니다.

여기에서 우리는 자기 비드가 혈액에서 분리되어 외부 자기장을 적용하여 버퍼 스트림으로 수집되는 Y-Y 모양의 마이크로 채널의 최적화를 다룹니다. 비드 회수 및 시스템 처리량에 대한 여러 기하학적 특징 (즉, 단면 형상, 두께, 길이 및 부피)의 영향을 연구합니다.

이를 위해 분리 중에 비드에 작용하는 지배적인 힘을 고려하는 실험적으로 검증된 CFD (Computational Fluid Dynamics) 수치 모델을 사용합니다.

우리의 결과는 직사각형의 긴 장치가 높은 입자 회수율과 높은 처리량을 제공하기 때문에 최고의 성능을 보여줍니다. 따라서 이 방법론은 자기 구동 정제, 농축 또는 분리를 위한 랩 온어 칩 장치의 합리적인 설계에 적용될 수 있습니다.

Keywords: particle magnetophoresis, CFD, cross section, chip fabrication

Figure 1 (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 1 (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume, and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume, and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.

References

  1. Gómez-Pastora J., Xue X., Karampelas I.H., Bringas E., Furlani E.P., Ortiz I. Analysis of separators for magnetic beads recovery: From large systems to multifunctional microdevices. Sep. Purif. Technol. 2017;172:16–31. doi: 10.1016/j.seppur.2016.07.050. [CrossRef] [Google Scholar]
  2. Wise N., Grob T., Morten K., Thompson I., Sheard S. Magnetophoretic velocities of superparamagnetic particles, agglomerates and complexes. J. Magn. Magn. Mater. 2015;384:328–334. doi: 10.1016/j.jmmm.2015.02.031. [CrossRef] [Google Scholar]
  3. Khashan S.A., Elnajjar E., Haik Y. CFD simulation of the magnetophoretic separation in a microchannel. J. Magn. Magn. Mater. 2011;323:2960–2967. doi: 10.1016/j.jmmm.2011.06.001. [CrossRef] [Google Scholar]
  4. Khashan S.A., Furlani E.P. Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field. Sep. Purif. Technol. 2014;125:311–318. doi: 10.1016/j.seppur.2014.02.007. [CrossRef] [Google Scholar]
  5. Furlani E.P. Magnetic biotransport: Analysis and applications. Materials. 2010;3:2412–2446. doi: 10.3390/ma3042412. [CrossRef] [Google Scholar]
  6. Gómez-Pastora J., Bringas E., Ortiz I. Design of novel adsorption processes for the removal of arsenic from polluted groundwater employing functionalized magnetic nanoparticles. Chem. Eng. Trans. 2016;47:241–246. [Google Scholar]
  7. Gómez-Pastora J., Bringas E., Lázaro-Díez M., Ramos-Vivas J., Ortiz I. The reverse of controlled release: Controlled sequestration of species and biotoxins into nanoparticles (NPs) In: Stroeve P., Mahmoudi M., editors. Drug Delivery Systems. World Scientific; Hackensack, NJ, USA: 2017. pp. 207–244. [Google Scholar]
  8. Ruffert C. Magnetic bead-magic bullet. Micromachines. 2016;7:21. doi: 10.3390/mi7020021. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  9. Yáñez-Sedeño P., Campuzano S., Pingarrón J.M. Magnetic particles coupled to disposable screen printed transducers for electrochemical biosensing. Sensors. 2016;16:1585. doi: 10.3390/s16101585. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  10. Schrittwieser S., Pelaz B., Parak W.J., Lentijo-Mozo S., Soulantica K., Dieckhoff J., Ludwig F., Guenther A., Tschöpe A., Schotter J. Homogeneous biosensing based on magnetic particle labels. Sensors. 2016;16:828. doi: 10.3390/s16060828. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  11. He J., Huang M., Wang D., Zhang Z., Li G. Magnetic separation techniques in sample preparation for biological analysis: A review. J. Pharm. Biomed. Anal. 2014;101:84–101. doi: 10.1016/j.jpba.2014.04.017. [PubMed] [CrossRef] [Google Scholar]
  12. Ha Y., Ko S., Kim I., Huang Y., Mohanty K., Huh C., Maynard J.A. Recent advances incorporating superparamagnetic nanoparticles into immunoassays. ACS Appl. Nano Mater. 2018;1:512–521. doi: 10.1021/acsanm.7b00025. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  13. Gómez-Pastora J., González-Fernández C., Fallanza M., Bringas E., Ortiz I. Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies. Chem. Eng. J. 2018;344:487–497. doi: 10.1016/j.cej.2018.03.110. [CrossRef] [Google Scholar]
  14. Gale B.K., Jafek A.R., Lambert C.J., Goenner B.L., Moghimifam H., Nze U.C., Kamarapu S.K. A review of current methods in microfluidic device fabrication and future commercialization prospects. Inventions. 2018;3:60. doi: 10.3390/inventions3030060. [CrossRef] [Google Scholar]
  15. Niemeyer C.M., Mirkin C.A., editors. Nanobiotechnology; Concepts, Applications and Perspectives. Wiley-VCH; Weinheim, Germany: 2004. [Google Scholar]
  16. Khashan S.A., Dagher S., Alazzam A., Mathew B., Hilal-Alnaqbi A. Microdevice for continuous flow magnetic separation for bioengineering applications. J. Micromech. Microeng. 2017;27:055016. doi: 10.1088/1361-6439/aa666d. [CrossRef] [Google Scholar]
  17. Basauri A., Gomez-Pastora J., Fallanza M., Bringas E., Ortiz I. Predictive model for the design of reactive micro-separations. Sep. Purif. Technol. 2019;209:900–907. doi: 10.1016/j.seppur.2018.09.028. [CrossRef] [Google Scholar]
  18. Abdollahi P., Karimi-Sabet J., Moosavian M.A., Amini Y. Microfluidic solvent extraction of calcium: Modeling and optimization of the process variables. Sep. Purif. Technol. 2020;231:115875. doi: 10.1016/j.seppur.2019.115875. [CrossRef] [Google Scholar]
  19. Khashan S.A., Alazzam A., Furlani E. A novel design for a microfluidic magnetophoresis system: Computational study; Proceedings of the 12th International Symposium on Fluid Control, Measurement and Visualization (FLUCOME2013); Nara, Japan. 18–23 November 2013. [Google Scholar]
  20. Pamme N. Magnetism and microfluidics. Lab Chip. 2006;6:24–38. doi: 10.1039/B513005K. [PubMed] [CrossRef] [Google Scholar]
  21. Gómez-Pastora J., Amiri Roodan V., Karampelas I.H., Alorabi A.Q., Tarn M.D., Iles A., Bringas E., Paunov V.N., Pamme N., Furlani E.P., et al. Two-step numerical approach to predict ferrofluid droplet generation and manipulation inside multilaminar flow chambers. J. Phys. Chem. C. 2019;123:10065–10080. doi: 10.1021/acs.jpcc.9b01393. [CrossRef] [Google Scholar]
  22. Gómez-Pastora J., Karampelas I.H., Bringas E., Furlani E.P., Ortiz I. Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions. Sci. Rep. 2019;9:7265. doi: 10.1038/s41598-019-43827-x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  23. Tarn M.D., Pamme N. On-Chip Magnetic Particle-Based Immunoassays Using Multilaminar Flow for Clinical Diagnostics. In: Taly V., Viovy J.L., Descroix S., editors. Microchip Diagnostics Methods and Protocols. Humana Press; New York, NY, USA: 2017. pp. 69–83. [Google Scholar]
  24. Phurimsak C., Tarn M.D., Peyman S.A., Greenman J., Pamme N. On-chip determination of c-reactive protein using magnetic particles in continuous flow. Anal. Chem. 2014;86:10552–10559. doi: 10.1021/ac5023265. [PubMed] [CrossRef] [Google Scholar]
  25. Wu X., Wu H., Hu Y. Enhancement of separation efficiency on continuous magnetophoresis by utilizing L/T-shaped microchannels. Microfluid. Nanofluid. 2011;11:11–24. doi: 10.1007/s10404-011-0768-7. [CrossRef] [Google Scholar]
  26. Vojtíšek M., Tarn M.D., Hirota N., Pamme N. Microfluidic devices in superconducting magnets: On-chip free-flow diamagnetophoresis of polymer particles and bubbles. Microfluid. Nanofluid. 2012;13:625–635. doi: 10.1007/s10404-012-0979-6. [CrossRef] [Google Scholar]
  27. Gómez-Pastora J., González-Fernández C., Real E., Iles A., Bringas E., Furlani E.P., Ortiz I. Computational modeling and fluorescence microscopy characterization of a two-phase magnetophoretic microsystem for continuous-flow blood detoxification. Lab Chip. 2018;18:1593–1606. doi: 10.1039/C8LC00396C. [PubMed] [CrossRef] [Google Scholar]
  28. Forbes T.P., Forry S.P. Microfluidic magnetophoretic separations of immunomagnetically labeled rare mammalian cells. Lab Chip. 2012;12:1471–1479. doi: 10.1039/c2lc40113d. [PubMed] [CrossRef] [Google Scholar]
  29. Nandy K., Chaudhuri S., Ganguly R., Puri I.K. Analytical model for the magnetophoretic capture of magnetic microspheres in microfluidic devices. J. Magn. Magn. Mater. 2008;320:1398–1405. doi: 10.1016/j.jmmm.2007.11.024. [CrossRef] [Google Scholar]
  30. Plouffe B.D., Lewis L.H., Murthy S.K. Computational design optimization for microfluidic magnetophoresis. Biomicrofluidics. 2011;5:013413. doi: 10.1063/1.3553239. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  31. Hale C., Darabi J. Magnetophoretic-based microfluidic device for DNA isolation. Biomicrofluidics. 2014;8:044118. doi: 10.1063/1.4893772. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  32. Becker H., Gärtner C. Polymer microfabrication methods for microfluidic analytical applications. Electrophoresis. 2000;21:12–26. doi: 10.1002/(SICI)1522-2683(20000101)21:1<12::AID-ELPS12>3.0.CO;2-7. [PubMed] [CrossRef] [Google Scholar]
  33. Pekas N., Zhang Q., Nannini M., Juncker D. Wet-etching of structures with straight facets and adjustable taper into glass substrates. Lab Chip. 2010;10:494–498. doi: 10.1039/B912770D. [PubMed] [CrossRef] [Google Scholar]
  34. Wang T., Chen J., Zhou T., Song L. Fabricating microstructures on glass for microfluidic chips by glass molding process. Micromachines. 2018;9:269. doi: 10.3390/mi9060269. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  35. Castaño-Álvarez M., Pozo Ayuso D.F., García Granda M., Fernández-Abedul M.T., Rodríguez García J., Costa-García A. Critical points in the fabrication of microfluidic devices on glass substrates. Sens. Actuators B Chem. 2008;130:436–448. doi: 10.1016/j.snb.2007.09.043. [CrossRef] [Google Scholar]
  36. Prakash S., Kumar S. Fabrication of microchannels: A review. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2015;229:1273–1288. doi: 10.1177/0954405414535581. [CrossRef] [Google Scholar]
  37. Leester-Schädel M., Lorenz T., Jürgens F., Ritcher C. Fabrication of Microfluidic Devices. In: Dietzel A., editor. Microsystems for Pharmatechnology: Manipulation of Fluids, Particles, Droplets, and Cells. Springer; Basel, Switzerland: 2016. pp. 23–57. [Google Scholar]
  38. Bartlett N.W., Wood R.J. Comparative analysis of fabrication methods for achieving rounded microchannels in PDMS. J. Micromech. Microeng. 2016;26:115013. doi: 10.1088/0960-1317/26/11/115013. [CrossRef] [Google Scholar]
  39. Ng P.F., Lee K.I., Yang M., Fei B. Fabrication of 3D PDMS microchannels of adjustable cross-sections via versatile gel templates. Polymers. 2019;11:64. doi: 10.3390/polym11010064. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  40. Furlani E.P., Sahoo Y., Ng K.C., Wortman J.C., Monk T.E. A model for predicting magnetic particle capture in a microfluidic bioseparator. Biomed. Microdevices. 2007;9:451–463. doi: 10.1007/s10544-007-9050-x. [PubMed] [CrossRef] [Google Scholar]
  41. Tarn M.D., Peyman S.A., Robert D., Iles A., Wilhelm C., Pamme N. The importance of particle type selection and temperature control for on-chip free-flow magnetophoresis. J. Magn. Magn. Mater. 2009;321:4115–4122. doi: 10.1016/j.jmmm.2009.08.016. [CrossRef] [Google Scholar]
  42. Furlani E.P. Permanent Magnet and Electromechanical Devices; Materials, Analysis and Applications. Academic Press; Waltham, MA, USA: 2001. [Google Scholar]
  43. White F.M. Viscous Fluid Flow. McGraw-Hill; New York, NY, USA: 1974. [Google Scholar]
  44. Mathew B., Alazzam A., El-Khasawneh B., Maalouf M., Destgeer G., Sung H.J. Model for tracing the path of microparticles in continuous flow microfluidic devices for 2D focusing via standing acoustic waves. Sep. Purif. Technol. 2015;153:99–107. doi: 10.1016/j.seppur.2015.08.026. [CrossRef] [Google Scholar]
  45. Furlani E.J., Furlani E.P. A model for predicting magnetic targeting of multifunctional particles in the microvasculature. J. Magn. Magn. Mater. 2007;312:187–193. doi: 10.1016/j.jmmm.2006.09.026. [CrossRef] [Google Scholar]
  46. Furlani E.P., Ng K.C. Analytical model of magnetic nanoparticle transport and capture in the microvasculature. Phys. Rev. E. 2006;73:061919. doi: 10.1103/PhysRevE.73.061919. [PubMed] [CrossRef] [Google Scholar]
  47. Eibl R., Eibl D., Pörtner R., Catapano G., Czermak P. Cell and Tissue Reaction Engineering. Springer; Berlin/Heidelberg, Germany: 2009. [Google Scholar]
  48. Pamme N., Eijkel J.C.T., Manz A. On-chip free-flow magnetophoresis: Separation and detection of mixtures of magnetic particles in continuous flow. J. Magn. Magn. Mater. 2006;307:237–244. doi: 10.1016/j.jmmm.2006.04.008. [CrossRef] [Google Scholar]
  49. Alorabi A.Q., Tarn M.D., Gómez-Pastora J., Bringas E., Ortiz I., Paunov V.N., Pamme N. On-chip polyelectrolyte coating onto magnetic droplets-Towards continuous flow assembly of drug delivery capsules. Lab Chip. 2017;17:3785–3795. doi: 10.1039/C7LC00918F. [PubMed] [CrossRef] [Google Scholar]
  50. Zhang H., Guo H., Chen Z., Zhang G., Li Z. Application of PECVD SiC in glass micromachining. J. Micromech. Microeng. 2007;17:775–780. doi: 10.1088/0960-1317/17/4/014. [CrossRef] [Google Scholar]
  51. Mourzina Y., Steffen A., Offenhäusser A. The evaporated metal masks for chemical glass etching for BioMEMS. Microsyst. Technol. 2005;11:135–140. doi: 10.1007/s00542-004-0430-3. [CrossRef] [Google Scholar]
  52. Mata A., Fleischman A.J., Roy S. Fabrication of multi-layer SU-8 microstructures. J. Micromech. Microeng. 2006;16:276–284. doi: 10.1088/0960-1317/16/2/012. [CrossRef] [Google Scholar]
  53. Su N. 8 2000 Negative Tone Photoresist Formulations 2002–2025. MicroChem Corporation; Newton, MA, USA: 2002. [Google Scholar]
  54. Su N. 8 2000 Negative Tone Photoresist Formulations 2035–2100. MicroChem Corporation; Newton, MA, USA: 2002. [Google Scholar]
  55. Fu C., Hung C., Huang H. A novel and simple fabrication method of embedded SU-8 micro channels by direct UV lithography. J. Phys. Conf. Ser. 2006;34:330–335. doi: 10.1088/1742-6596/34/1/054. [CrossRef] [Google Scholar]
  56. Kazoe Y., Yamashiro I., Mawatari K., Kitamori T. High-pressure acceleration of nanoliter droplets in the gas phase in a microchannel. Micromachines. 2016;7:142. doi: 10.3390/mi7080142. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  57. Sharp K.V., Adrian R.J., Santiago J.G., Molho J.I. Liquid flows in microchannels. In: Gad-el-Hak M., editor. MEMS: Introduction and Fundamentals. CRC Press; Boca Raton, FL, USA: 2006. pp. 10-1–10-46. [Google Scholar]
  58. Oh K.W., Lee K., Ahn B., Furlani E.P. Design of pressure-driven microfluidic networks using electric circuit analogy. Lab Chip. 2012;12:515–545. doi: 10.1039/C2LC20799K. [PubMed] [CrossRef] [Google Scholar]
  59. Bruus H. Theoretical Microfluidics. Oxford University Press; New York, NY, USA: 2008. [Google Scholar]
  60. Beebe D.J., Mensing G.A., Walker G.M. Physics and applications of microfluidics in biology. Annu. Rev. Biomed. Eng. 2002;4:261–286. doi: 10.1146/annurev.bioeng.4.112601.125916. [PubMed] [CrossRef] [Google Scholar]
  61. Yalikun Y., Tanaka Y. Large-scale integration of all-glass valves on a microfluidic device. Micromachines. 2016;7:83. doi: 10.3390/mi7050083. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  62. Van Heeren H., Verhoeven D., Atkins T., Tzannis A., Becker H., Beusink W., Chen P. [(accessed on 9 March 2020)];Design Guideline for Microfluidic Device and Component Interfaces (Part 2) Version 3. Available online: http://www.makefluidics.com/en/design-guideline?id=7.
  63. Scheuble N., Iles A., Wootton R.C.R., Windhab E.J., Fischer P., Elvira K.S. Microfluidic technique for the simultaneous quantification of emulsion instabilities and lipid digestion kinetics. Anal. Chem. 2017;89:9116–9123. doi: 10.1021/acs.analchem.7b01853. [PubMed] [CrossRef] [Google Scholar]
  64. Lynch E.C. Red blood cell damage by shear stress. Biophys. J. 1972;12:257–273. [PMC free article] [PubMed] [Google Scholar]
  65. Paul R., Apel J., Klaus S., Schügner F., Schwindke P., Reul H. Shear stress related blood damage in laminar Couette flow. Artif. Organs. 2003;27:517–529. doi: 10.1046/j.1525-1594.2003.07103.x. [PubMed] [CrossRef] [Google Scholar]
  66. Gómez-Pastora J., Karampelas I.H., Xue X., Bringas E., Furlani E.P., Ortiz I. Magnetic bead separation from flowing blood in a two-phase continuous-flow magnetophoretic microdevice: Theoretical analysis through computational fluid dynamics simulation. J. Phys. Chem. C. 2017;121:7466–7477. doi: 10.1021/acs.jpcc.6b12835. [CrossRef] [Google Scholar]
  67. Lim J., Yeap S.P., Leow C.H., Toh P.Y., Low S.C. Magnetophoresis of iron oxide nanoparticles at low field gradient: The role of shape anisotropy. J. Colloid Interface Sci. 2014;421:170–177. doi: 10.1016/j.jcis.2014.01.044. [PubMed] [CrossRef] [Google Scholar]
  68. Culbertson C.T., Sibbitts J., Sellens K., Jia S. Fabrication of Glass Microfluidic Devices. In: Dutta D., editor. Microfluidic Electrophoresis: Methods and Protocols. Humana Press; New York, NY, USA: 2019. pp. 1–12. [Google Scholar]
Fig. 7. Simulation results of temperature distribution between Ni stamps and PBO-SAM/PMMA substrate in NIL process: (A) stamp cross-sectional, (B) PMMA substrate cross-sectional, (C) 3-dimensional and (D) intrinsic 3-dimensional views, respectively. The study of computed condition in nanoimprint process is at 150 o C and 50 bar during 10 min. Note that for NIL experimental parameters, the simulated results have already decided before doing nanoimprint experiment.

A non-fluorine mold release agent for Ni stamp in nanoimprint process

Tien-Li Chang a,*, Jung-Chang Wang b
, Chun-Chi Chen c
, Ya-Wei Lee d
, Ta-Hsin Chou a
a Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Rm. 125, Building 22, 195 Section 4, Chung Hsing Road, Chutung, Hsinchu 310, Taiwan, ROC bDepartment of Manufacturing Research and Development, ADDA Corporation, Taiwan
cNational Nano Device Laboratories, Taiwan
d Research and Development Division, Ordnance Readiness Development Center, Taiwan

Abstract

이 연구는 나노 임프린트 공정에서 Ni 몰드 스탬프와 PMMA (폴리 메틸 메타 크릴 레이트) 기판 사이의 접착 방지 층으로서 새로운 재료를 제시합니다. 폴리 벤족 사진 ((6,6′-bis (2,3-dihydro3-methyl-4H-1,3-benzoxazinyl))) 분자 자기 조립 단층 (PBO-SAM)은 점착 방지 코팅제로 간주되어 불소 함유 화합물은 Ni / PMMA 기판의 나노 임프린트 공정을 개선 할 수 있습니다. 이 작업에서 나노 구조 기반 Ni 스탬프와 각인 된 PMMA 몰드는 각각 전자빔 석판화 (EBL)와 수제 나노 임프린트 장비에 의해 수행됩니다. 제작 된 나노 패턴의 형성을 제어하기 위해 시뮬레이션은 HEL (hot embossing lithography) 공정 동안 PBO-SAM / PMMA 기판의 변형에 대한 온도 분포의 영향을 분석 할 수 있습니다. 여기서 기둥 패턴의 직경은 Ni 스탬프 표면에 200nm 및 400nm 피치입니다. 이 적합성 조건에서 소수성 PBO-SAM 표면을 기반으로하여 Ni 몰드 스탬프의 결과는 품질 및 수량 제어에서 90 % 이상의 개선을 추론합니다.

Introduction

나노 임프린트 리소그래피 (NIL)는 초 미세 패터닝 기판 기술을 대량 생산할 수있는 가장 큰 잠재력입니다 [1,2]. 최근에는 광전자 장치 [3], 양자 컴퓨팅 장치 [4], 바이오 센서 [5] 및 전자 장치 [6]에 요구 될 수있는 NEMS / MEMS 기술의 빠른 개발이 이루어지고 있습니다.

따라서 기존의 포토 리소 그래프는 할당에 적합한 방법이 아닐 수 있습니다 [7]. X 선, 이온빔, 전자빔 리소그래피의 경우 LCD의 도광판 초박막 판과 같은 대 면적 패턴 제작에 적합하지 않습니다. 제어하기 어렵습니다. 일부 제작된 문제를 기반으로 NIL 프로세스는 재료, 패턴 크기, 구조 및 기판 지형면에서 유연성을 제공합니다 [8].

오늘날 NIL 제조 방법은 낮은 비용과 높은 처리량의 높은 패터닝 해상도의 조합으로 학제 간 나노 스케일 연구 및 상용 제품의 새로운 문을 열 수 있는 큰 관심을 받고 있습니다. 그러나 이 나노 임프린트 기술이 산업 규모 공정을 위해 충분히 성숙하기 전에 몇 가지 응용 문제를 해결해야 합니다.

각인된 몰드 공정은 종종 고온 (폴리머의 유리 전이 온도에 대해> 100oC)과 고압 (> 100bar)에서 수행되기 때문에 분명히 바람직하지 않습니다. 가열 및 냉각 공정의 열주기는 금형 및 각인 된 기판의 왜곡을 유발할 수 있습니다. 한 가지 특별한 문제는 스탬프와 폴리머 사이의 접착 방지 층 처리를 제어하여 기계적 결함이 임프린트 품질과 스탬프 수명에 영향을 미칠 수있는 중요한 패턴 결함이되는 것을 방지하는 것입니다.

Schift et al. 플루오르화 트리클로로 실란을 마이크로 미터 체제에서 실리콘에 대한 접착 방지 코팅으로 사용하는 것으로 입증되었습니다 [9]. 또한 Park et al. Ni 몰드 스탬프에 더 나은 접착 방지 코팅 공정을 달성하기 위해 불소화 실란제를 사용했습니다 [10].

그러나 지금까지 Ni 스탬프에 대한 접착 방지 코팅 처리의 NIL 공정에서 비 불소 물질에 대한 시도는 거의 이루어지지 않았습니다. 우리의 생활 환경은 그것을 유지하기 위해 불소가 아닌 물질이 필요합니다. 또한 Ni 계 소재의 부드러운 특성을 바탕으로 가장 중요한 롤러 나노 임프린트 기술을 개발할 수 있습니다.

본 연구의 목적은 Ni 스탬프와 PMMA 기판 사이의 점착 방지 코팅제로 PBO-SAM을 개발하여 나노 제조 기술, 즉 NIL을 향상시키는 것입니다.

Experiment

먼저 4,4′- 이소 프로필 리 덴디 페놀 (비스페놀 -A, BA-m), 포름 알데히드 및 ​​메틸 아민을 반응시켜 폴리 벤족 사진을 제조 하였다. 미국 Aldrich Chemical company, Inc.에서 구입 한 모든 화학 물질. 합성 과정에서 포름 알데히드/디 옥산 및 메틸 아민 / 디 옥산 물질을 10 o C에서 항아리에서 10분 동안 측정하는 벤족 사진 단량체가 필요했습니다.

디 에틸 에테르를 기화시킨 후, 벤족 사진 전구체가 완성되었다. benzoxazine 전구체를 140 o C에서 1 시간 동안 가열하면 BA-m 폴리 벤족 사진을 얻을 수 있습니다. 다음으로 4 인치입니다.

이 연구에서는 p 형 Si (10 0) 웨이퍼를 사용할 수 있습니다. SiO2 기반 Ni (원자량 5.87g / mole) 기판의 제조를 위해 Ti (5nm) 및 SiO2 (20nm)를 순차적으로 증착 한 후 O2- 플라즈마 처리를 수행했습니다. Ni 기판과 SiO2 층 사이의 접착력을 높이기 위해 Ti 중간층이 사용되었습니다. 아세톤, 이소프로판올 및 탈 이온수를 사용하여 세척 한 후 샘플을 포토 레지스트 (ZEP520A-7, Nippon Zeon Co., Ltd.)로 스핀 코팅했습니다.

Fig. 1. Schematic diagram of nanostructures using NIL process: (A) EBL equipment for fabricated mold stamp. (B) HEL equipment for nanoimprint pattern with computer controlled electronics. (C) A nickel-based pillar mold can imprint into a PBO-SAM polymer resist layer; afterward, the mold removal and pattern transfer are based on anisotropic etching to remove reside.
Fig. 1. Schematic diagram of nanostructures using NIL process: (A) EBL equipment for fabricated mold stamp. (B) HEL equipment for nanoimprint pattern with computer controlled electronics. (C) A nickel-based pillar mold can imprint into a PBO-SAM polymer resist layer; afterward, the mold removal and pattern transfer are based on anisotropic etching to remove reside.

마스터 몰드는 그림 1 (A)에서 Ni 필름의 반응성 이온 에칭 (RIE)과 함께 Crestec CABL8210 전자 빔 직접 쓰기 도구 (30 keV, 100 pA)를 사용하여 제작되었습니다. 그런 다음 시뮬레이션된 결과는 NIL 프로세스에서 엠보싱 압력으로 기계적 고장의 효과를 제공할 수 있으며, 이는 우리가 원하는 나노 패턴 설계 및 연구에 도움이 될 수 있습니다.

PBOSAM / PMMA 기판 모델의 변형은 3 차원 접근법에 기반한 유한 체적 방법 (FVM)을 통해 예측할 수 있습니다. Navier-Stokes 방정식 [11]에서 압력과 속도 사이의 결합은 SIMPLE 알고리즘을 사용하여 이루어집니다. 2 차 상향 이산화 방식은 대류 플럭스 및 운동량의 확산 플럭스, 유체의 질량 분율에 대한 중심 차이 방식에 대해 구현됩니다. 완화 부족 요인의 일반적인 값은 0.5입니다.

수렴 기준이 1105로 설정된 연속성을 제외한 모든 변수에 대해 잔차가 1103 미만인 경우 솔루션이 수렴된 것으로 간주됩니다. 여기서 각인된 나노 패턴은 그림 1 (B)와 같이 수제 장비에서 수행한 HEL 공정을 통해 사용할 수 있습니다. PBO-SAM 코팅 방법으로 HEL 절차를 활용 한 나노 패턴의 제작은 그림 1 (C)에 개략적으로 표시되었습니다.

200nm의 얇은 PMMA 필름 (분자량 15kg / mole)을 SiO2 기판에 스핀 코팅 한 후 160oC에서 30 분 동안 핫 플레이트에서 베이킹했습니다. 또한 PBO-SAM 코팅은 접착 방지제입니다. CVD 공정에 의해 증착되었습니다. 마스터는 150oC 및 50bar에서 10 분 동안 PBO-SAM / PMMA 기판 필름에 엠보싱하여 복제되었습니다.

마지막으로, 엠보싱 된 나노 구조물의 바닥에 남아 있던 PBO-SAM / PMMA 층은 RIE 처리로 제거되었습니다. 각 임프린트 후 스탬프 및 기판의 품질이 제작 된 후 현미경을 사용하여 관찰하고 물 접촉각 (CA) 측정을 사용하여 습윤 및 접착 특성을 알아낼 수 있습니다.

Fig. 2. FTIR absorption spectrum of polybenzoxazines indicates the vibrational modes of molecular bonds.
Fig. 2. FTIR absorption spectrum of polybenzoxazines indicates the vibrational modes of molecular bonds.
Fig. 3. FE-SEM micrograph of Ni stamps before imprinted PMMA substrate. The pillar diameter is 200 nm, and its period is 400 nm.
Fig. 3. FE-SEM micrograph of Ni stamps before imprinted PMMA substrate. The pillar diameter is 200 nm, and its period is 400 nm.
Fig. 5. Contact angles of water drops on (A) a PMMA polymer film surface, and (B) a smooth PBO-SAM coating film surfaceFig. 6. Simulation of Ni stamps and PBO-SAM/PMMA substrate in NIL process: (A) A nanoimprint system geometry, and (B) its grid plot.
Fig. 5. Contact angles of water drops on (A) a PMMA polymer film surface, and (B) a smooth PBO-SAM coating film surfaceFig. 6. Simulation of Ni stamps and PBO-SAM/PMMA substrate in NIL process: (A) A nanoimprint system geometry, and (B) its grid plot.
Fig. 7. Simulation results of temperature distribution between Ni stamps and PBO-SAM/PMMA substrate in NIL process: (A) stamp cross-sectional, (B) PMMA substrate cross-sectional, (C) 3-dimensional and (D) intrinsic 3-dimensional views, respectively. The study of computed condition in nanoimprint process is at 150 o C and 50 bar during 10 min. Note that for NIL experimental parameters, the simulated results have already decided before doing nanoimprint experiment.
Fig. 7. Simulation results of temperature distribution between Ni stamps and PBO-SAM/PMMA substrate in NIL process: (A) stamp cross-sectional, (B) PMMA substrate cross-sectional, (C) 3-dimensional and (D) intrinsic 3-dimensional views, respectively. The study of computed condition in nanoimprint process is at 150 o C and 50 bar during 10 min. Note that for NIL experimental parameters, the simulated results have already decided before doing nanoimprint experiment.

References

[1] M.D. Austin, H.X. Ge, W. Wu, M.T. Li, Z.N. Yu, D. Wasserman, S.A. Lyon, S.Y. Chou, Nature 417 (2002) 835.
[2] S.Y. Chou, C. Keimel, J. Gu, Appl. Phys. Lett. 84 (2004) 5299.
[3] Q. Wang, G. Farrell, P. Wang, G. Rajan, T. Thomas, Sensor Actuator A 134 (2007) 405.
[4] C. Kentsch, W. Henschel, D. Wharam, D.P. Kern, Microelectron. Eng. 83 (2006) 1753.
[5] T.L. Chang, Y.W. Lee, C.C. Chen, F.H. Ko, Microelectron. Eng. 84 (2007) 1689.
[6] S. Tisa, F. Zappa, A. Tosi, S. Cova, Sensor Actuator A 140 (2007) 113.
[7] M. Agirregabiria, F.J. Blanco, J. Berganzo, M.T. Arroyo, A. Fullaondo, K. Mayora, J.M. Ruano-López, Lab Chip 5 (2005) 5545.
[8] W. Hu, E.K.F. Yim, R.M. Reano, K.W. Leong, S.W. Pang, J. Vac. Sci. Technol. B 84 (2005) 2984.
[9] H. Schift, L.J. Heyderman, C. Padeste, J. Gobrecht, Microelectron. Eng. 423 (2002) 61.
[10] S. Park, H. Schift, C. Padeste, B. Schnyder, R. Kötz, J. Gobrecht, Microelectron. Eng. 73–74 (2004) 196.
[11] A. Yokoo, M. Nakao, H. Yoshikawa, H. Masuda, T. Tamamura, Jpn. J. Appl. Phys. 38 (1999) 7268.

Simulation Gallery

Simulation Gallery

Simulation Gallery | 시뮬레이션 갤러리

시뮬레이션 비디오 갤러리에서 FLOW-3D  제품군으로 모델링 할 수 있는 다양한 가능성을 살펴보십시오 .

적층 제조 시뮬레이션 갤러리

FLOW-3D AM 은 레이저 파우더 베드 융합, 바인더 제트 및 직접 에너지 증착과 같은 적층 제조 공정을 시뮬레이션하고 분석합니다. FLOW-3D AM 의 다중 물리 기능은 공정 매개 변수의 분석 및 최적화를 위해 분말 확산 및 압축, 용융 풀 역학, L-PBF 및 DED에 대한 다공성 형성, 바인더 분사 공정을 위한 수지 침투 및 확산에 대한 매우 정확한 시뮬레이션을 제공합니다. 

Multi-material Laser Powder Bed Fusion | FLOW-3D AM

Micro and meso scale simulations using FLOW-3D AM help us understand the mixing of different materials in the melt pool and the formation of potential defects such as lack of fusion and porosity. In this simulation, the stainless steel and aluminum powders have independently-defined temperature dependent material properties that FLOW-3D AM tracks to accurately capture the melt pool dynamics. Learn more about FLOW-3D AM’s mutiphysics simulation capabilities at https://www.flow3d.com/products/flow3…

Laser Welding Simulation Gallery

FLOW-3D WELD 는 레이저 용접 공정에 대한 강력한 통찰력을 제공하여 공정 최적화를 달성합니다. 더 나은 공정 제어로 다공성, 열 영향 영역을 최소화하고 미세 구조 진화를 제어 할 수 있습니다. 레이저 용접 공정을 정확하게 시뮬레이션하기 위해 FLOW-3D WELD 는 레이저 열원, 레이저-재료 상호 작용, 유체 흐름, 열 전달, 표면 장력, 응고, 다중 레이저 반사 및 위상 변화를 특징으로 합니다.

Keyhole welding simulation | FLOW-3D WELD

물 및 환경 시뮬레이션 갤러리

FLOW-3D 는 물고기 통로, 댐 파손, 배수로, 눈사태, 수력 발전 및 기타 수자원 및 환경 공학 과제 모델링을 포함하여 유압 산업에 대한 많은 응용 분야를 가지고 있습니다. 엔지니어는 수력 발전소의 기존 인프라 용량을 늘리고, 어류 통로, 수두 손실을 최소화하는 흡입구, 포 이베이 설계 및 테일 레이스 흐름을위한 개선 된 설계를 개발하고, 수세 및 퇴적 및 공기 유입을 분석 할 수 있습니다.

금속 주조 시뮬레이션 갤러리

FLOW-3D CAST  에는 캐스팅을 위해 특별히 설계된 광범위하고 강력한 물리적 모델이 포함되어 있습니다. 이러한 특수 모델에는 lost foam casting, non-Newtonian fluids, and die cycling에 대한 알고리즘이 포함됩니다. FLOW-3D CAST 의 강력한 시뮬레이션 엔진과 결함 예측을 위한 새로운 도구는 설계주기를 단축하고 비용을 절감 할 수 있는 통찰력을 제공합니다.

HPDC |Comparison of slow shot profiles and entrained air during a filling simulation |FLOW-3D CAST

Shown is a video comparing two slow shot profiles. The graphs highlight the shot profiles through time and the difference in entrained air between the slow shots. Note the lack of air entrained in shot sleeve with calculated shot profile which yields a much better controlled flow within the shot sleeve.

Coastal & Maritime Applications | FLOW-3D

FLOW-3D는 선박 설계, 슬로싱 다이내믹스, 파동 충격 및 환기 등 연안 및 해양 애플리케이션에 이상적인 소프트웨어입니다. 연안 애플리케이션의 경우 FLOW-3D는 연안 구조물에 심각한 폭풍과 쓰나미 파장의 세부 정보를 정확하게 예측하고 플래시 홍수 및 중요 구조물 홍수 및 손상 분석에 사용됩니다.

Figure 1.1: A water droplet with a radius of 1 mm resting on a glass substrate. The surface of the droplet takes on a spherical cap shape. The contact angle θ is defined by the balance of the interfacial forces.

Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles

기판 냉각 및 액적 모양 및 조성이 액적 증발 및 입자 증착에 미치는 영향

by Vahid Bazargan
M.A.Sc., Mechanical Engineering, The University of British Columbia, 2008
B.Sc., Mechanical Engineering, Sharif University of Technology, 2006
B.Sc., Chemical & Petroleum Engineering, Sharif University of Technology, 2006

고착 방울은 평평한 기판에 놓인 액체 방울입니다. 작은 고정 액적이 증발하는 동안 액적의 접촉선은 고정된 접촉 영역이 있는 고정된 단계와 고정된 접촉각이 있는 고정 해제된 단계의 두 가지 단계를 거칩니다. 고정된 접촉 라인이 있는 증발은 액적 내부에서 접촉 라인을 향한 흐름을 생성합니다.

이 흐름은 입자를 운반하고 접촉 선 근처에 침전시킵니다. 이로 인해 일반적으로 관찰되는 “커피 링”현상이 발생합니다. 이 논문은 증발 과정과 고착성 액적의 증발 유도 흐름에 대한 연구를 제공하고 콜로이드 현탁액에서 입자의 침착에 대한 통찰력을 제공합니다. 여기서 우리는 먼저 작은 고착 방울의 증발을 연구하고 증발 과정에서 기판의 열전도도의 중요성에 대해 논의합니다.

현재 증발 모델이 500µm 미만의 액적 크기에 대해 심각한 오류를 생성하는 방법을 보여줍니다. 우리의 모델에는 열 효과가 포함되어 있으며, 특히 증발 잠열의 균형을 맞추기 위해 액적에 열을 제공하는 기판의 열전도도를 포함합니다. 실험 결과를 바탕으로 접촉각의 진화와 관련된 접촉 선의 가상 움직임을 정의하여 고정 및 고정 해제 단계의 전체 증발 시간을 고려합니다.

우리의 모델은 2 % 미만의 오차로 500 µm보다 작은 물방울에 대한 실험 결과와 일치합니다. 또한 유한한 크기의 라인 액적의 증발을 연구하고 증발 중 접촉 라인의 복잡한 동작에 대해 논의합니다. 에너지 공식을 적용하고 접촉 선이 구형 방울의 후퇴 접촉각보다 높은 접촉각을 가진 선 방울의 두 끝에서 후퇴하기 시작 함을 보여줍니다. 그리고 라인 방울 내부의 증발 유도 흐름을 보여줍니다.

마지막으로, 계면 활성제 존재 하에서 접촉 라인의 거동을 논의하고 입자 증착에 대한 Marangoni 흐름 효과에 대해 논의합니다. 열 Marangoni 효과는 접촉 선 근처에 증착 된 입자의 양에 영향을 미치며, 기판 온도가 낮을수록 접촉 선 근처에 증착되는 입자의 양이 많다는 것을 알 수 있습니다.

Figure 1.1: A water droplet with a radius of 1 mm resting on a glass substrate. The surface of the droplet takes on a spherical cap shape. The contact angle θ is defined by the balance of the interfacial forces.
Figure 1.1: A water droplet with a radius of 1 mm resting on a glass substrate. The surface of the droplet takes on a spherical cap shape. The contact angle θ is defined by the balance of the interfacial forces.
Figure 2.1: Evaporation modes of sessile droplets on a substrate: (a) evaporation at constant contact angle (de-pinned stage) and (b) evaporation at constant contact area (pinned stage)
Figure 2.1: Evaporation modes of sessile droplets on a substrate: (a) evaporation at constant contact angle (de-pinned stage) and (b) evaporation at constant contact area (pinned stage)
Figure 2.2: A sessil droplet with its image can be profiled as the equiconvex lens formed by two intersecting spheres with radius of a.
Figure 2.2: A sessil droplet with its image can be profiled as the equiconvex lens formed by two intersecting spheres with radius of a.
Figure 2.3: The droplet life time for both evaporation modes derived from Equation 2.2.
Figure 2.3: The droplet life time for both evaporation modes derived from Equation 2.2.
Figure 2.4: A probability of escape for vapor molecules at two different sites of the surface of the droplet for diffusion controlled evaporation. The random walk path initiated from a vapor molecule is more likely to result in a return to the surface if the starting point is further away from the edge of the droplet.
Figure 2.4: A probability of escape for vapor molecules at two different sites of the surface of the droplet for diffusion controlled evaporation. The random walk path initiated from a vapor molecule is more likely to result in a return to the surface if the starting point is further away from the edge of the droplet.
Figure 2.5: Schematic of the sessile droplet on a substrate
Figure 2.5: Schematic of the sessile droplet on a substrate. The evaporation rate at the surface of the droplet is enhanced toward the edge of the droplet.
Figure 2.6: The domain mesh (a) and the solution of the Laplace equation for diffusion of the water vapor molecule with the concentration of Cv = 1.9×10−8 g/mm3 at the surface of the droplet into the ambient air with the relative humidity of 55%, i.e. φ = 0.55 (b).
Figure 2.6: The domain mesh (a) and the solution of the Laplace equation for diffusion of the water vapor molecule with the concentration of Cv = 1.9×10−8 g/mm3 at the surface of the droplet into the ambient air with the relative humidity of 55%, i.e. φ = 0.55 (b).
Figure 3.1: The portable micro printing setup. A motorized linear stage from Zaber Technologies Inc. was used to control the place and speed of the micro nozzle.
Figure 3.1: The portable micro printing setup. A motorized linear stage from Zaber Technologies Inc. was used to control the place and speed of the micro nozzle.
Figure 4.6: Temperature contours inside the substrate adjacent to the droplet
Figure 4.6: Temperature contours inside the substrate adjacent to the droplet
Figure 4.7: The effect of substrate cooling on the evaporation rate, the basic model shows the same value for all substrates.
Figure 4.7: The effect of substrate cooling on the evaporation rate, the basic model shows the same value for all substrates.

Bibliography

[1] R. G. Picknett and R. Bexon, “The evaporation of sessile or pendant drops in still air,” Journal of Colloid and Interface Science, vol. 61, pp. 336–350, Sept. 1977. → pages viii, 8, 9, 18, 42
[2] H. Y. Erbil, “Evaporation of pure liquid sessile and spherical suspended drops: A review,” Advances in Colloid and Interface Science, vol. 170, pp. 67–86, Jan. 2012. → pages 1
[3] R. Sharma, C. Y. Lee, J. H. Choi, K. Chen, and M. S. Strano, “Nanometer positioning, parallel alignment, and placement of single anisotropic nanoparticles using hydrodynamic forces in cylindrical droplets,” Nano Lett., vol. 7, no. 9, pp. 2693–2700, 2007. → pages 1, 54, 71
[4] S. Tokonami, H. Shiigi, and T. Nagaoka, “Review: Micro- and nanosized molecularly imprinted polymers for high-throughput analytical applications,” Analytica Chimica Acta, vol. 641, pp. 7–13, May 2009. →pages 71
[5] A. A. Sagade and R. Sharma, “Copper sulphide (CuxS) as an ammonia gas sensor working at room temperature,” Sensors and Actuators B: Chemical, vol. 133, pp. 135–143, July 2008. → pages
[6] W. R. Small, C. D. Walton, J. Loos, and M. in het Panhuis, “Carbon nanotube network formation from evaporating sessile drops,” The Journal of Physical Chemistry B, vol. 110, pp. 13029–13036, July 2006. → pages 71
[7] S. H. Ko, H. Lee, and K. H. Kang, “Hydrodynamic flows in electrowetting,” Langmuir, vol. 24, pp. 1094–1101, Feb. 2008. → pages 42
[8] T. T. Nellimoottil, P. N. Rao, S. S. Ghosh, and A. Chattopadhyay, “Evaporation-induced patterns from droplets containing motile and nonmotile bacteria,” Langmuir, vol. 23, pp. 8655–8658, Aug. 2007. → pages 1
[9] R. Sharma and M. S. Strano, “Centerline placement and alignment of anisotropic nanotubes in high aspect ratio cylindrical droplets of nanometer diameter,” Advanced Materials, vol. 21, no. 1, p. 6065, 2009. → pages 1, 54, 71
[10] V. Dugas, J. Broutin, and E. Souteyrand, “Droplet evaporation study applied to DNA chip manufacturing,” Langmuir, vol. 21, pp. 9130–9136, Sept. → pages 2, 71
[11] Y.-C. Hu, Q. Zhou, Y.-F. Wang, Y.-Y. Song, and L.-S. Cui, “Formation mechanism of micro-flows in aqueous poly(ethylene oxide) droplets on a substrate at different temperatures,” Petroleum Science, vol. 10, pp. 262–268, June 2013. → pages 2, 34, 54
[12] T.-S. Wong, T.-H. Chen, X. Shen, and C.-M. Ho, “Nanochromatography driven by the coffee ring effect,” Analytical Chemistry, vol. 83, pp. 1871–1873, Mar. 2011. → pages 71
[13] J.-H. Kim, S.-B. Park, J. H. Kim, and W.-C. Zin, “Polymer transports inside evaporating water droplets at various substrate temperatures,” The Journal of Physical Chemistry C, vol. 115, pp. 15375–15383, Aug. 2011. → pages 54
[14] S. Choi, S. Stassi, A. P. Pisano, and T. I. Zohdi, “Coffee-ring effect-based three dimensional patterning of Micro/Nanoparticle assembly with a single droplet,” Langmuir, vol. 26, pp. 11690–11698, July 2010. → pages
[15] D. Wang, S. Liu, B. J. Trummer, C. Deng, and A. Wang, “Carbohydrate microarrays for the recognition of cross-reactive molecular markers of microbes and host cells,” Nature biotechnology, vol. 20, pp. 275–281, Mar. PMID: 11875429. → pages 2, 54, 71
[16] H. K. Cammenga, “Evaporation mechanisms of liquids,” Current topics in materials science, vol. 5, pp. 335–446, 1980. → pages 3
[17] C. Snow, “Potential problems and capacitance for a conductor bounded by two intersecting spheres,” Journal of Research of the National Bureau of Standards, vol. 43, p. 337, 1949. → pages 9
[18] R. D. Deegan, O. Bakajin, T. F. Dupont, G. Huber, S. R. Nagel, and T. A. Witten, “Contact line deposits in an evaporating drop,” Physical Review E, vol. 62, p. 756, July 2000. → pages 10, 14, 18, 27, 53, 54, 71, 84
[19] H. Hu and R. G. Larson, “Evaporation of a sessile droplet on a substrate,” The Journal of Physical Chemistry B, vol. 106, pp. 1334–1344, Feb. 2002. → pages 12, 18, 29, 43, 44, 48, 49, 53, 61, 71, 84
[20] Y. O. Popov, “Evaporative deposition patterns: Spatial dimensions of the deposit,” Physical Review E, vol. 71, p. 036313, Mar. 2005. → pages 14, 27, 43, 44, 45, 54
[21] H. Gelderblom, A. G. Marin, H. Nair, A. van Houselt, L. Lefferts, J. H. Snoeijer, and D. Lohse, “How water droplets evaporate on a superhydrophobic substrate,” Physical Review E, vol. 83, no. 2, p. 026306,→ pages
[22] F. Girard, M. Antoni, S. Faure, and A. Steinchen, “Influence of heating temperature and relative humidity in the evaporation of pinned droplets,” Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 323, pp. 36–49, June 2008. → pages 18
[23] Y. Y. Tarasevich, “Simple analytical model of capillary flow in an evaporating sessile drop,” Physical Review E, vol. 71, p. 027301, Feb. 2005. → pages 19, 54, 62, 72
[24] A. J. Petsi and V. N. Burganos, “Potential flow inside an evaporating cylindrical line,” Physical Review E, vol. 72, p. 047301, Oct. 2005. → pages 22, 55, 62, 68, 71
[25] A. J. Petsi and V. N. Burganos, “Evaporation-induced flow in an inviscid liquid line at any contact angle,” Physical Review E, vol. 73, p. 041201, Apr.→ pages 23, 53, 55, 72
[26] H. Masoud and J. D. Felske, “Analytical solution for stokes flow inside an evaporating sessile drop: Spherical and cylindrical cap shapes,” Physics of Fluids, vol. 21, pp. 042102–042102–11, Apr. 2009. → pages 23, 55, 62, 71, 72
[27] H. Hu and R. G. Larson, “Analysis of the effects of marangoni stresses on the microflow in an evaporating sessile droplet,” Langmuir, vol. 21, pp. 3972–3980, Apr. 2005. → pages 24, 28, 53, 54, 56, 62, 68, 71, 72, 74, 84
[28] R. Bhardwaj, X. Fang, and D. Attinger, “Pattern formation during the evaporation of a colloidal nanoliter drop: a numerical and experimental study,” New Journal of Physics, vol. 11, p. 075020, July 2009. → pages 28
[29] A. Petsi, A. Kalarakis, and V. Burganos, “Deposition of brownian particles during evaporation of two-dimensional sessile droplets,” Chemical Engineering Science, vol. 65, pp. 2978–2989, May 2010. → pages 28
[30] J. Park and J. Moon, “Control of colloidal particle deposit patterns within picoliter droplets ejected by ink-jet printing,” Langmuir, vol. 22, pp. 3506–3513, Apr. 2006. → pages 28
[31] H. Hu and R. G. Larson, “Marangoni effect reverses coffee-ring depositions,” The Journal of Physical Chemistry B, vol. 110, pp. 7090–7094, Apr. 2006. → pages 29, 74
[32] K. H. Kang, S. J. Lee, C. M. Lee, and I. S. Kang, “Quantitative visualization of flow inside an evaporating droplet using the ray tracing method,” Measurement Science and Technology, vol. 15, pp. 1104–1112, June 2004. → pages 34
[33] S. T. Beyer and K. Walus, “Controlled orientation and alignment in films of single-walled carbon nanotubes using inkjet printing,” Langmuir, vol. 28, pp. 8753–8759, June 2012. → pages 42, 71
[34] G. McHale, “Surface free energy and microarray deposition technology,” Analyst, vol. 132, pp. 192–195, Feb. 2007. → pages 42
[35] R. Bhardwaj, X. Fang, P. Somasundaran, and D. Attinger, “Self-assembly of colloidal particles from evaporating droplets: Role of DLVO interactions and proposition of a phase diagram,” Langmuir, vol. 26, pp. 7833–7842, June→ pages 42
[36] G. J. Dunn, S. K. Wilson, B. R. Duffy, S. David, and K. Sefiane, “The strong influence of substrate conductivity on droplet evaporation,” Journal of Fluid Mechanics, vol. 623, no. 1, p. 329351, 2009. → pages 44
[37] M. S. Plesset and A. Prosperetti, “Flow of vapour in a liquid enclosure,” Journal of Fluid Mechanics, vol. 78, pp. 433–444, 1976. → pages 44
[38] S. Das, P. R. Waghmare, M. Fan, N. S. K. Gunda, S. S. Roy, and S. K. Mitra, “Dynamics of liquid droplets in an evaporating drop: liquid droplet coffee stain? effect,” RSC Advances, vol. 2, pp. 8390–8401, Aug. 2012. → pages 53
[39] B. J. Fischer, “Particle convection in an evaporating colloidal droplet,” Langmuir, vol. 18, pp. 60–67, Jan. 2002. → pages 54
[40] J. L. Wilbur, A. Kumar, H. A. Biebuyck, E. Kim, and G. M. Whitesides, “Microcontact printing of self-assembled monolayers: applications in microfabrication,” Nanotechnology, vol. 7, p. 452, Dec. 1996. → pages 54
[41] T. Kawase, H. Sirringhaus, R. H. Friend, and T. Shimoda, “Inkjet printed via-hole interconnections and resistors for all-polymer transistor circuits,” Advanced Materials, vol. 13, no. 21, p. 16011605, 2001. → pages 71
[42] B.-J. de Gans, P. C. Duineveld, and U. S. Schubert, “Inkjet printing of polymers: State of the art and future developments,” Advanced Materials, vol. 16, no. 3, p. 203213, 2004. → pages 71
[43] H. Sirringhaus, T. Kawase, R. H. Friend, T. Shimoda, M. Inbasekaran, W. Wu, and E. P. Woo, “High-resolution inkjet printing of all-polymer transistor circuits,” Science, vol. 290, pp. 2123–2126, Dec. 2000. PMID:→ pages
[44] D. Soltman and V. Subramanian, “Inkjet-printed line morphologies and temperature control of the coffee ring effect,” Langmuir, vol. 24, pp. 2224–2231, Mar. 2008. → pages 54
[45] R. Tadmor and P. S. Yadav, “As-placed contact angles for sessile drops,” Journal of Colloid and Interface Science, vol. 317, pp. 241–246, Jan. 2008. → pages 56
[46] J. Drelich, “The significance and magnitude of the line tension in three-phase (solid-liquid-fluid) systems,” Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 116, pp. 43–54, Sept. 1996. → pages 56
[47] R. Tadmor, “Line energy, line tension and drop size,” Surface Science, vol. 602, pp. L108–L111, July 2008. → pages 69
[48] C.-H. Choi and C.-J. C. Kim, “Droplet evaporation of pure water and protein solution on nanostructured superhydrophobic surfaces of varying heights,” Langmuir, vol. 25, pp. 7561–7567, July 2009. → pages 71
[49] K. F. Baughman, R. M. Maier, T. A. Norris, B. M. Beam, A. Mudalige, J. E. Pemberton, and J. E. Curry, “Evaporative deposition patterns of bacteria from a sessile drop: Effect of changes in surface wettability due to exposure to a laboratory atmosphere,” Langmuir, vol. 26, pp. 7293–7298, May 2010.
[50] D. Brutin, B. Sobac, and C. Nicloux, “Influence of substrate nature on the evaporation of a sessile drop of blood,” Journal of Heat Transfer, vol. 134, pp. 061101–061101, May 2012. → pages 71
[51] D. Pech, M. Brunet, P.-L. Taberna, P. Simon, N. Fabre, F. Mesnilgrente, V. Condra, and H. Durou, “Elaboration of a microstructured inkjet-printed carbon electrochemical capacitor,” Journal of Power Sources, vol. 195, pp. 1266–1269, Feb. 2010. → pages 71
[52] J. Bachmann, A. Ellies, and K. Hartge, “Development and application of a new sessile drop contact angle method to assess soil water repellency,” Journal of Hydrology, vol. 231232, pp. 66–75, May 2000. → pages 71
[53] H. Y. Erbil, G. McHale, and M. I. Newton, “Drop evaporation on solid surfaces: constant contact angle mode,” Langmuir, vol. 18, no. 7, pp. 2636–2641, 2002. → pages
[54] X. Fang, B. Li, J. C. Sokolov, M. H. Rafailovich, and D. Gewaily, “Hildebrand solubility parameters measurement via sessile drops evaporation,” Applied Physics Letters, vol. 87, pp. 094103–094103–3, Aug.→ pages
[55] Y. C. Jung and B. Bhushan, “Wetting behaviour during evaporation and condensation of water microdroplets on superhydrophobic patterned surfaces,” Journal of Microscopy, vol. 229, no. 1, p. 127140, 2008. → pages 71
[56] J. Drelich, J. D. Miller, and R. J. Good, “The effect of drop (bubble) size on advancing and receding contact angles for heterogeneous and rough solid surfaces as observed with sessile-drop and captive-bubble techniques,”
Journal of Colloid and Interface Science, vol. 179, pp. 37–50, Apr. 1996. →pages 72, 75
[57] D. Bargeman and F. Van Voorst Vader, “Effect of surfactants on contact angles at nonpolar solids,” Journal of Colloid and Interface Science, vol. 42, pp. 467–472, Mar. 1973. → pages 73
[58] J. Menezes, J. Yan, and M. Sharma, “The mechanism of alteration of macroscopic contact angles by the adsorption of surfactants,” Colloids and Surfaces, vol. 38, no. 2, pp. 365–390, 1989. → pages
[59] T. Okubo, “Surface tension of structured colloidal suspensions of polystyrene and silica spheres at the air-water interface,” Journal of Colloid and Interface Science, vol. 171, pp. 55–62, Apr. 1995. → pages 73, 76
[60] R. Pyter, G. Zografi, and P. Mukerjee, “Wetting of solids by surface-active agents: The effects of unequal adsorption to vapor-liquid and solid-liquid interfaces,” Journal of Colloid and Interface Science, vol. 89, pp. 144–153, Sept. 1982. → pages 73
[61] T. Mitsui, S. Nakamura, F. Harusawa, and Y. Machida, “Changes in the interfacial tension with temperature and their effects on the particle size and stability of emulsions,” Kolloid-Zeitschrift und Zeitschrift fr Polymere, vol. 250, pp. 227–230, Mar. 1972. → pages 73
[62] S. Phongikaroon, R. Hoffmaster, K. P. Judd, G. B. Smith, and R. A. Handler, “Effect of temperature on the surface tension of soluble and insoluble surfactants of hydrodynamical importance,” Journal of Chemical & Engineering Data, vol. 50, pp. 1602–1607, Sept. 2005. → pages 73, 80
[63] V. S. Vesselovsky and V. N. Pertzov, “Adhesion of air bubbles to the solid surface,” Zh. Fiz. Khim, vol. 8, pp. 245–259, 1936. → pages 75
[64] Hideo Nakae, Ryuichi Inui, Yosuke Hirata, and Hiroyuki Saito, “Effects of surface roughness on wettability,” Acta Materialia, vol. 46, pp. 2313–2318, Apr. 1998. → pages
[65] R. J. Good and M. Koo, “The effect of drop size on contact angle,” Journal of Colloid and Interface Science, vol. 71, pp. 283–292, Sept. 1979. → pages

Figure 2. Simulation of droplet separation by EWOD

Non-Linear Electrohydrodynamics in Microfluidic Devices

미세 유체 장치의 비선형 전기 유체 역학

by Jun ZengHewlett-Packard Laboratories, Hewlett-Packard Company, 1501 Page Mill Road, Palo Alto, CA 94304, USAInt. J. Mol. Sci.201112(3), 1633-1649; https://doi.org/10.3390/ijms12031633Received: 24 January 2011 / Revised: 10 February 2011 / Accepted: 24 February 2011 / Published: 3 March 2011

Abstract

Since the inception of microfluidics, the electric force has been exploited as one of the leading mechanisms for driving and controlling the movement of the operating fluid and the charged suspensions. Electric force has an intrinsic advantage in miniaturized devices. Because the electrodes are placed over a small distance, from sub-millimeter to a few microns, a very high electric field is easy to obtain. The electric force can be highly localized as its strength rapidly decays away from the peak. This makes the electric force an ideal candidate for precise spatial control. The geometry and placement of the electrodes can be used to design electric fields of varying distributions, which can be readily realized by Micro-Electro-Mechanical Systems (MEMS) fabrication methods. In this paper, we examine several electrically driven liquid handling operations. The emphasis is given to non-linear electrohydrodynamic effects. We discuss the theoretical treatment and related numerical methods. Modeling and simulations are used to unveil the associated electrohydrodynamic phenomena. The modeling based investigation is interwoven with examples of microfluidic devices to illustrate the applications. 

Keywords: dielectrophoresiselectrohydrodynamicselectrowettinglab-on-a-chipmicrofluidicsmodelingnumerical simulationreflective display

요약

미세 유체학이 시작된 이래로 전기력은 작동 유체와 충전 된 서스펜션의 움직임을 제어하고 제어하는 ​​주요 메커니즘 중 하나로 활용되어 왔습니다. 전기력은 소형 장치에서 본질적인 이점이 있습니다. 전극이 밀리미터 미만에서 수 미크론까지 작은 거리에 배치되기 때문에 매우 높은 전기장을 쉽게 얻을 수 있습니다. 

전기력은 강도가 피크에서 멀어지면서 빠르게 감소하기 때문에 고도로 국부화 될 수 있습니다. 이것은 전기력을 정밀한 공간 제어를 위한 이상적인 후보로 만듭니다.

전극의 기하학적 구조와 배치는 다양한 분포의 전기장을 설계하는 데 사용될 수 있으며, 이는 MEMS (Micro-Electro-Mechanical Systems) 제조 방법으로 쉽게 실현할 수 있습니다. 

이 논문에서 우리는 몇 가지 전기 구동 액체 처리 작업을 검토합니다. 비선형 전기 유체 역학적 효과에 중점을 둡니다. 이론적 처리 및 관련 수치 방법에 대해 논의합니다. 모델링과 시뮬레이션은 관련된 전기 유체 역학 현상을 밝히는 데 사용됩니다. 모델링 기반 조사는 응용 분야를 설명하기 위해 미세 유체 장치의 예와 결합됩니다. 

키워드 : 유전 영동 ; 전기 유체 역학 ; 전기 습윤 ; 랩 온어 칩 ; 미세 유체 ; 모델링 ; 수치 시뮬레이션 ; 반사 디스플레이

Droplet processing array Droplet based BioFlip
igure 1. Example of droplet-based digital microfluidics architecture. Above is an elevation view showing the layered structure of the chip. Below is a diagram illustrating the system (Adapted from [4]).
Figure 2. Simulation of droplet separation by EWOD
Figure 2. Simulation of droplet separation by EWOD. The top two figures illustrate the device configuration. Electric voltages are applied to all four electrodes embedded in the insulating material. The bottom left figure shows transient simulation solution. It illustrates the process of separating one droplet into two via EWOD. The bottom right figure shows the electric potential distribution inside the device. The color indicates the electric potential; the iso-potential surfaces are also drawn. The image shows the electric field is absent within the droplet body indicating the droplet is either conductive or highly polarizable.
Figure 4. Transient sequence of the Taylor cone formation
Figure 4. Transient sequence of the Taylor cone formation: simulation and experiment comparison. Experimental images are shown in the top row. Simulation results are shown in the bottom row. Their correspondence is indicated by the vertical alignment (Adapted from [4]).
Figure 6. Simulation of charge screening effect using a parallel-plate cell
Figure 6. Simulation of charge screening effect using a parallel-plate cell. Top-left image shows the electric current as function of time and driving voltage, top-right image shows the evolution of the species concentration as function of time and space, the bottom image shows the electric current readout after switching the applied voltage.
Figure 7. Transient simulation of electrohydrodynamic instability and the development of the cellular convective flow pattern.
Figure 7. Transient simulation of electrohydrodynamic instability and the development of the cellular convective flow pattern.
Figure 3. Simulation of dielectrophoresis driven axon migration
Figure 3. Simulation of dielectrophoresis driven axon migration. The set of small images on the left shows a transient simulation of single axon migration under an electric field generated by a pin electrode. The image on the right is a snapshot of a simulation where two axons are fused by dielectrophoresis using a pin electrode. Axons are outlined in white. Also shown are the iso-potential curves.

References

  1. Muller, RS. MEMS: Quo vadis in century XXI. Microelectron. Eng 200053(1–4), 47–54. [Google Scholar]
  2. Reyes, DR; Iossifidis, D; Auroux, PA; Manz, A. Micro total analysis systems. 1. Introduction, theory, and technology. Anal.Chem 200274, 2623–2636. [Google Scholar]
  3. Levy, U; Shamai, R. Tunable optofluidic devices. Microfluid. Nanofluid 20084, 97–105. [Google Scholar]
  4. Zeng, J; Korsmeyer, FT. Principles of droplet electrohydrodynamics for lab-on-a-chip. Lab Chip 20044, 265–277. [Google Scholar]
  5. Fair, RB. Digital microfluidics: Is a true lab-on-a-chip possible? Microfluid. Nanofluid 20073, 245–281. [Google Scholar]
  6. Pollack, MG; Fair, RB; Shenderov, AD. Electrowetting-based actuation of liquid droplets for microfluidic applications. Appl. Phys. Lett 200077(11), 1725–1726. [Google Scholar]
  7. Peykov, V; Quinn, A; Ralston, J. Electrowetting: A model for contact-angle saturation. Colloid Polym. Sci 2000278, 789–793. [Google Scholar]
  8. Verheijen, HJJ; Prins, MWJ. Reversible electrowetting and trapping of charge: Model and experiments. Langmuir 199915, 6616–6620. [Google Scholar]
  9. Mugele, F; Baret, J. Electrowetting: From basics to applications. J. Phys. Condens. Matter 200517, R705–R774. [Google Scholar]
  10. Quilliet, C; Berge, B. Electrowetting: A recent outbreak. Curr. Opin. Colloid Interface Sci 20016, 34–39. [Google Scholar]
  11. Probstein, RF. Physicochemical Hydrodynamics; Wiley: New York, NY, USA, 1994. [Google Scholar]
  12. Koo, J; Kleinstreuer, C. Liquid flow in microchannels: Experimental observations and computational analyses of microfluidics effects. J. Micromech. Microeng 200313, 568–579. [Google Scholar]
  13. Hu, G; Li, D. Multiscale phenomena in microfluidics and nanofluidics. Chem. Eng. Sci 200762, 3443–3454. [Google Scholar]
  14. Haus, HA; Melcher, JR. Electromagnetic Fields and Energy; Prentice-Hall: Englewood Cliffs, NJ, USA, 1989. [Google Scholar]
  15. Leal, LG. Laminar Flow and Convective Transport Processes: Scaling Principles and Asymptotic Analysis; Butterworth-Heinemann: Oxford, UK, 1992. [Google Scholar]
  16. Collins, RT; Harris, MT; Basaran, OA. Breakup of electrified jets. J. Fluid Mech 2007588, 75–129. [Google Scholar]
  17. Sista, R; Hua, Z; Thwar, P; Sudarsan, A; Srinivasan, V; Eckhardt, A; Pollack, M; Pamula, V. Development of a digital microfluidic platform for point of care testing. Lab Chip 20088, 2091–2104. [Google Scholar]
  18. Zeng, J. Modeling and simulation of electrified droplets and its application to computer-aided design of digital microfluidics. IEEE Trans. Comput. Aid. Des. Integr. Circ. Syst 200625(2), 224–233. [Google Scholar]
  19. Walker, SW; Bonito, A; Nochetto, RH. Mixed finite element method for electrowetting on dielectric with contact line pinning. Interface. Free Bound 201012, 85–119. [Google Scholar]
  20. Eck, C; Fontelos, M; Grün, G; Klingbeil, F; Vantzos, O. On a phase-field model for electrowetting. Interface. Free Bound 200911, 259–290. [Google Scholar]
  21. Gascoyne, PRC; Vykoukal, JV. Dielectrophoresis-based sample handling in general-purpose programmable diagnostic instruments. Proc. IEEE 200492(1), 22–42. [Google Scholar]
  22. Jones, TB; Gunji, M; Washizu, M. Dielectrophoretic liquid actuation and nanodroplet formation. J. Appl. Phys 200189(3), 1441–1448. [Google Scholar]
  23. Sretavan, D; Chang, W; Keller, C; Kliot, M. Microscale surgery on single axons. Neurosurgery 200557(4), 635–646. [Google Scholar]
  24. Pohl, HA; Crane, JS. Dielectrophoresis of cells. Biophys. J 197111, 711–727. [Google Scholar]
  25. Melcher, JR; Taylor, GI. Electrohydrodynamics: A review of the role of interfacial shear stresses. Annu. Rev. Fluid Mech 19691, 111–146. [Google Scholar]
  26. Saville, DA. Electrohydrodynamics: The taylor-melcher leaky-dielectric model. Annu. Rev. Fluid Mech 199729, 27–64. [Google Scholar]
  27. Schultz, GA; Corso, TN; Prosser, SJ; Zhang, S. A fully integrated monolithic microchip electrospray device for mass spectrometry. Anal. Chem 200072(17), 4058–4063. [Google Scholar]
  28. Killeen, K; Yin, H; Udiavar, S; Brennen, R; Juanitas, M; Poon, E; Sobek, D; van de Goor, T. Chip-MS: A polymeric microfluidic device with integrated mass-spectrometer interface. Micro Total Anal. Syst 2001, 331–332. [Google Scholar]
  29. Dukhin, SS. Electrokinetic phenomena of the second kind and their applications. Adv. Colloid Interface Sci 199135, 173–196. [Google Scholar]
  30. Wang, Y-C; Stevens, AL; Han, J. Million-fold preconcentration of proteins and peptides by nanofluidic filter. Anal. Chem 200577(14), 4293–4299. [Google Scholar]
  31. Kim, SJ; Wang, Y-C; Han, J. Nonlinear electrokinetic flow pattern near nanofluidic channel. Micro Total Anal. Syst 20061, 522–524. [Google Scholar]
  32. Comiskey, B; Albert, JD; Yoshizawa, H; Jacobson, J. An electrophoretic ink for all-printed reflective electronic displays. Nature 1998394(6690), 253–255. [Google Scholar]
  33. Beunis, F; Strubbe, F; Neyts, K; Bert, T; De Smet, H; Verschueren, A; Schlangen, L. P-39: Electric field compensation in electrophoretic ink display. In Proceedings of the Twenty-fifth International Display Research Conference—Eurodisplay 2005; Edinburgh, UK, 19–22 2005; pp. 344–345. [Google Scholar]
  34. Strubbe, F; Verschueren, ARM; Schlangen, LJM; Beunis, F; Neyts, K. Generation current of charged micelles in nonaqueous liquids: Measurements and simulations. J. Colloid Interface Sci 2006300, 396–403. [Google Scholar]
  35. Hsu, MF; Dufresne, ER; Weitz, DA. Charge stabilization in nonpolar solvents. Langmuir 200521, 4881–4887. [Google Scholar]
  36. Hayes, RA; Feenstra, BJ. Video-speed electronic paper based on electrowetting. Nature 2003425, 383–385. [Google Scholar]
  37. Chakrabarty, K; Su, F. Digital Microfluidic Biochips: Synthesis, Testing, and Reconfiguration Techniques; CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar]
  38. Chakrabarty, K; Fair, RB; Zeng, J. Design tools for digital microfluidic biochips: Towards functional diversification and more than Moore. IEEE Trans.CAD Integr. Circ. Syst 201029(7), 1001–1017. [Google Scholar]
Damascene templates

High-Rate Nanoscale Offset Printing Process Using Directed Assembly and Transfer of Nanomaterials

지난 10 년 동안 나노 크기의 재료와 공정을 제품에 통합하는 데 제한적인 성공을 거두면서 나노 기술에 상당한 투자와 발전이 있었습니다.

잉크젯, 그라비아, 스크린 프린팅과 같은 접근 방식은 나노 물질을 사용하여 구조와 장치를 만드는 데 사용됩니다. [1–7] 그러나 상당히 느리고 µm 스케일 분해능 만 제공 할 수 있습니다. 다양한 모양과 크기의 100nm 미만의 특징을 달성하기 위해 딥펜 리소그래피 (DPN) [8-11] 및 소프트 리소그래피 [12-16]와 같은 다양한 기술이 개발되고 광범위하게 연구되었습니다.

DPN은 직접 쓰기 기술로, atomic force microscopy 현미경 팁을 사용하여 다양한 기판에 여러 패턴을 생성합니다. DPN을 사용한 확장 성을 해결하기 위해 단일 AFM 팁 대신 2D 형식으로 배포 된 AFM (Atomic Force Microscopy) 팁 [17,18]이 사용되었습니다. 소프트 리소그래피에서는 나노 물질을 포함하는 잉크로 적셔진 원하는 릴리프 패턴을 가진 경화된 엘라스토머가 기판과 컨 포멀 접촉하게 되며, 여기서 패턴 화 된 나노 물질이 전달되어 기판에서 원하는 특징을 달성합니다.

이 논문에서는 작거나 큰 영역에서 몇 분 만에 나노, 마이크로 또는 거시적 구조를 인쇄 할 수 있는 다중 스케일 오프셋 인쇄 접근 방식을 제시합니다. 이 프로세스는 나노 입자 (NP), 탄소 나노 튜브 (CNT) 또는 용해 된 폴리머를 포함하는 서스펜션 (잉크)에서 나노 물질의 전기 영동 방향 조립을 사용하여 특별히 제작 된 재사용 가능한 Damascene 템플릿에 패턴을 “inking” 하는 것으로 시작됩니다. 이 잉크 프로세스는 실온과 압력에서 수행됩니다.

두 번째 단계는 템플릿에 조립된 나노 물질이 다른 기판으로 전송되는 “printing”로 구성됩니다. 전송 프로세스가 끝나면 템플릿은 다음 조립 및 전송주기에서 즉시 재사용 할 수 있습니다. 이 오프셋 인쇄 프로세스를 통해 NP (폴리스티렌 라텍스 (PSL), 실리카,은) 및 CNT (다중 벽 및 단일 벽)를 100μm에서 500nm까지의 크기 범위를 가진 패턴에 조립하고 유동성 기판에 성공적으로 옮깁니다.

다양한 나노 물질을 다양한 아키텍처로 조립하기 위해 템플릿 유도 유동, 대류, 유전 영동 (DEP) 및 전기 영동 조립과 같은 몇 가지 직접 조립 프로세스가 조사되었습니다. 모세관력이 지배적인 조립 메커니즘인 유체 조립 공정은 다양한 나노 물질에 적용 할 수 있습니다.

대류 조립 공정은 현탁 메니 스커 스와 증발을 활용하여 단일 나노 입자 분해능으로 정밀 조립을 가능하게 합니다. 이러한 조립 공정 중 많은 부분이 트렌치와 같은 마이크로 및 나노 스케일 기능으로 고해상도의 직접 조립을 보여 주었지만, 확장성 부족, 느린 공정 속도 및 반복성과 같은 많은 단점이 있습니다.

DEP 어셈블리는 NP와 전극 사이에 고배향 탄소 나노 튜브 어셈블리를 사용하여 나노 와이어 및 구조를 만드는 데 사용되었습니다. 조립 효율은 전기장과 전기장 구배에 상당한 영향을 미치는 전극의 기하학적 구조와 간격에 크게 좌우됩니다. 전기 영동 기반 조립 공정은 유체 조립에 비해 훨씬 짧은 시간에 전도성 표면에 표면 전하를 가진 나노 물질을 조립하는 것을 포함합니다. [34–37]

그러나 전기 영동 조립은 조립이 전도성 표면에 발생해야 하므로 다양한 장치를 만드는 데 실용적이지 않습니다. 한 가지 해결책은 원하는 나노 스케일 구조를 기반으로 전도성 패턴이 있는 템플릿을 만들고, 전기 영동 공정을 사용하여 패턴 위에 나노 물질을 조립 한 다음 조립 된 구조를 수용 기판에 옮기는 것입니다.

그림 1a와 같이 절연 필름에 전도성 와이어와 같은 패턴 구조가있는 기존 템플릿을 사용하면 나노 스케일 와이어의 잠재적 인 큰 강하로 인해 어셈블리가 불균일 해지며 대부분의 입자는 그림 1에 표시된 마이크로 와이어 b. 또한 NP는 3D 와이어의 측벽에도 조립되므로 바람직하지 않습니다. 또한 나노 스케일 와이어와 템플릿 사이의 작은 접촉 면적으로 인해 나노 스케일 와이어는 이송 과정에서 쉽게 벗겨집니다.

Damascene templates
Figure 1. Damascene templates: a) A schematic of a conventional wire template used for electrophoretic assembly. In these templates nanowire are connected to a micrometer scale electrodes, which are in turn connected, to a large metal pad through which the potential is applied. b) SEM images of a typical nanoparticle assembly result obtained for confi guration shown in (a). c) A schematic of a Damascene template where all of the wires (nano- or micrometer scale) and the metal pad are connected to a conductive fi lm underneath the insulating fi lm. d) A schematic of Damascene template fabrication. Inset is artifi cially colored cross-sectional SEM image showing the metal nanowires to be at the same height as that of the SiO 2 and showing the conductive fi lm underneath the insulator. e) An optical image of a 3 inch Damascene template.
Offset printing
Figure 2. Offset printing: a) A schematic of the nanoscale offset printing approach. The insulating (SiO 2 ) surface of the Damascene template is selectively coated with a hydrophobic SAM (OTS). Using electrophoresis, nanomaterials are assembled on the conductive patterns of the Damascene template (“inking”), which are then transferred to a recipient substrate (“printing”). After the transfer, the template is ready for the next assembly and transfer cycle. b) SEM image of 50 nm PSL particles assembly with high density on 1 µm wide electrodes. c) Silica particles (20 nm) assembly on crossbar 2D patterns demonstrating the versatility of the Damascene template. Inset fi gure is a high-resolution image of assembled silica particles. d) SEM image of assembled SWNTs on micrometer scale patterns. e) MWNTs assembled on 100 µm features. f) Cellulose assembled on 2 µm electrodes. g) SWNTs assembled in cross bar architecture patterns. h) Flexible devices with array of transferred SWNTs and metal electrodes (printed on PEN). Inset is the microscopy image of two electropads and transferred SWNTs on PEN fi lm.
Analysis of nanomaterial assembly on electrodes
Figure 3. Analysis of nanomaterial assembly on electrodes

이것은 또한 그림 3b에 표시된대로 유한 체적 모델링 (Flow 3D)을 사용하는 전기장 윤곽 시뮬레이션 결과에 의해 확인됩니다. 전기장 강도의 윤곽은 전도성 패턴의 가장자리에있는 전기장이 중앙에있는 것보다 더 강하다는 것을 나타냅니다. 그러나 적용된 전위가 2.5V로 증가하면 그림 3c에 표시된대로 100nm 실리카 입자가 Damascene 템플릿을 가로 질러 전도성 패턴의 표면에 완전히 조립되어 조립을위한 임계 전기장 강도에 도달했음을 나타냅니다. 정렬 된 SWNT는 여과 전달 경로를 피하고 나노 튜브 사이의 접합 저항을 최소화하여 소자 성능의 최소 변화를 가져 오기 때문에 많은 응용 분야에서 고도로 조직화 된 SWNT가 필요합니다.

References

[1] M.Abulikemu, E.H.Da’as, H.Haverinen, D.Cha, M.A.Malik, G.E.Jabbour, Angew.Chem.Int.Ed.2014, 53, 599.
[2] a) Z.Lu, M.Layani, X.Zhao, L.P.Tan, T.Sun, S.Fan, Q.Yan, S.Magdassi, H.H.Hng, Small 2014, 10, 3551; b) H.Ko, J.Lee, Y.Kim, B.Lee, C.H.Jung, J.H.Choi, O.S.Kwon, K.Shin, Adv.Mater.2014, 26, 2286.
[3] C.J.Hansen, R.Saksena, D.B.Kolesky, J.J.Vericella, S.J.Kranz, G.P.Muldowney, K.T.Christensen, J.A.Lewis, Adv.Mater.2013, 25, 2.
[4] F.C.Krebs, N.Espinosa, M.Hösel, R.R.Søndergaard, M.Jørgensen, Adv.Mater.2014, 26, 29.
[5] W.Honda, S.Harada, T.Arie, S.Akita, K.Takei, Adv.Funct.Mater. 2014, 24, 3298.
[6] R.Guo, Y.Yu, Z.Xie, X.Liu, X.Zhou, Y.Gao, Z.Liu, F.Zhou, Y.Yang, Z.Zheng, Adv.Mater.2013, 25, 3343.
[7] A.Dzwilewski, T.Wågberg, L.Edman, J.Am.Chem.Soc.2009, 131, 4006.
[8] R.D.Piner, J.Zhu, F.Xu, S.Hong, C.A.Mirkin, Science 1999, 283, 661.
[9] J.-H.Lim, C.A.Mirkin, Adv.Mater.2002, 14, 1474.
[10] X.Liu, L.Fu, S.Hong, V.P.Dravid, C.A.Mirkin, Adv.Mater.2002,14, 231.
[11] D.A.Weinberger, S.Hong, C.A.Mirkin, B.W.Wessels, T.B.Higgins, Adv.Mater.2000, 12, 1600.
[12] J.P.Rolland, E.C.Hagberg, G.M.Denison, K.R.Carter, J.M.DeSimone, Angew.Chem.2004, 116, 5920.
[13] T.Granlund, T.Nyberg, L.S.Roman, M.Svensson, O.Inganäs, Adv.Mater.2000, 12, 269.
[14] Y.Xia, G.M.Whitesides, Annu.Rev.Mater.Sci.1998, 28, 153.
[15] W.S.Beh, I.T.Kim, D.Qin, Y.Xia, G.M.Whitesides.Adv.Mater. 1999, 11, 1038.
[16] Y.Yin, B.Gates, Y.Xia.Adv.Mater.2000, 12, 1426.
[17] K.Salaita, Y.Wang, J.Fragala, R.A.Vega, C.Liu, C.A.Mirkin,Angew.Chem.2006, 118, 7378.
[18] D.Bullen, S.-W.Chung, X.Wang, J.Zou, C.A.Mirkin, C.Liu, Appl.Phys.Lett.2004, 84, 789.
[19] Y.L.Kim, H.Y.Jung, S.Park, B.Li, F.Liu, J.Hao, Y.-K.Kwon, Y.J.Jung, S.Kar, Nat.Photonics 2014, 8, 239.
[20] X.Xiong, L.Jaberansari, M.G.Hahm, A.Busnaina, Y.J.Jung, Small 2007, 3, 2006.
[21] A.B.Marciel, M.Tanyeri, B.D.Wall, J.D.Tovar, C.M.Schroeder, W.L.Wilson, Adv.Mater.2013, 25, 6398.
[22] J.T.Wang, J.Wang, J.J.Han, Small 2011, 7, 1728.
[23] S.Y.Lee, S.H.Kim, H.Hwang, J.Y.Sim, S.M.Yang, Adv.Mater. 2014, 26, 2391.
[24] J.Y.Oh, J.T.Park, H.J.Jang, W.J.Cho, M.S.Islam, Adv.Mater. 2014, 26, 1929.
[25] K.W.Song, R.Costi, V.Bulovi, Adv.Mater.2013, 25, 1420.
[26] P.Maury, M.Escalante, D.N.Reinhoudt, J.Huskens, Adv.Mater. 2005, 17, 2718.
[27] Y.Xia, Y.Yin, Y.Lu, J.McLellan, Adv.Funct.Mater.2003, 13, 907.
[28] L.Jaber-Ansari, M.G.Hahm, S.Somu, Y.E.Sanz, A.Busnaina, Y.J.Jung, J.Am.Chem.Soc.2008, 131, 804.
[29] T.Kraus, L.Malaquin, H.Schmid, W.Riess, N.D.Spencer, H.Wolf,Nat.Nanotechnol.2007, 2, 570.
[30] K.D.Hermanson, S.O.Lumsdon, J.P.Williams, E.W.Kaler, O.D.Velev, Science 2001, 294, 1082.
[31] H.-W.Seo, C.-S.Han, D.-G.Choi, K.-S.Kim, Y.-H.Lee, Microelectron.Eng.2005, 81, 83.
[32] E.M.Freer, O.Grachev, X.Duan, S.Martin, D.P.Stumbo, Nat.Nanotechnol.2010, 5, 525.
[33] D.Xu, A.Subramanian, L.Dong, B.J.Nelson, IEEE Trans.Nanotechnol.2009, 8, 449.
[34] X.Xiong, P.Makaram, A.Busnaina, K.Bakhtari, S.Somu, N.McGruer, J.Park, Appl.Phys.Lett.2006, 89, 193108.
[35] R.C.Bailey, K.J.Stevenson, J.T.Hupp, Adv.Mater.2000, 12, 1930.
[36] Q.Zhang, T.Xu, D.Butterfi eld, M.J.Misner, D.Y.Ryu, T.Emrick, T.P.Russell, Nano Lett.2005, 5, 357.
[37] E.Kumacheva, R.K.Golding, M.Allard, E.H.Sargent, Adv.Mater. 2002, 14, 221.
[38] M.Wei, Z.Tao, X.Xiong, M.Kim, J.Lee, S.Somu, S.Sengupta, A.Busnaina, C.Barry, J.Mead, Macromol.Rapid Commun.2006, 27, 1826.
[39] a) D.Schwartz, S.Steinberg, J.Israelachvili, J.Zasadzinski, Phys.Rev.Lett.1992, 69, 3354; b) W.Yang, P.Thordarson, J.J.Gooding, S.P.Ringer, F.Braet, Nanotechnology 2007, 18, 412001.
[40] S.Siavoshi, C.Yilmaz, S.Somu, T.Musacchio, J.R.Upponi, V.P.Torchilin, A.Busnaina, Langmuir 2011, 27, 7301.
[41] E.Artukovic, M.Kaempgen, D.Hecht, S.Roth, G.Grüner, NanoLett.2005, 5, 757.
[42] L.Hu, D.Hecht, G.Grüner, Nano Lett.2004, 4, 2513.
[43] M.Fuhrer, J.Nygård, L.Shih, M.Forero, Y.G.Yoon, H.J.Choi, J.Ihm, S.G.Louie, A.Zettl, P.L.McEuen, Science 2000, 288,
494.
[44] J.J.Gooding, A.Chou, J.Liu, D.Losic, J.G.Shapter, D.B.Hibbert,Electrochem.Commun.2007, 9, 1677.
[45] A.Chou, T.Böcking, N.K.Singh, J.J.Gooding, Chem.Commun. 2005, 7, 842.
[46] D.Hines, V.Ballarotto, E.Williams, Y.Shao, S.Solin, J.Appl.Phys. 2007, 101, 024503.
[47] H.Park, A.Afzali, S.-J.Han, G.S.Tulevski, A.D.Franklin, J.Tersoff, J.B.Hannon, W.Haensch, Nat.Nanotechnol.2012, 7, 787.
[48] S.Somu, H.Wang, Y.Kim, L.Jaberansari, M.G.Hahm, B.Li, T.Kim, X.Xiong, Y.J.Jung, M.Upmanyu, A.Busnaina, ACS Nano 2010, 4, 4142.
[49] L.Jaber-Ansari, M.G.Hahm, T.H.Kim, S.Somu, A.Busnaina, Y.J.Jung, Appl.Phys.A 2009, 96, 373.
[50] B.Li, M.G.Hahm, Y.L.Kim, H.Y.Jung, S.Kar, Y.J.Jung, ACS Nano 2011, 5, 4826.
[51] B.Li, H.Y.Jung, H.Wang, Y.L.Kim, T.Kim, M.G.Hahm, A.Busnaina, M.Upmanyu, Y.J.Jung, Adv.Funct.Mater.2011, 21, 1810.
[52] M.A.Meitl, Z.T.Zhu, V.Kumar, K.J.Lee, X.Feng, Y.Y.Huang, I.Adesida, R.G.Nuzzo, J.A.Rogers, Nat.Mater.2005, 5, 33.
[53] F.N.Ishikawa, H.Chang, K.Ryu, P.Chen, A.Badmaev, L.GomezDe Arco, G.Shen, C.Zhou, ACS Nano 2008, 3, 73.
[54] N.Inagaki, Plasma Surface Modifi cation and Plasma Polymerization, CRC, Boca Raton, FL, USA 1996.
[55] E.Liston, L.Martinu, M.Wertheimer, J.Adhes.Sci.Technol.1993, 7, 1091.
[56] T.Tsai, C.Lee, N.Tai, W.Tuan, Appl.Phys.Lett.2009, 95, 013107.
[57] J.G.Bai, Z.Z.Zhang, J.N.Calata, G.-Q.Lu, IEEE Trans.Compon.Packag.Technol.2006, 29, 589.
[58] J.G.Toffaletti, Crit.Rev.Clin.Lab.Sci.1991, 28, 253.
[59] J.-L.Vincent, P.Dufaye, J.Berré, M.Leeman, J.-P.Degaute, R.J.Kahn, Crit.Care Med.1983, 11, 449.
[60] R.Henning, M.Weil, F.Weiner, Circ.Shock 1982, 9, 307.

연료 탱크 슬로싱

시뮬레이션 사례 설명

이 예는 제트 전투기 연료 탱크 내 연료 슬로싱을 나타냅니다. 이 시뮬레이션을 통해 엔지니어는 탱크 내 연료 모션을 제어하는 배플의 성능을 평가하고 적절한 제어 시스템을 설계할 수 있습니다.

자세한 내용이 궁금하시면 언제든지 기술지원팀에 연락주시기 바랍니다.

This example represents fuel sloshing in a jet fighter fuel tank. The simulation allows engineers to evaluate the performance of the baffles in controlling the fuel motion in the tank and to design appropriate control systems.

Fuel Tank Sloshing
벨기에 Zele에서 나온 WWTP의 개략도

활성화 된 슬러지 모델링

Activated Sludge Model

폐수 처리 플랜트 (WWTP) 내부의 생화학 적 반응 및 유체 역학에 대한 자세한 이해는 설계자와 엔지니어가 새로운 플랜트 설계를 평가하고, 관리 결정을 정량화하고, 새로운 제어 계획을 개발하고, 안전한 작업자 교육을 제공하는 데 도움이 될 수 있습니다. 이 블로그에서는 독자들에게 대규모 생화학 반응 시스템을 동적으로 해결 하는 FLOW-3D 의 새로운 ASM (Activated Sludge Model)을 소개합니다.

폭기조

폭기조는 대부분의 생화학 반응이 WWTP의 2 차 처리 부분에서 발생하는 곳입니다. 일반적으로 폭기 탱크는 대부분의 생화학 반응이 완료되는 데 걸리는 시간을 허용하는 긴 경로를 가지고 있습니다. 종이 폭기조의 전체 길이를 횡단하는 데 걸리는 시간을 잔류 시간이라고합니다. 폭기조에 산소가 주입되어 폐수에서 박테리아가 증식합니다. 박테리아는 산소를 사용하여 물에있는 폐기물을 분해하고 그렇게하면서 플록 또는 슬러지 블랭킷이라고하는 응집체를 형성합니다. 활성화 된 슬러지의 일부는 폐수의 생화학 적 처리를 더욱 촉진하기 위해 폭기조로 다시 재활용됩니다.

벨기에 Zele에서 나온 WWTP의 개략도
벨기에 Zele에서 나온 WWTP의 개략도

생화학 반응의 표준 시스템

국제 물 협회 (IWA)는 지난 40 년간 생화학 적 반응을 설명하는 세 가지 주요 수학적 시스템을 제안했다. 이러한 각 시스템 인 ASM-1, ASM-2 및 ASM-3은 폭기조 내부의 다양한 종의 성장 및 붕괴 역학을 다양한 세부 수준으로 포착합니다. ASM-3이 가장 포괄적입니다. 첫 번째 시스템 인 ASM-1은 아래 표 형식과 확장 형식으로 표시됩니다.

결합 편미분 방정식의 확장 시스템으로서의 생화학 반응의 ASM-1 시스템
결합 편미분 방정식의 확장 시스템으로서의 생화학 반응의 ASM-1 시스템

ASM 솔버 기능

대부분의 생화학 반응은 Monod 모델 또는 유사한 모델을 기반으로합니다. Monod 모델은 미생물의 성장 및 붕괴 속도를 예측하는 수학적 모델이며 간단한 방정식으로 설명됩니다.

여기서 a 와 k 는 최대 비 성장률 상수이고 기질 농도는 최대 비 성장률의 절반에 해당합니다. C 는 시간에 따라 변화하는 미생물 종의 농도 t 입니다. Monod 모델은 종의 농도에 따라 반응의 순서를 동적으로 변경하는 특성이 있습니다.

For C   >> A는 , 변화율 C는  0 차에 접근한다.

For C   << a는 , 변화율 C는 일차 접근한다.

이 모든 것은 미생물 종의 농도가 높으면 썩고 자라는 속도가 빨라지고, 종의 양이 적으면 썩거나 자라는 속도가 느리다는 것입니다. Monod 방정식의 해는 다음과 같이 Lambert 함수에 의해 제공됩니다.

간단한 Monod 방정식에 대한 분석 솔루션과 FLOW-3D 솔루션의 비교
간단한 Monod 방정식에 대한 분석 솔루션과 FLOW-3D 솔루션의 비교

생화학 반응을 설명하는 표준 시스템에는 Monod 용어의 긴 사슬이 포함되어 있습니다. FLOW-3D 의 ASM 모델은 WWTP에서 박테리아 종의 Monod 기반 성장 및 붕괴를 완벽하게 추적 할 수 있습니다. ASM 모델은 FLOW-3D 의 유체 역학 솔버 와 통합되어 속도 및 압력 장을 기반으로 한 박테리아의 움직임이 성장 및 붕괴 속도와 결합 될 수 있습니다.

FLOW-3D 의 ASM 솔버 결과가 벨기에 Zele의 폐수 처리장 (WWTP)에서 배출 될 때 다양한 유입수 종 농도의 붕괴 및 성장에 대해 보여줄 것 입니다. 종 및 유체 역학 계산을 정확하게 추적하면 폐수 처리 전문가가 정량적으로 뒷받침되는 설계 및 운영 결정을 내릴 수 있습니다.

Zele WWTP

Zele WWTP는 1983 년 50,000 명의 주민을 위해 벨기에에서 건설되었습니다. 일반적으로이 WWTP의 유입수는 가정용 폐수 40 %와 산업 폐수 60 %로 구성됩니다. 1 차 처리 공정 후 유입수는 생물학적 활성 슬러지 처리장으로 흘러 재활용 활성 슬러지와 혼합됩니다.

벨기에 Zele에서 나온 WWTP의 개략도 [2]. 녹색 상자는 2 차 처리 과정을 나타냅니다.
벨기에 Zele에서 나온 WWTP의 개략도 [2]. 녹색 상자는 2 차 처리 과정을 나타냅니다.

활성 오니 조 또는 폭기조는 약 400 m의 레인 6으로 분할되어 하나의 플러그 유동 폭기조 구성 3 각. 폭기조에서 나오는 유출 물은 각각 2050 m 3 용적의 2 개의 2 차 정화기 (SC1 및 SC2)로 이동합니다 . 최종 폐수는 인근 하천으로 배출됩니다. 2 차 정화기 아래에서 활성 슬러지의 일부는 폭기조로 다시 재활용되어 2 차 처리의 효율성을 높입니다.

우리는 2 차 처리 구성 요소의 기하학적 구조와 다양한 종의 유입 농도에 대한 자세한 정보를 이용할 수 있기 때문에 사례 연구를 위해이 WWTP를 선택했습니다. 정보는 상세하지만 완전하지는 않으며이 불완전한 정보는 폐수 농도에 중대한 영향을 미칠 것이며 나중에 논의 할 것입니다.

기하학, 메싱 및 물리학

지오메트리 생성 및 메싱은 간단했습니다. FLOW-3D 에는 완전한 WWTP를 완전히 정의하는 데 사용 된 기본 지오메트리 모양 모음이 있습니다. 이러한 모양은 생성하기 쉽고 외부 CAD 소프트웨어를 사용하여 생성 된 일부 지오메트리와 달리 오류가 없습니다. 마찬가지로, 구조화 된 그리드를 사용하면 구조화되지 않은 그리드 생성과 관련된 일반적인 오류를 처리하는 시간이 절약되었습니다.

폭기조 내부의 물리학은 복잡하며 질량 및 운동량 보존 방정식 (Navier-Stokes 방정식), 종 수송, 반응 역학, 산소 용해 및 연속 밀도 평가의 완전한 시스템을 해결해야합니다. FLOW-3D 는 가장 정확한 계산을 위해 완전히 결합 된 방식으로 이러한 모든 물리학을 설명합니다.

FLOW-3D의 Zele WWTP 설정. 화살표는 흐름 방향을 나타내며 유입수는 녹색 화살표의 시작 부분에서 도메인으로 들어갑니다.
FLOW-3D의 Zele WWTP 설정. 화살표는 흐름 방향을 나타내며 유입수는 녹색 화살표의 시작 부분에서 도메인으로 들어갑니다.

세 가지 표준 수학적 모델 인 ASM-1, ASM-2 및 ASM-3 중에서 연구자들은이 WWTP에서 ASM-1 수학적 모델을 사용합니다. 이는 간단하면서도 많은 중요한 생화학 과정을 다루기 때문입니다. ASM-1 모델은 일반적으로 폐수에서 발견되거나 처리 과정에서 생성되는 13 종의 진화를 고려합니다 [표 1].

종 IDZele의 초기 유입 농도 (mg / l)
가용성 불활성 유기물SI7.5
쉽게 생분해되는 기질SS400.0
미립자 불활성 유기물XI40.0
천천히 생분해되는 기질XS40.0
활성 종속 영양 바이오 매스XB, H120.0
활성 독립 영양 바이오 매스XB, A5.0
바이오 매스 붕괴로 인한 미립자 제품XP0.0
산소SO0.0
질산염 및 아질산염 질소SNO0.0
암모늄 질소SNH15.0
용해성 생분해 성 유기 질소SND8.2
미립자 생분해 성 유기 질소XND11.3
알칼리도SALKNot included

표 1. 표준 ASM-1 수학 시스템의 종 목록과 Zele WWTP에서 측정 된 초기 유입수 농도. 이러한 초기 농도 중 일부는 추론되며 큰 불확실성이 관련 될 수 있습니다. S와 X는 각각 용해성 물질과 미립자 물질을 나타냅니다.

이들 종 각각은 반응하지 않는 불활성 종 (SI 및 XI)을 제외하고 하나 이상의 생화학 적 과정에 의존합니다. 불활성 종의 유입 및 유출 농도는 XI의 경우와 같이 침전으로 인해 달라질 수 있습니다. SALK는 WWTP에서 측정되지 않았기 때문에이 사례 연구에서 무시되었습니다.

관심 유출량

폐수 엔지니어가 관심을 갖는 주요 유출량은 총 화학적 산소 요구량 (COD tot ), 암모늄 질소 (SNH) 농도, 아질산염 및 질산염 질소 (SNO) 및 총 킬달 질소 (TKN)입니다.

  • COD tot = SI + SS + XI + XS
  • TKN ~ XND + SND + SNH

이 양은 처리 된 물의 전반적인 품질을 나타냅니다.

유출량측정 된 유입 농도 (mg / l)FLOW-3D 유출 농도 (mg / l)
CODtot600264.04
SNH1530.34
SNO01.86
TKN3537.28

총 COD, SNH 및 TKN의 농도는 폐수가 폭기조를 통과하여 WWTP를 빠져 나 가면서 감소해야합니다. 이 동작은 총 COD [표 2]에 대해 올바르게 예측되지만 SNH 및 TKN에 대해서는 그렇지 않습니다. SNO의 농도는 증가 할 것으로 예상되며 이는 ASM 솔버에 의해 정확하게 예측됩니다. 모든 폐수 종의 농도는 아래 애니메이션에 표시됩니다.

Zele WWTP에 있는 모든 종의 진화에 대한 시뮬레이션 결과

애니메이션은 Zele WWTP에있는 모든 종의 진화에 대한 시뮬레이션 결과를 보여줍니다.

WWTP 데이터에 대한 결과의 민감도

나는 폐수에서 일부 종의 잘못된 진화를 모델링의 가정과 누락된 WWTP 데이터에 기인합니다. 유입수에서 측정 된 종 농도의 불확실성; 초기 농도에 대한 정보 누락; 그리고 입자상 물질의 침강 특성에 대한 누락 된 데이터는 폐수의 종 농도에 영향을 미쳤을 가능성이 있습니다.

마찬가지로 불완전한 지오메트리 사양은 WWTP 내부의 유체 역학 계산의 정확성에 부정적인 영향을 미칠 수 있습니다. 또한 폭기조에 산소를 살포하는 것에 대한 정보는 부분적으로 만있었습니다. 산소는 다른 종의 부패와 성장에 큰 영향을 미치는 중요한 구성 요소입니다.

WWTP의 모든 데이터를 항상 측정 할 수있는 것은 아닙니다. 이러한 경우 보정 된 수치 모델을 가상 실험실로 효과적으로 사용하여 다양한 WWTP 설계를 테스트 할 수 있습니다. 이 사례 연구는 특히 폭기조에서 WWTP의 2 차 처리 부분에서 종의 농도를 추적 할 수 있음을 보여줍니다. 그리고 이것은 유체 역학 효과를 고려하면서 할 수 있습니다. 완전한 WWTP 데이터와 문제 사양이 존재하는 경우 엔지니어와 설계자는 WWTP 플랜트 운영 및 설계 최적화에 대해 더 나은 정보를 바탕으로 결정을 내릴 수 있습니다.

우리는 활성 슬러지 모델을 추가로 개발하고 보정하기 위해 폐수 처리 산업의 연구원 및 전문가와 협력 할 수 있습니다. 귀하의 WWTP 프로젝트 및 연구에 대해 논의하려면 adwaith@flow3d.com 으로 이메일을 보내 주십시오 .

참고 문헌

[1] Henze M., Lossdrecht M.C.M., Ekama G.A., Brdjanovic D., Biological Wastewater Treatment, Principles, Modelling and Design, IWA publishing 2008.

[2] Peterson B., Vanrollenghem P.A., Gernaey K., Henze M. (2002) Evaluation of an ASM-1 model calibration procedure on a municipal–industrial wastewater treatment plant, Journal of Hydroinformatics, 4(1): 15-38.

[3] Henze, M., Grady, C. P. L. Jr., Gujer, W., Marais, G. v. R. & Matsuo, T. (1987) Activated Sludge Model No. 1. IAWPRC Scientific and Technical Reports No. 1. London, UK.

Moving Boundaries: An Eulerian Approach

Moving Boundaries: An Eulerian Approach

많은 문제에서, 유체 및 고체 영역의 내부 경계가 그 안에서 이동할 수 있도록하면서 공간에 고정 된 그리드를 유지하는 것이 유리합니다. 이는 리 메싱의 필요성을 피할 수 있으므로 이러한 경계의 형태에 급격한 변화가 발생할 때마다 적절합니다. 메시 생성도 크게 단순화되었습니다.

고정 그리드 내에서 유체 인터페이스, 침전물, 응고 된 유체 및 탄성 재료의 경계 이동을 모델링하기위한 다양한 접근 방식이 표시됩니다. 유체 경계의 이동은 VOF (Volume-of-Fluid) 방법의 변형으로 수행되며, 각 계산 셀에서 유체의 양을 나타내는 양이 고정 메시를 통해 조정됩니다.

퇴적물의 침식 및 퇴적은 퇴적물 수색 모델을 사용하여 계산됩니다. 국부적 인 침식 속도는 패킹 된 퇴적물 / 유체 경계면에 존재하는 국부적 인 전단 응력을 기반으로하며, 증착은 Stokes 유동 근사치로 예측됩니다.

Emptying of gravure cell (same cell dimensions as filling case); a
three-dimensional perspective is shown. The transfer roll surface
(block at top) is moving away from the gravure roll at 0.5m/s. The
static contact of the fluid with all surfaces is 30°. The elapsed time
is 150

충진 층 경계면은 퇴적물 농도와 퇴적물의 포장 분율에 따라 달라집니다. 용융 금속은 온도가 빙점 아래로 떨어지면 굳을 수 있습니다. 응고 된 “유체”는 동결 및 용융을 유발하는 열유속의 양으로부터 결정된대로 표면이 증가하거나 수축하는 고체처럼 처리됩니다.

탄성 응력은 응고 된 재료 / 공기 인터페이스를 예측하는 VOF 방법을 사용하여 동일한 고정 그리드 내의 운동량 균형에 탄성 응력 계산을 추가하여 응고 된 영역에서 계산됩니다.

매우 일시적인 흐름 문제의 경우 유체와 공극 공간 사이 또는 두 개의 혼합 불가능한 유체 사이에있는 유체 인터페이스는 문제의 역학에 따라 자유롭게 움직여야합니다.

한 가지 해결책은 인터페이스와 함께 변형되는 메시를 만드는 것입니다. 이것은 시뮬레이션 중에 인터페이스의 형태가 거의 변경되지 않는 상황에서 잘 작동합니다. 그러나보다 일반적인 경우에는 시뮬레이션 중에 새 메시를 반복적으로 생성해야하거나 변경되지 않은 메시 내에서 자유 표면 경계를 생성하는 방법이 필요합니다. 이 작업은 후자를 제시합니다. VOF (Vol-of-fluid) 함수는 자유 표면의 위치를 추적하는 데 사용됩니다. 또한이 함수는 곡률을 계산하여 표면 장력의 영향을 예측하는 데 사용됩니다.

<원문보기> Moving-Boundaries-an-Eularian-Approach.pdf

Aerospace Electric Charge Distribution

Electric Charge Distribution

비행중 또는 급유 중에 항공기 연료가 슬로싱되면 전하가 발생하여 인터페이스의 fuel-vapor 혼합물이 전도성이됩니다. 과도 전위 및 필드 분포 분석은 연료 탱크의 최적 배출 위치를 식별하는 데 도움이됩니다. FLOW-3D를 사용한 이 전하 분포 시뮬레이션은 일련의 yaw, pitch 및 roll motion에서 회전을 통해 가속하는 동안 항공기 연료 탱크 내부의 연료 거동을 보여줍니다.

Cell Behavior

Cell Behavior

정밀하고 신중하게 제어되는 화학 반응성 구배를 생성 할 수있는 능력은 미세 유체학을 운동성, 화학성 및 소수의 미생물 집단에서 항생제에 대한 내성을 단기간에 진화시키고 개발하는 능력을 연구하는 이상적인 도구가 됩니다. FLOW-3D는 연구자들이 아래 예제에 표시된 것처럼 새롭고 더 나은 gradient generators를 고안하는 데 도움이 될 수 있습니다.

1-D Gradient generator with de-coupled convection and diffusion

FLOW-3D를 사용한 이 1-D 미세유체 팔레트 시뮬레이션에서는 표시된 흐름선을 통해 주 중앙 마이크로 채널에서 대류 셀의 깨끗한 디커플링을 확인할 수 있습니다. 이 흐름은 모두 대류 단위로만 제한되며 마이크로 채널로 유출되는 단 한 개의 흐름도 없어 대류 및 확산의 디커플링이 우수합니다. 소스 농도의 진화는 그림에서 볼 수 있으며, 애니메이션이 끝날 때쯤이면 눈에 띄게 일정해집니다.

This FLOW-3D simulation of a 2-D microfluidic palette demonstrates a spatio-temporal control on the generated gradients. The source and sink are rotated at an angular velocity. Also, after every t seconds, the active access port is deactivated and the next port is turned on. To see the live status of the diffusion inside the chamber, three line probes are placed in the simulation (marked in red, blue and black, respectively, in the bottom right window of the simulation).2-D 마이크로 유체 팔레트의 이  FLOW-3D 시뮬레이션은 생성된 그라데이션에 대한 spatio-temporal 제어를 보여줍니다. 소스 및 sink는 각 속도로 회전합니다. 또한 t초마다 활성 액세스 포트가 비활성화되고 다음 포트가 켜집니다. 챔버 내부의 확산 상태를 확인하기 위해 시뮬레이션에 세 개의 라인 프로브가 배치됩니다(시뮬레이션의 오른쪽 하단 창에 각각 빨간색, 파란색 및 검은색 표시).

Read the Microfluidic Palette – A Gradient Generator blog.

FLOW-3D CAST Bibliography

FLOW-3D CAST bibliography

아래는 FSI의 금속 주조 참고 문헌에 수록된 기술 논문 모음입니다. 이 모든 논문에는 FLOW-3D CAST 해석 결과가 수록되어 있습니다. FLOW-3D CAST를 사용하여 금속 주조 산업의 응용 프로그램을 성공적으로 시뮬레이션하는 방법에 대해 자세히 알아보십시오.

Below is a collection of technical papers in our Metal Casting Bibliography. All of these papers feature FLOW-3D CAST results. Learn more about how FLOW-3D CAST can be used to successfully simulate applications for the Metal Casting Industry.

33-20     Eric Riedel, Martin Liepe Stefan Scharf, Simulation of ultrasonic induced cavitation and acoustic streaming in liquid and solidifying aluminum, Metals, 10.4; 476, 2020. doi.org/10.3390/met10040476

20-20   Wu Yue, Li Zhuo and Lu Rong, Simulation and visual tester verification of solid propellant slurry vacuum plate casting, Propellants, Explosives, Pyrotechnics, 2020. doi.org/10.1002/prep.201900411

17-20   C.A. Jones, M.R. Jolly, A.E.W. Jarfors and M. Irwin, An experimental characterization of thermophysical properties of a porous ceramic shell used in the investment casting process, Supplimental Proceedings, pp. 1095-1105, TMS 2020 149th Annual Meeting and Exhibition, San Diego, CA, February 23-27, 2020. doi.org/10.1007/978-3-030-36296-6_102

12-20   Franz Josef Feikus, Paul Bernsteiner, Ricardo Fernández Gutiérrez and Michal Luszczak , Further development of electric motor housings, MTZ Worldwide, 81, pp. 38-43, 2020. doi.org/10.1007/s38313-019-0176-z

09-20   Mingfan Qi, Yonglin Kang, Yuzhao Xu, Zhumabieke Wulabieke and Jingyuan Li, A novel rheological high pressure die-casting process for preparing large thin-walled Al–Si–Fe–Mg–Sr alloy with high heat conductivity, high plasticity and medium strength, Materials Science and Engineering: A, 776, art. no. 139040, 2020. doi.org/10.1016/j.msea.2020.139040

07-20   Stefan Heugenhauser, Erhard Kaschnitz and Peter Schumacher, Development of an aluminum compound casting process – Experiments and numerical simulations, Journal of Materials Processing Technology, 279, art. no. 116578, 2020. doi.org/10.1016/j.jmatprotec.2019.116578

05-20   Michail Papanikolaou, Emanuele Pagone, Mark Jolly and Konstantinos Salonitis, Numerical simulation and evaluation of Campbell running and gating systems, Metals, 10.1, art. no. 68, 2020. doi.org/10.3390/met10010068

102-19   Ferencz Peti and Gabriela Strnad, The effect of squeeze pin dimension and operational parameters on material homogeneity of aluminium high pressure die cast parts, Acta Marisiensis. Seria Technologica, 16.2, 2019. doi.org/0.2478/amset-2019-0010

94-19   E. Riedel, I. Horn, N. Stein, H. Stein, R. Bahr, and S. Scharf, Ultrasonic treatment: a clean technology that supports sustainability incasting processes, Procedia, 26th CIRP Life Cycle Engineering (LCE) Conference, Indianapolis, Indiana, USA, May 7-9, 2019. 

93-19   Adrian V. Catalina, Liping Xue, Charles A. Monroe, Robin D. Foley, and John A. Griffin, Modeling and Simulation of Microstructure and Mechanical Properties of AlSi- and AlCu-based Alloys, Transactions, 123rd Metalcasting Congress, Atlanta, GA, USA, April 27-30, 2019. 

84-19   Arun Prabhakar, Michail Papanikolaou, Konstantinos Salonitis, and Mark Jolly, Sand casting of sheet lead: numerical simulation of metal flow and solidification, The International Journal of Advanced Manufacturing Technology, pp. 1-13, 2019. doi.org/10.1007/s00170-019-04522-3

72-19   Santosh Reddy Sama, Eric Macdonald, Robert Voigt, and Guha Manogharan, Measurement of metal velocity in sand casting during mold filling, Metals, 9:1079, 2019. doi.org/10.3390/met9101079

71-19   Sebastian Findeisen, Robin Van Der Auwera, Michael Heuser, and Franz-Josef Wöstmann, Gießtechnische Fertigung von E-Motorengehäusen mit interner Kühling (Casting production of electric motor housings with internal cooling), Geisserei, 106, pp. 72-78, 2019 (in German).

58-19     Von Malte Leonhard, Matthias Todte, and Jörg Schäffer, Realistic simulation of the combustion of exothermic feeders, Casting, No. 2, pp. 28-32, 2019. In English and German.

52-19     S. Lakkum and P. Kowitwarangkul, Numerical investigations on the effect of gas flow rate in the gas stirred ladle with dual plugs, International Conference on Materials Research and Innovation (ICMARI), Bangkok, Thailand, December 17-21, 2018. IOP Conference Series: Materials Science and Engineering, Vol. 526, 2019. doi.org/10.1088/1757-899X/526/1/012028

47-19     Bing Zhou, Shuai Lu, Kaile Xu, Chun Xu, and Zhanyong Wang, Microstructure and simulation of semisolid aluminum alloy castings in the process of stirring integrated transfer-heat (SIT) with water cooling, International Journal of Metalcasting, Online edition, pp. 1-13, 2019. doi.org/10.1007/s40962-019-00357-6

31-19     Zihao Yuan, Zhipeng Guo, and S.M. Xiong, Skin layer of A380 aluminium alloy die castings and its blistering during solution treatment, Journal of Materials Science & Technology, Vol. 35, No. 9, pp. 1906-1916, 2019. doi.org/10.1016/j.jmst.2019.05.011

25-19     Stefano Mascetti, Raul Pirovano, and Giulio Timelli, Interazione metallo liquido/stampo: Il fenomeno della metallizzazione, La Metallurgia Italiana, No. 4, pp. 44-50, 2019. In Italian.

20-19     Fu-Yuan Hsu, Campbellology for runner system design, Shape Casting: The Minerals, Metals & Materials Series, pp. 187-199, 2019. doi.org/10.1007/978-3-030-06034-3_19

19-19     Chengcheng Lyu, Michail Papanikolaou, and Mark Jolly, Numerical process modelling and simulation of Campbell running systems designs, Shape Casting: The Minerals, Metals & Materials Series, pp. 53-64, 2019. doi.org/10.1007/978-3-030-06034-3_5

18-19     Adrian V. Catalina, Liping Xue, and Charles Monroe, A solidification model with application to AlSi-based alloys, Shape Casting: The Minerals, Metals & Materials Series, pp. 201-213, 2019. doi.org/10.1007/978-3-030-06034-3_20

17-19     Fu-Yuan Hsu and Yu-Hung Chen, The validation of feeder modeling for ductile iron castings, Shape Casting: The Minerals, Metals & Materials Series, pp. 227-238, 2019. doi.org/10.1007/978-3-030-06034-3_22

04-19   Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, Vol. 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

02-19   Jingying Sun, Qichi Le, Li Fu, Jing Bai, Johannes Tretter, Klaus Herbold and Hongwei Huo, Gas entrainment behavior of aluminum alloy engine crankcases during the low-pressure-die-casting-process, Journal of Materials Processing Technology, Vol. 266, pp. 274-282, 2019. doi.org/10.1016/j.jmatprotec.2018.11.016

92-18   Fast, Flexible… More Versatile, Foundry Management Technology, March, 2018. 

82-18   Xu Zhao, Ping Wang, Tao Li, Bo-yu Zhang, Peng Wang, Guan-zhou Wang and Shi-qi Lu, Gating system optimization of high pressure die casting thin-wall AlSi10MnMg longitudinal loadbearing beam based on numerical simulation, China Foundry, Vol. 15, no. 6, pp. 436-442, 2018. doi: 10.1007/s41230-018-8052-z

80-18   Michail Papanikolaou, Emanuele Pagone, Konstantinos Salonitis, Mark Jolly and Charalampos Makatsoris, A computational framework towards energy efficient casting processes, Sustainable Design and Manufacturing 2018: Proceedings of the 5th International Conference on Sustainable Design and Manufacturing (KES-SDM-18), Gold Coast, Australia, June 24-26 2018, SIST 130, pp. 263-276, 2019. doi.org/10.1007/978-3-030-04290-5_27

64-18   Vasilios Fourlakidis, Ilia Belov and Attila Diószegi, Strength prediction for pearlitic lamellar graphite iron: Model validation, Metals, Vol. 8, No. 9, 2018. doi.org/10.3390/met8090684

51-18   Xue-feng Zhu, Bao-yi Yu, Li Zheng, Bo-ning Yu, Qiang Li, Shu-ning Lü and Hao Zhang, Influence of pouring methods on filling process, microstructure and mechanical properties of AZ91 Mg alloy pipe by horizontal centrifugal casting, China Foundry, vol. 15, no. 3, pp.196-202, 2018. doi.org/10.1007/s41230-018-7256-6

47-18   Santosh Reddy Sama, Jiayi Wang and Guha Manogharan, Non-conventional mold design for metal casting using 3D sand-printing, Journal of Manufacturing Processes, vol. 34-B, pp. 765-775, 2018. doi.org/10.1016/j.jmapro.2018.03.049

42-18   M. Koru and O. Serçe, The Effects of Thermal and Dynamical Parameters and Vacuum Application on Porosity in High-Pressure Die Casting of A383 Al-Alloy, International Journal of Metalcasting, pp. 1-17, 2018. doi.org/10.1007/s40962-018-0214-7

41-18   Abhilash Viswanath, S. Savithri, U.T.S. Pillai, Similitude analysis on flow characteristics of water, A356 and AM50 alloys during LPC process, Journal of Materials Processing Technology, vol. 257, pp. 270-277, 2018. doi.org/10.1016/j.jmatprotec.2018.02.031

29-18   Seyboldt, Christoph and Liewald, Mathias, Investigation on thixojoining to produce hybrid components with intermetallic phase, AIP Conference Proceedings, vol. 1960, no. 1, 2018. doi.org/10.1063/1.5034992

28-18   Laura Schomer, Mathias Liewald and Kim Rouven Riedmüller, Simulation of the infiltration process of a ceramic open-pore body with a metal alloy in semi-solid state to design the manufacturing of interpenetrating phase composites, AIP Conference Proceedings, vol. 1960, no. 1, 2018. doi.org/10.1063/1.5034991

41-17   Y. N. Wu et al., Numerical Simulation on Filling Optimization of Copper Rotor for High Efficient Electric Motors in Die Casting Process, Materials Science Forum, Vol. 898, pp. 1163-1170, 2017.

12-17   A.M.  Zarubin and O.A. Zarubina, Controlling the flow rate of melt in gravity die casting of aluminum alloys, Liteynoe Proizvodstvo (Casting Manufacturing), pp 16-20, 6, 2017. In Russian.

10-17   A.Y. Korotchenko, Y.V. Golenkov, M.V. Tverskoy and D.E. Khilkov, Simulation of the Flow of Metal Mixtures in the Mold, Liteynoe Proizvodstvo (Casting Manufacturing), pp 18-22, 5, 2017. In Russian.

08-17   Morteza Morakabian Esfahani, Esmaeil Hajjari, Ali Farzadi and Seyed Reza Alavi Zaree, Prediction of the contact time through modeling of heat transfer and fluid flow in compound casting process of Al/Mg light metals, Journal of Materials Research, © Materials Research Society 2017

04-17   Huihui Liu, Xiongwei He and Peng Guo, Numerical simulation on semi-solid die-casting of magnesium matrix composite based on orthogonal experiment, AIP Conference Proceedings 1829, 020037 (2017); doi.org/10.1063/1.4979769.

100-16  Robert Watson, New numerical techniques to quantify and predict the effect of entrainment defects, applied to high pressure die casting, PhD Thesis: University of Birmingham, 2016.

88-16   M.C. Carter, T. Kauffung, L. Weyenberg and C. Peters, Low Pressure Die Casting Simulation Discovery through Short Shot, Cast Expo & Metal Casting Congress, April 16-19, 2016, Minneapolis, MN, Copyright 2016 American Foundry Society.

61-16   M. Koru and O. Serçe, Experimental and numerical determination of casting mold interfacial heat transfer coefficient in the high pressure die casting of a 360 aluminum alloy, ACTA PHYSICA POLONICA A, Vol. 129 (2016)

59-16   R. Pirovano and S. Mascetti, Tracking of collapsed bubbles during a filling simulation, La Metallurgia Italiana – n. 6 2016

43-16   Kevin Lee, Understanding shell cracking during de-wax process in investment casting, Ph.D Thesis: University of Birmingham, School of Engineering, Department of Chemical Engineering, 2016.

35-16   Konstantinos Salonitis, Mark Jolly, Binxu Zeng, and Hamid Mehrabi, Improvements in energy consumption and environmental impact by novel single shot melting process for casting, Journal of Cleaner Production, doi.org/10.1016/j.jclepro.2016.06.165, Open Access funded by Engineering and Physical Sciences Research Council, June 29, 2016

20-16   Fu-Yuan Hsu, Bifilm Defect Formation in Hydraulic Jump of Liquid Aluminum, Metallurgical and Materials Transactions B, 2016, Band: 47, Heft 3, 1634-1648.

15-16   Mingfan Qia, Yonglin Kanga, Bing Zhoua, Wanneng Liaoa, Guoming Zhua, Yangde Lib,and Weirong Li, A forced convection stirring process for Rheo-HPDC aluminum and magnesium alloys, Journal of Materials Processing Technology 234 (2016) 353–367

112-15   José Miguel Gonçalves Ledo Belo da Costa, Optimization of filling systems for low pressure by FLOW-3D, Dissertação de mestrado integrado em Engenharia Mecânica, 2015.

89-15   B.W. Zhu, L.X. Li, X. Liu, L.Q. Zhang and R. Xu, Effect of Viscosity Measurement Method to Simulate High Pressure Die Casting of Thin-Wall AlSi10MnMg Alloy Castings, Journal of Materials Engineering and Performance, Published online, November 2015, doi.org/10.1007/s11665-015-1783-8, © ASM International.

88-15   Peng Zhang, Zhenming Li, Baoliang Liu, Wenjiang Ding and Liming Peng, Improved tensile properties of a new aluminum alloy for high pressure die casting, Materials Science & Engineering A651(2016)376–390, Available online, November 2015.

83-15   Zu-Qi Hu, Xin-Jian Zhang and Shu-Sen Wu, Microstructure, Mechanical Properties and Die-Filling Behavior of High-Performance Die-Cast Al–Mg–Si–Mn Alloy, Acta Metall. Sin. (Engl. Lett.), doi.org/10.1007/s40195-015-0332-7, © The Chinese Society for Metals and Springer-Verlag Berlin Heidelberg 2015.

82-15   J. Müller, L. Xue, M.C. Carter, C. Thoma, M. Fehlbier and M. Todte, A Die Spray Cooling Model for Thermal Die Cycling Simulations, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

81-15   M. T. Murray, L.F. Hansen, L. Chilcott, E. Li and A.M. Murray, Case Studies in the Use of Simulation- Improved Yield and Reduced Time to Market, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

80-15   R. Bhola, S. Chandra and D. Souders, Predicting Castability of Thin-Walled Parts for the HPDC Process Using Simulations, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

76-15   Prosenjit Das, Sudip K. Samanta, Shashank Tiwari and Pradip Dutta, Die Filling Behaviour of Semi Solid A356 Al Alloy Slurry During Rheo Pressure Die Casting, Transactions of the Indian Institute of Metals, pp 1-6, October 2015

74-15   Murat KORU and Orhan SERÇE, Yüksek Basınçlı Döküm Prosesinde Enjeksiyon Parametrelerine Bağlı Olarak Döküm Simülasyon, Cumhuriyet University Faculty of Science, Science Journal (CSJ), Vol. 36, No: 5 (2015) ISSN: 1300-1949, May 2015

69-15   A. Viswanath, S. Sivaraman, U. T. S. Pillai, Computer Simulation of Low Pressure Casting Process Using FLOW-3D, Materials Science Forum, Vols. 830-831, pp. 45-48, September 2015

68-15   J. Aneesh Kumar, K. Krishnakumar and S. Savithri, Computer Simulation of Centrifugal Casting Process Using FLOW-3D, Materials Science Forum, Vols. 830-831, pp. 53-56, September 2015

59-15   F. Hosseini Yekta and S. A. Sadough Vanini, Simulation of the flow of semi-solid steel alloy using an enhanced model, Metals and Materials International, August 2015.

44-15   Ulrich E. Klotz, Tiziana Heiss and Dario Tiberto, Platinum investment casting material properties, casting simulation and optimum process parameters, Jewelry Technology Forum 2015

41-15   M. Barkhudarov and R. Pirovano, Minimizing Air Entrainment in High Pressure Die Casting Shot Sleeves, GIFA 2015, Düsseldorf, Germany

40-15   M. Todte, A. Fent, and H. Lang, Simulation in support of the development of innovative processes in the casting industry, GIFA 2015, Düsseldorf, Germany

19-15   Bruce Morey, Virtual casting improves powertrain design, Automotive Engineering, SAE International, March 2015.

15-15   K.S. Oh, J.D. Lee, S.J. Kim and J.Y. Choi, Development of a large ingot continuous caster, Metall. Res. Technol. 112, 203 (2015) © EDP Sciences, 2015, doi.org/10.1051/metal/2015006, www.metallurgical-research.org

14-15   Tiziana Heiss, Ulrich E. Klotz and Dario Tiberto, Platinum Investment Casting, Part I: Simulation and Experimental Study of the Casting Process, Johnson Matthey Technol. Rev., 2015, 59, (2), 95, doi.org/10.1595/205651315×687399

138-14 Christopher Thoma, Wolfram Volk, Ruben Heid, Klaus Dilger, Gregor Banner and Harald Eibisch, Simulation-based prediction of the fracture elongation as a failure criterion for thin-walled high-pressure die casting components, International Journal of Metalcasting, Vol. 8, No. 4, pp. 47-54, 2014. doi.org/10.1007/BF03355594

107-14  Mehran Seyed Ahmadi, Dissolution of Si in Molten Al with Gas Injection, ProQuest Dissertations And Theses; Thesis (Ph.D.), University of Toronto (Canada), 2014; Publication Number: AAT 3637106; ISBN: 9781321195231; Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.; 191 p.

99-14   R. Bhola and S. Chandra, Predicting Castability for Thin-Walled HPDC Parts, Foundry Management Technology, December 2014

92-14   Warren Bishenden and Changhua Huang, Venting design and process optimization of die casting process for structural components; Part II: Venting design and process optimization, Die Casting Engineer, November 2014

90-14   Ken’ichi Kanazawa, Ken’ichi Yano, Jun’ichi Ogura, and Yasunori Nemoto, Optimum Runner Design for Die-Casting using CFD Simulations and Verification with Water-Model Experiments, Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition, IMECE2014, November 14-20, 2014, Montreal, Quebec, Canada, IMECE2014-37419

89-14   P. Kapranos, C. Carney, A. Pola, and M. Jolly, Advanced Casting Methodologies: Investment Casting, Centrifugal Casting, Squeeze Casting, Metal Spinning, and Batch Casting, In Comprehensive Materials Processing; McGeough, J., Ed.; 2014, Elsevier Ltd., 2014; Vol. 5, pp 39–67.

77-14   Andrei Y. Korotchenko, Development of Scientific and Technological Approaches to Casting Net-Shaped Castings in Sand Molds Free of Shrinkage Defects and Hot Tears, Post-doctoral thesis: Russian State Technological University, 2014. In Russian.

69-14   L. Xue, M.C. Carter, A.V. Catalina, Z. Lin, C. Li, and C. Qiu, Predicting, Preventing Core Gas Defects in Steel Castings, Modern Casting, September 2014

68-14   L. Xue, M.C. Carter, A.V. Catalina, Z. Lin, C. Li, and C. Qiu, Numerical Simulation of Core Gas Defects in Steel Castings, Copyright 2014 American Foundry Society, 118th Metalcasting Congress, April 8 – 11, 2014, Schaumburg, IL

51-14   Jesus M. Blanco, Primitivo Carranza, Rafael Pintos, Pedro Arriaga, and Lakhdar Remaki, Identification of Defects Originated during the Filling of Cast Pieces through Particles Modelling, 11th World Congress on Computational Mechanics (WCCM XI), 5th European Conference on Computational Mechanics (ECCM V), 6th European Conference on Computational Fluid Dynamics (ECFD VI), E. Oñate, J. Oliver and A. Huerta (Eds)

47-14   B. Vijaya Ramnatha, C.Elanchezhiana, Vishal Chandrasekhar, A. Arun Kumarb, S. Mohamed Asif, G. Riyaz Mohamed, D. Vinodh Raj , C .Suresh Kumar, Analysis and Optimization of Gating System for Commutator End Bracket, Procedia Materials Science 6 ( 2014 ) 1312 – 1328, 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)

42-14  Bing Zhou, Yong-lin Kang, Guo-ming Zhu, Jun-zhen Gao, Ming-fan Qi, and Huan-huan Zhang, Forced convection rheoforming process for preparation of 7075 aluminum alloy semisolid slurry and its numerical simulation, Trans. Nonferrous Met. Soc. China 24(2014) 1109−1116

37-14    A. Karwinski, W. Lesniewski, P. Wieliczko, and M. Malysza, Casting of Titanium Alloys in Centrifugal Induction Furnaces, Archives of Metallurgy and Materials, Volume 59, Issue 1, doi.org/10.2478/amm-2014-0068, 2014.

26-14    Bing Zhou, Yonglin Kang, Mingfan Qi, Huanhuan Zhang and Guoming ZhuR-HPDC Process with Forced Convection Mixing Device for Automotive Part of A380 Aluminum Alloy, Materials 2014, 7, 3084-3105; doi.org/10.3390/ma7043084

20-14  Johannes Hartmann, Tobias Fiegl, Carolin Körner, Aluminum integral foams with tailored density profile by adapted blowing agents, Applied Physics A, doi.org/10.1007/s00339-014-8377-4, March 2014.

19-14    A.Y. Korotchenko, N.A. Nikiforova, E.D. Demjanov, N.C. Larichev, The Influence of the Filling Conditions on the Service Properties of the Part Side Frame, Russian Foundryman, 1 (January), pp 40-43, 2014. In Russian.

11-14 B. Fuchs and C. Körner, Mesh resolution consideration for the viability prediction of lost salt cores in the high pressure die casting process, Progress in Computational Fluid Dynamics, Vol. 14, No. 1, 2014, Copyright © 2014 Inderscience Enterprises Ltd.

08-14 FY Hsu, SW Wang, and HJ Lin, The External and Internal Shrinkages in Aluminum Gravity Castings, Shape Casting: 5th International Symposium 2014. Available online at Google Books

103-13  B. Fuchs, H. Eibisch and C. Körner, Core Viability Simulation for Salt Core Technology in High-Pressure Die Casting, International Journal of Metalcasting, July 2013, Volume 7, Issue 3, pp 39–45

94-13    Randall S. Fielding, J. Crapps, C. Unal, and J.R.Kennedy, Metallic Fuel Casting Development and Parameter Optimization Simulations, International Conference on Fast reators and Related Fuel Cycles (FR13), 4-7 March 2013, Paris France

90-13  A. Karwińskia, M. Małyszaa, A. Tchórza, A. Gila, B. Lipowska, Integration of Computer Tomography and Simulation Analysis in Evaluation of Quality of Ceramic-Carbon Bonded Foam Filter, Archives of Foundry Engineering, doi.org/10.2478/afe-2013-0084, Published quarterly as the organ of the Foundry Commission of the Polish Academy of Sciences, ISSN, (2299-2944), Volume 13, Issue 4/2013

88-13  Litie and Metallurgia (Casting and Metallurgy), 3 (72), 2013, N.V.Sletova, I.N.Volnov, S.P.Zadrutsky, V.A.Chaikin, Modeling of the Process of Removing Non-metallic Inclusions in Aluminum Alloys Using the FLOW-3D program, pp 138-140. In Russian.

85-13    Michał Szucki,Tomasz Goraj, Janusz Lelito, Józef S. Suchy, Numerical Analysis of Solid Particles Flow in Liquid Metal, XXXVII International Scientific Conference Foundryman’ Day 2013, Krakow, 28-29 November 2013

84-13  Körner, C., Schwankl, M., Himmler, D., Aluminum-Aluminum compound castings by electroless deposited zinc layers, Journal of Materials Processing Technology (2014), doi.org/10.1016/j.jmatprotec.2013.12.01483-13.

77-13  Antonio Armillotta & Raffaello Baraggi & Simone Fasoli, SLM tooling for die casting with conformal cooling channels, The International Journal of Advanced Manufacturing Technology, doi.org/10.1007/s00170-013-5523-7, December 2013.

64-13   Johannes Hartmann, Christina Blümel, Stefan Ernst, Tobias Fiegl, Karl-Ernst Wirth, Carolin Körner, Aluminum integral foam castings with microcellular cores by nano-functionalization, J Mater Sci, doi.org/10.1007/s10853-013-7668-z, September 2013.

46-13  Nicholas P. Orenstein, 3D Flow and Temperature Analysis of Filling a Plutonium Mold, LA-UR-13-25537, Approved for public release; distribution is unlimited. Los Alamos Annual Student Symposium 2013, 2013-07-24 (Rev.1)

42-13   Yang Yue, William D. Griffiths, and Nick R. Green, Modelling of the Effects of Entrainment Defects on Mechanical Properties in a Cast Al-Si-Mg Alloy, Materials Science Forum, 765, 225, 2013.

39-13  J. Crapps, D.S. DeCroix, J.D Galloway, D.A. Korzekwa, R. Aikin, R. Fielding, R. Kennedy, C. Unal, Separate effects identification via casting process modeling for experimental measurement of U-Pu-Zr alloys, Journal of Nuclear Materials, 15 July 2013.

35-13   A. Pari, Real Life Problem Solving through Simulations in the Die Casting Industry – Case Studies, © Die Casting Engineer, July 2013.

34-13  Martin Lagler, Use of Simulation to Predict the Viability of Salt Cores in the HPDC Process – Shot Curve as a Decisive Criterion, © Die Casting Engineer, July 2013.

24-13    I.N.Volnov, Optimizatsia Liteynoi Tekhnologii, (Casting Technology Optimization), Liteyshik Rossii (Russian Foundryman), 3, 2013, 27-29. In Russian

23-13  M.R. Barkhudarov, I.N. Volnov, Minimizatsia Zakhvata Vozdukha v Kamere Pressovania pri Litie pod Davleniem, (Minimization of Air Entrainment in the Shot Sleeve During High Pressure Die Casting), Liteyshik Rossii (Russian Foundryman), 3, 2013, 30-34. In Russian

09-13  M.C. Carter and L. Xue, Simulating the Parameters that Affect Core Gas Defects in Metal Castings, Copyright 2012 American Foundry Society, Presented at the 2013 CastExpo, St. Louis, Missouri, April 2013

08-13  C. Reilly, N.R. Green, M.R. Jolly, J.-C. Gebelin, The Modelling Of Oxide Film Entrainment In Casting Systems Using Computational Modelling, Applied Mathematical Modelling, http://dx.doi.org/10.1016/j.apm.2013.03.061, April 2013.

03-13  Alexandre Reikher and Krishna M. Pillai, A fast simulation of transient metal flow and solidification in a narrow channel. Part II. Model validation and parametric study, Int. J. Heat Mass Transfer (2013), http://dx.doi.org/10.1016/j.ijheatmasstransfer.2012.12.061.

02-13  Alexandre Reikher and Krishna M. Pillai, A fast simulation of transient metal flow and solidification in a narrow channel. Part I: Model development using lubrication approximation, Int. J. Heat Mass Transfer (2013), http://dx.doi.org/10.1016/j.ijheatmasstransfer.2012.12.060.

116-12  Jufu Jianga, Ying Wang, Gang Chena, Jun Liua, Yuanfa Li and Shoujing Luo, “Comparison of mechanical properties and microstructure of AZ91D alloy motorcycle wheels formed by die casting and double control forming, Materials & Design, Volume 40, September 2012, Pages 541-549.

107-12  F.K. Arslan, A.H. Hatman, S.Ö. Ertürk, E. Güner, B. Güner, An Evaluation for Fundamentals of Die Casting Materials Selection and Design, IMMC’16 International Metallurgy & Materials Congress, Istanbul, Turkey, 2012.

103-12 WU Shu-sen, ZHONG Gu, AN Ping, WAN Li, H. NAKAE, Microstructural characteristics of Al−20Si−2Cu−0.4Mg−1Ni alloy formed by rheo-squeeze casting after ultrasonic vibration treatment, Transactions of Nonferrous Metals Society of China, 22 (2012) 2863-2870, November 2012. Full paper available online.

109-12 Alexandre Reikher, Numerical Analysis of Die-Casting Process in Thin Cavities Using Lubrication Approximation, Ph.D. Thesis: The University of Wisconsin Milwaukee, Engineering Department (2012) Theses and Dissertations. Paper 65.

97-12 Hong Zhou and Li Heng Luo, Filling Pattern of Step Gating System in Lost Foam Casting Process and its Application, Advanced Materials Research, Volumes 602-604, Progress in Materials and Processes, 1916-1921, December 2012.

93-12  Liangchi Zhang, Chunliang Zhang, Jeng-Haur Horng and Zichen Chen, Functions of Step Gating System in the Lost Foam Casting Process, Advanced Materials Research, 591-593, 940, DOI: 10.4028/www.scientific.net/AMR.591-593.940, November 2012.

91-12  Hong Yan, Jian Bin Zhu, Ping Shan, Numerical Simulation on Rheo-Diecasting of Magnesium Matrix Composites, 10.4028/www.scientific.net/SSP.192-193.287, Solid State Phenomena, 192-193, 287.

89-12  Alexandre Reikher and Krishna M. Pillai, A Fast Numerical Simulation for Modeling Simultaneous Metal Flow and Solidification in Thin Cavities Using the Lubrication Approximation, Numerical Heat Transfer, Part A: Applications: An International Journal of Computation and Methodology, 63:2, 75-100, November 2012.

82-12  Jufu Jiang, Gang Chen, Ying Wang, Zhiming Du, Weiwei Shan, and Yuanfa Li, Microstructure and mechanical properties of thin-wall and high-rib parts of AM60B Mg alloy formed by double control forming and die casting under the optimal conditions, Journal of Alloys and Compounds, http://dx.doi.org/10.1016/j.jallcom.2012.10.086, October 2012.

78-12   A. Pari, Real Life Problem Solving through Simulations in the Die Casting Industry – Case Studies, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

77-12  Y. Wang, K. Kabiri-Bamoradian and R.A. Miller, Rheological behavior models of metal matrix alloys in semi-solid casting process, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

76-12  A. Reikher and H. Gerber, Analysis of Solidification Parameters During the Die Cast Process, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

75-12 R.A. Miller, Y. Wang and K. Kabiri-Bamoradian, Estimating Cavity Fill Time, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012Indianapolis, IN.

65-12  X.H. Yang, T.J. Lu, T. Kim, Influence of non-conducting pore inclusions on phase change behavior of porous media with constant heat flux boundaryInternational Journal of Thermal Sciences, Available online 10 October 2012. Available online at SciVerse.

55-12  Hejun Li, Pengyun Wang, Lehua Qi, Hansong Zuo, Songyi Zhong, Xianghui Hou, 3D numerical simulation of successive deposition of uniform molten Al droplets on a moving substrate and experimental validation, Computational Materials Science, Volume 65, December 2012, Pages 291–301.

52-12 Hongbing Ji, Yixin Chen and Shengzhou Chen, Numerical Simulation of Inner-Outer Couple Cooling Slab Continuous Casting in the Filling Process, Advanced Materials Research (Volumes 557-559), Advanced Materials and Processes II, pp. 2257-2260, July 2012.

47-12    Petri Väyrynen, Lauri Holappa, and Seppo Louhenkilpi, Simulation of Melting of Alloying Materials in Steel Ladle, SCANMET IV – 4th International Conference on Process Development in Iron and Steelmaking, Lulea, Sweden, June 10-13, 2012.

46-12  Bin Zhang and Dave Salee, Metal Flow and Heat Transfer in Billet DC Casting Using Wagstaff® Optifill™ Metal Distribution Systems, 5th International Metal Quality Workshop, United Arab Emirates Dubai, March 18-22, 2012.

45-12 D.R. Gunasegaram, M. Givord, R.G. O’Donnell and B.R. Finnin, Improvements engineered in UTS and elongation of aluminum alloy high pressure die castings through the alteration of runner geometry and plunger velocity, Materials Science & Engineering.

44-12    Antoni Drys and Stefano Mascetti, Aluminum Casting Simulations, Desktop Engineering, September 2012

42-12   Huizhen Duan, Jiangnan Shen and Yanping Li, Comparative analysis of HPDC process of an auto part with ProCAST and FLOW-3D, Applied Mechanics and Materials Vols. 184-185 (2012) pp 90-94, Online available since 2012/Jun/14 at www.scientific.net, © (2012) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.184-185.90.

41-12    Deniece R. Korzekwa, Cameron M. Knapp, David A. Korzekwa, and John W. Gibbs, Co-Design – Fabrication of Unalloyed Plutonium, LA-UR-12-23441, MDI Summer Research Group Workshop Advanced Manufacturing, 2012-07-25/2012-07-26 (Los Alamos, New Mexico, United States)

29-12  Dario Tiberto and Ulrich E. Klotz, Computer simulation applied to jewellery casting: challenges, results and future possibilities, IOP Conf. Ser.: Mater. Sci. Eng.33 012008. Full paper available at IOP.

28-12  Y Yue and N R Green, Modelling of different entrainment mechanisms and their influences on the mechanical reliability of Al-Si castings, 2012 IOP Conf. Ser.: Mater. Sci. Eng. 33,012072.Full paper available at IOP.

27-12  E Kaschnitz, Numerical simulation of centrifugal casting of pipes, 2012 IOP Conf. Ser.: Mater. Sci. Eng. 33 012031, Issue 1. Full paper available at IOP.

15-12  C. Reilly, N.R Green, M.R. Jolly, The Present State Of Modeling Entrainment Defects In The Shape Casting Process, Applied Mathematical Modelling, Available online 27 April 2012, ISSN 0307-904X, 10.1016/j.apm.2012.04.032.

12-12   Andrei Starobin, Tony Hirt, Hubert Lang, and Matthias Todte, Core drying simulation and validation, International Foundry Research, GIESSEREIFORSCHUNG 64 (2012) No. 1, ISSN 0046-5933, pp 2-5

10-12  H. Vladimir Martínez and Marco F. Valencia (2012). Semisolid Processing of Al/β-SiC Composites by Mechanical Stirring Casting and High Pressure Die Casting, Recent Researches in Metallurgical Engineering – From Extraction to Forming, Dr Mohammad Nusheh (Ed.), ISBN: 978-953-51-0356-1, InTech

07-12     Amir H. G. Isfahani and James M. Brethour, Simulating Thermal Stresses and Cooling Deformations, Die Casting Engineer, March 2012

06-12   Shuisheng Xie, Youfeng He and Xujun Mi, Study on Semi-solid Magnesium Alloys Slurry Preparation and Continuous Roll-casting Process, Magnesium Alloys – Design, Processing and Properties, ISBN: 978-953-307-520-4, InTech.

04-12 J. Spangenberg, N. Roussel, J.H. Hattel, H. Stang, J. Skocek, M.R. Geiker, Flow induced particle migration in fresh concrete: Theoretical frame, numerical simulations and experimental results on model fluids, Cement and Concrete Research, http://dx.doi.org/10.1016/j.cemconres.2012.01.007, February 2012.

01-12   Lee, B., Baek, U., and Han, J., Optimization of Gating System Design for Die Casting of Thin Magnesium Alloy-Based Multi-Cavity LCD Housings, Journal of Materials Engineering and Performance, Springer New York, Issn: 1059-9495, 10.1007/s11665-011-0111-1, Volume 1 / 1992 – Volume 21 / 2012. Available online at Springer Link.

104-11  Fu-Yuan Hsu and Huey Jiuan Lin, Foam Filters Used in Gravity Casting, Metall and Materi Trans B (2011) 42: 1110. doi:10.1007/s11663-011-9548-8.

99-11    Eduardo Trejo, Centrifugal Casting of an Aluminium Alloy, thesis: Doctor of Philosophy, Metallurgy and Materials School of Engineering University of Birmingham, October 2011. Full paper available upon request.

93-11  Olga Kononova, Andrejs Krasnikovs ,Videvuds Lapsa,Jurijs Kalinka and Angelina Galushchak, Internal Structure Formation in High Strength Fiber Concrete during Casting, World Academy of Science, Engineering and Technology 59 2011

76-11  J. Hartmann, A. Trepper, and C. Körner, Aluminum Integral Foams with Near-Microcellular Structure, Advanced Engineering Materials 2011, Volume 13 (2011) No. 11, © Wiley-VCH

71-11  Fu-Yuan Hsu and Yao-Ming Yang Confluence Weld in an Aluminum Gravity Casting, Journal of Materials Processing Technology, Available online 23 November 2011, ISSN 0924-0136, 10.1016/j.jmatprotec.2011.11.006.

65-11     V.A. Chaikin, A.V. Chaikin, I.N.Volnov, A Study of the Process of Late Modification Using Simulation, in Zagotovitelnye Proizvodstva v Mashinostroenii, 10, 2011, 8-12. In Russian.

54-11  Ngadia Taha Niane and Jean-Pierre Michalet, Validation of Foundry Process for Aluminum Parts with FLOW-3D Software, Proceedings of the 2011 International Symposium on Liquid Metal Processing and Casting, 2011.

51-11    A. Reikher and H. Gerber, Calculation of the Die Cast parameters of the Thin Wall Aluminum Cast Part, 2011 Die Casting Congress & Tabletop, Columbus, OH, September 19-21, 2011

50-11   Y. Wang, K. Kabiri-Bamoradian, and R.A. Miller, Runner design optimization based on CFD simulation for a die with multiple cavities, 2011 Die Casting Congress & Tabletop, Columbus, OH, September 19-21, 2011

48-11 A. Karwiński, W. Leśniewski, S. Pysz, P. Wieliczko, The technology of precision casting of titanium alloys by centrifugal process, Archives of Foundry Engineering, ISSN: 1897-3310), Volume 11, Issue 3/2011, 73-80, 2011.

46-11  Daniel Einsiedler, Entwicklung einer Simulationsmethodik zur Simulation von Strömungs- und Trocknungsvorgängen bei Kernfertigungsprozessen mittels CFD (Development of a simulation methodology for simulating flow and drying operations in core production processes using CFD), MSc thesis at Technical University of Aalen in Germany (Hochschule Aalen), 2011.

44-11  Bin Zhang and Craig Shaber, Aluminum Ingot Thermal Stress Development Modeling of the Wagstaff® EpsilonTM Rolling Ingot DC Casting System during the Start-up Phase, Materials Science Forum Vol. 693 (2011) pp 196-207, © 2011 Trans Tech Publications, July, 2011.

43-11 Vu Nguyen, Patrick Rohan, John Grandfield, Alex Levin, Kevin Naidoo, Kurt Oswald, Guillaume Girard, Ben Harker, and Joe Rea, Implementation of CASTfill low-dross pouring system for ingot casting, Materials Science Forum Vol. 693 (2011) pp 227-234, © 2011 Trans Tech Publications, July, 2011.

40-11  A. Starobin, D. Goettsch, M. Walker, D. Burch, Gas Pressure in Aluminum Block Water Jacket Cores, © 2011 American Foundry Society, International Journal of Metalcasting/Summer 2011

37-11 Ferencz Peti, Lucian Grama, Analyze of the Possible Causes of Porosity Type Defects in Aluminum High Pressure Diecast Parts, Scientific Bulletin of the Petru Maior University of Targu Mures, Vol. 8 (XXV) no. 1, 2011, ISSN 1841-9267

31-11  Johannes Hartmann, André Trepper, Carolin Körner, Aluminum Integral Foams with Near-Microcellular Structure, Advanced Engineering Materials, 13: n/a. doi: 10.1002/adem.201100035, June 2011.

27-11  A. Pari, Optimization of HPDC Process using Flow Simulation Case Studies, Die Casting Engineer, July 2011

26-11    A. Reikher, H. Gerber, Calculation of the Die Cast Parameters of the Thin Wall Aluminum Die Casting Part, Die Casting Engineer, July 2011

21-11 Thang Nguyen, Vu Nguyen, Morris Murray, Gary Savage, John Carrig, Modelling Die Filling in Ultra-Thin Aluminium Castings, Materials Science Forum (Volume 690), Light Metals Technology V, pp 107-111, 10.4028/www.scientific.net/MSF.690.107, June 2011.

19-11 Jon Spangenberg, Cem Celal Tutum, Jesper Henri Hattel, Nicolas Roussel, Metter Rica Geiker, Optimization of Casting Process Parameters for Homogeneous Aggregate Distribution in Self-Compacting Concrete: A Feasibility Study, © IEEE Congress on Evolutionary Computation, 2011, New Orleans, USA

16-11  A. Starobin, C.W. Hirt, H. Lang, and M. Todte, Core Drying Simulation and Validations, AFS Proceedings 2011, © American Foundry Society, Presented at the 115th Metalcasting Congress, Schaumburg, Illinois, April 2011.

15-11  J. J. Hernández-Ortega, R. Zamora, J. López, and F. Faura, Numerical Analysis of Air Pressure Effects on the Flow Pattern during the Filling of a Vertical Die Cavity, AIP Conf. Proc., Volume 1353, pp. 1238-1243, The 14th International Esaform Conference on Material Forming: Esaform 2011; doi:10.1063/1.3589686, May 2011. Available online.

10-11 Abbas A. Khalaf and Sumanth Shankar, Favorable Environment for Nondentric Morphology in Controlled Diffusion Solidification, DOI: 10.1007/s11661-011-0641-z, © The Minerals, Metals & Materials Society and ASM International 2011, Metallurgical and Materials Transactions A, March 11, 2011.

08-11 Hai Peng Li, Chun Yong Liang, Li Hui Wang, Hong Shui Wang, Numerical Simulation of Casting Process for Gray Iron Butterfly Valve, Advanced Materials Research, 189-193, 260, February 2011.

04-11  C.W. Hirt, Predicting Core Shooting, Drying and Defect Development, Foundry Management & Technology, January 2011.

76-10  Zhizhong Sun, Henry Hu, Alfred Yu, Numerical Simulation and Experimental Study of Squeeze Casting Magnesium Alloy AM50, Magnesium Technology 2010, 2010 TMS Annual Meeting & ExhibitionFebruary 14-18, 2010, Seattle, WA.

68-10  A. Reikher, H. Gerber, K.M. Pillai, T.-C. Jen, Natural Convection—An Overlooked Phenomenon of the Solidification Process, Die Casting Engineer, January 2010

54-10    Andrea Bernardoni, Andrea Borsi, Stefano Mascetti, Alessandro Incognito and Matteo Corrado, Fonderia Leonardo aveva ragione! L’enorme cavallo dedicato a Francesco Sforza era materialmente realizzabile, A&C – Analisis e Calcolo, Giugno 2010. In  Italian.

48-10  J. J. Hernández-Ortega, R. Zamora, J. Palacios, J. López and F. Faura, An Experimental and Numerical Study of Flow Patterns and Air Entrapment Phenomena During the Filling of a Vertical Die Cavity, J. Manuf. Sci. Eng., October 2010, Volume 132, Issue 5, 05101, doi:10.1115/1.4002535.

47-10  A.V. Chaikin, I.N. Volnov, and V.A. Chaikin, Development of Dispersible Mixed Inoculant Compositions Using the FLOW-3D Program, Liteinoe Proizvodstvo, October, 2010, in Russian.

42-10  H. Lakshmi, M.C. Vinay Kumar, Raghunath, P. Kumar, V. Ramanarayanan, K.S.S. Murthy, P. Dutta, Induction reheating of A356.2 aluminum alloy and thixocasting as automobile component, Transactions of Nonferrous Metals Society of China 20(20101) s961-s967.

41-10  Pamela J. Waterman, Understanding Core-Gas Defects, Desktop Engineering, October 2010. Available online at Desktop Engineering. Also published in the Foundry Trade Journal, November 2010.

39-10  Liu Zheng, Jia Yingying, Mao Pingli, Li Yang, Wang Feng, Wang Hong, Zhou Le, Visualization of Die Casting Magnesium Alloy Steering Bracket, Special Casting & Nonferrous Alloys, ISSN: 1001-2249, CN: 42-1148/TG, 2010-04. In Chinese.

37-10  Morris Murray, Lars Feldager Hansen, and Carl Reinhardt, I Have Defects – Now What, Die Casting Engineer, September 2010

36-10  Stefano Mascetti, Using Flow Analysis Software to Optimize Piston Velocity for an HPDC Process, Die Casting Engineer, September 2010. Also available in Italian: Ottimizzare la velocita del pistone in pressofusione.  A & C, Analisi e Calcolo, Anno XII, n. 42, Gennaio 2011, ISSN 1128-3874.

32-10  Guan Hai Yan, Sheng Dun Zhao, Zheng Hui Sha, Parameters Optimization of Semisolid Diecasting Process for Air-Conditioner’s Triple Valve in HPb59-1 Alloy, Advanced Materials Research (Volumes 129 – 131), Vol. Material and Manufacturing Technology, pp. 936-941, DOI: 10.4028/www.scientific.net/AMR.129-131.936, August 2010.

29-10 Zheng Peng, Xu Jun, Zhang Zhifeng, Bai Yuelong, and Shi Likai, Numerical Simulation of Filling of Rheo-diecasting A357 Aluminum Alloy, Special Casting & Nonferrous Alloys, DOI: CNKI:SUN:TZZZ.0.2010-01-024, 2010.

27-10 For an Aerospace Diecasting, Littler Uses Simulation to Reveal Defects, and Win a New Order, Foundry Management & Technology, July 2010

23-10 Michael R. Barkhudarov, Minimizing Air Entrainment, The Canadian Die Caster, June 2010

15-10 David H. Kirkwood, Michel Suery, Plato Kapranos, Helen V. Atkinson, and Kenneth P. Young, Semi-solid Processing of Alloys, 2010, XII, 172 p. 103 illus., 19 in color., Hardcover ISBN: 978-3-642-00705-7.

09-10  Shannon Wetzel, Fullfilling Da Vinci’s Dream, Modern Casting, April 2010.

08-10 B.I. Semenov, K.M. Kushtarov, Semi-solid Manufacturing of Castings, New Industrial Technologies, Publication of Moscow State Technical University n.a. N.E. Bauman, 2009 (in Russian)

07-10 Carl Reilly, Development Of Quantitative Casting Quality Assessment Criteria Using Process Modelling, thesis: The University of Birmingham, March 2010 (Available upon request)

06-10 A. Pari, Optimization of HPDC Process using Flow Simulation – Case Studies, CastExpo ’10, NADCA, Orlando, Florida, March 2010

05-10 M.C. Carter, S. Palit, and M. Littler, Characterizing Flow Losses Occurring in Air Vents and Ejector Pins in High Pressure Die Castings, CastExpo ’10, NADCA, Orlando, Florida, March 2010

04-10 Pamela Waterman, Simulating Porosity Factors, Foundry Management Technology, March 2010, Article available at Foundry Management Technology

03-10 C. Reilly, M.R. Jolly, N.R. Green, JC Gebelin, Assessment of Casting Filling by Modeling Surface Entrainment Events Using CFD, 2010 TMS Annual Meeting & Exhibition (Jim Evans Honorary Symposium), Seattle, Washington, USA, February 14-18, 2010

02-10 P. Väyrynen, S. Wang, J. Laine and S.Louhenkilpi, Control of Fluid Flow, Heat Transfer and Inclusions in Continuous Casting – CFD and Neural Network Studies, 2010 TMS Annual Meeting & Exhibition (Jim Evans Honorary Symposium), Seattle, Washington, USA, February 14-18, 2010

60-09   Somlak Wannarumon, and Marco Actis Grande, Comparisons of Computer Fluid Dynamic Software Programs applied to Jewelry Investment Casting Process, World Academy of Science, Engineering and Technology 55 2009.

59-09   Marco Actis Grande and Somlak Wannarumon, Numerical Simulation of Investment Casting of Gold Jewelry: Experiments and Validations, World Academy of Science, Engineering and Technology, Vol:3 2009-07-24

56-09  Jozef Kasala, Ondrej Híreš, Rudolf Pernis, Start-up Phase Modeling of Semi Continuous Casting Process of Brass Billets, Metal 2009, 19.-21.5.2009

51-09  In-Ting Hong, Huan-Chien Tung, Chun-Hao Chiu and Hung-Shang Huang, Effect of Casting Parameters on Microstructure and Casting Quality of Si-Al Alloy for Vacuum Sputtering, China Steel Technical Report, No. 22, pp. 33-40, 2009.

42-09  P. Väyrynen, S. Wang, S. Louhenkilpi and L. Holappa, Modeling and Removal of Inclusions in Continuous Casting, Materials Science & Technology 2009 Conference & Exhibition, Pittsburgh, Pennsylvania, USA, October 25-29, 2009

41-09 O.Smirnov, P.Väyrynen, A.Kravchenko and S.Louhenkilpi, Modern Methods of Modeling Fluid Flow and Inclusions Motion in Tundish Bath – General View, Proceedings of Steelsim 2009 – 3rd International Conference on Simulation and Modelling of Metallurgical Processes in Steelmaking, Leoben, Austria, September 8-10, 2009

21-09 A. Pari, Case Studies – Optimization of HPDC Process Using Flow Simulation, Die Casting Engineer, July 2009

20-09 M. Sirvio, M. Wos, Casting directly from a computer model by using advanced simulation software, FLOW-3D Cast, Archives of Foundry Engineering Volume 9, Issue 1/2009, 79-82

19-09 Andrei Starobin, C.W. Hirt, D. Goettsch, A Model for Binder Gas Generation and Transport in Sand Cores and Molds, Modeling of Casting, Welding, and Solidification Processes XII, TMS (The Minerals, Metals & Minerals Society), June 2009

11-09 Michael Barkhudarov, Minimizing Air Entrainment in a Shot Sleeve during Slow-Shot Stage, Die Casting Engineer (The North American Die Casting Association ISSN 0012-253X), May 2009

10-09 A. Reikher, H. Gerber, Application of One-Dimensional Numerical Simulation to Optimize Process Parameters of a Thin-Wall Casting in High Pressure Die Casting, Die Casting Engineer (The North American Die Casting Association ISSN 0012-253X), May 2009

7-09 Andrei Starobin, Simulation of Core Gas Evolution and Flow, presented at the North American Die Casting Association – 113th Metalcasting Congress, April 7-10, 2009, Las Vegas, Nevada, USA

6-09 A.Pari, Optimization of HPDC PROCESS: Case Studies, North American Die Casting Association – 113th Metalcasting Congress, April 7-10, 2009, Las Vegas, Nevada, USA

2-09 C. Reilly, N.R. Green and M.R. Jolly, Oxide Entrainment Structures in Horizontal Running Systems, TMS 2009, San Francisco, California, February 2009

30-08 I.N.Volnov, Computer Modeling of Casting of Pipe Fittings, © 2008, Pipe Fittings, 5 (38), 2008. Russian version

28-08 A.V.Chaikin, I.N.Volnov, V.A.Chaikin, Y.A.Ukhanov, N.R.Petrov, Analysis of the Efficiency of Alloy Modifiers Using Statistics and Modeling, © 2008, Liteyshik Rossii (Russian Foundryman), October, 2008

27-08 P. Scarber, Jr., H. Littleton, Simulating Macro-Porosity in Aluminum Lost Foam Castings, American Foundry Society, © 2008, AFS Lost Foam Conference, Asheville, North Carolina, October, 2008

25-08 FMT Staff, Forecasting Core Gas Pressures with Computer Simulation, Foundry Management and Technology, October 28, 2008 © 2008 Penton Media, Inc. Online article

24-08 Core and Mold Gas Evolution, Foundry Management and Technology, January 24, 2008 (excerpted from the FM&T May 2007 issue) © 2008 Penton Media, Inc.

22-08 Mark Littler, Simulation Eliminates Die Casting Scrap, Modern Casting/September 2008

21-08 X. Chen, D. Penumadu, Permeability Measurement and Numerical Modeling for Refractory Porous Materials, AFS Transactions © 2008 American Foundry Society, CastExpo ’08, Atlanta, Georgia, May 2008

20-08 Rolf Krack, Using Solidification Simulations for Optimising Die Cooling Systems, FTJ July/August 2008

19-08 Mark Littler, Simulation Software Eliminates Die Casting Scrap, ECS Casting Innovations, July/August 2008

13-08 T. Yoshimura, K. Yano, T. Fukui, S. Yamamoto, S. Nishido, M. Watanabe and Y. Nemoto, Optimum Design of Die Casting Plunger Tip Considering Air Entrainment, Proceedings of 10th Asian Foundry Congress (AFC10), Nagoya, Japan, May 2008

08-08 Stephen Instone, Andreas Buchholz and Gerd-Ulrich Gruen, Inclusion Transport Phenomena in Casting Furnaces, Light Metals 2008, TMS (The Minerals, Metals & Materials Society), 2008

07-08 P. Scarber, Jr., H. Littleton, Simulating Macro-Porosity in Aluminum Lost Foam Casting, AFS Transactions 2008 © American Foundry Society, CastExpo ’08, Atlanta, Georgia, May 2008

06-08 A. Reikher, H. Gerber and A. Starobin, Multi-Stage Plunger Deceleration System, CastExpo ’08, NADCA, Atlanta, Georgia, May 2008

05-08 Amol Palekar, Andrei Starobin, Alexander Reikher, Die-casting end-of-fill and drop forge viscometer flow transients examined with a coupled-motion numerical model, 68th World Foundry Congress, Chennai, India, February 2008

03-08 Petri J. Väyrynen, Sami K. Vapalahti and Seppo J. Louhenkilpi, On Validation of Mathematical Fluid Flow Models for Simulation of Tundish Water Models and Industrial Examples, AISTech 2008, May 2008

53-07   A. Kermanpur, Sh. Mahmoudi and A. Hajipour, Three-dimensional Numerical Simulation of Metal Flow and Solidification in the Multi-cavity Casting Moulds of Automotive Components, International Journal of Iron & Steel Society of Iran, Article 2, Volume 4, Issue 1, Summer and Autumn 2007, pages 8-15.

36-07 Duque Mesa A. F., Herrera J., Cruz L.J., Fernández G.P. y Martínez H.V., Caracterización Defectológica de Piezas Fundida por Lost Foam Casting Mediante Simulación Numérica, 8° Congreso Iberoamericano de Ingenieria Mecanica, Cusco, Peru, 23 al 25 de Octubre de 2007 (in Spanish)

27-07 A.Y. Korotchenko, A.M. Zarubin, I.A.Korotchenko, Modeling of High Pressure Die Casting Filling, Russian Foundryman, December 2007, pp 15-19. (in Russian)

26-07 I.N. Volnov, Modeling of Casting Processes with Variable Geometry, Russian Foundryman, November 2007, pp 27-30. (in Russian)

16-07 P. Väyrynen, S. Vapalahti, S. Louhenkilpi, L. Chatburn, M. Clark, T. Wagner, Tundish Flow Model Tuning and Validation – Steady State and Transient Casting Situations, STEELSIM 2007, Graz/Seggau, Austria, September 12-14 2007

11-07 Marco Actis Grande, Computer Simulation of the Investment Casting Process – Widening of the Filling Step, Santa Fe Symposium on Jewelry Manufacturing Technology, May 2007

09-07 Alexandre Reikher and Michael Barkhudarov, Casting: An Analytical Approach, Springer, 1st edition, August 2007, Hardcover ISBN: 978-1-84628-849-4. U.S. Order FormEurope Order Form.

07-07 I.N. Volnov, Casting Modeling Systems – Current State, Problems and Perspectives, (in Russian), Liteyshik Rossii (Russian Foundryman), June 2007

05-07 A.N. Turchin, D.G. Eskin, and L. Katgerman, Solidification under Forced-Flow Conditions in a Shallow Cavity, DOI: 10.1007/s1161-007-9183-9, © The Minerals, Metals & Materials Society and ASM International 2007

04-07 A.N. Turchin, M. Zuijderwijk, J. Pool, D.G. Eskin, and L. Katgerman, Feathery grain growth during solidification under forced flow conditions, © Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. DOI: 10.1016/j.actamat.2007.02.030, April 2007

03-07 S. Kuyucak, Sponsored Research – Clean Steel Casting Production—Evaluation of Laboratory Castings, Transactions of the American Foundry Society, Volume 115, 111th Metalcasting Congress, May 2007

02-07 Fu-Yuan Hsu, Mark R. Jolly and John Campbell, The Design of L-Shaped Runners for Gravity Casting, Shape Casting: 2nd International Symposium, Edited by Paul N. Crepeau, Murat Tiryakioðlu and John Campbell, TMS (The Minerals, Metals & Materials Society), Orlando, FL, Feb 2007

30-06 X.J. Liu, S.H. Bhavnani, R.A. Overfelt, Simulation of EPS foam decomposition in the lost foam casting process, Journal of Materials Processing Technology 182 (2007) 333–342, © 2006 Elsevier B.V. All rights reserved.

25-06 Michael Barkhudarov and Gengsheng Wei, Modeling Casting on the Move, Modern Casting, August 2006; Modeling of Casting Processes with Variable Geometry, Russian Foundryman, December 2007, pp 10-15. (in Russian)

24-06 P. Scarber, Jr. and C.E. Bates, Simulation of Core Gas Production During Mold Fill, © 2006 American Foundry Society

7-06 M.Y.Smirnov, Y.V.Golenkov, Manufacturing of Cast Iron Bath Tubs Castings using Vacuum-Process in Russia, Russia’s Foundryman, July 2006. In Russian.

6-06 M. Barkhudarov, and G. Wei, Modeling of the Coupled Motion of Rigid Bodies in Liquid Metal, Modeling of Casting, Welding and Advanced Solidification Processes – XI, May 28 – June 2, 2006, Opio, France, eds. Ch.-A. Gandin and M. Bellet, pp 71-78, 2006.

2-06 J.-C. Gebelin, M.R. Jolly and F.-Y. Hsu, ‘Designing-in’ Controlled Filling Using Numerical Simulation for Gravity Sand Casting of Aluminium Alloys, Int. J. Cast Met. Res., 2006, Vol.19 No.1

1-06 Michael Barkhudarov, Using Simulation to Control Microporosity Reduces Die Iterations, Die Casting Engineer, January 2006, pp. 52-54

30-05 H. Xue, K. Kabiri-Bamoradian, R.A. Miller, Modeling Dynamic Cavity Pressure and Impact Spike in Die Casting, Cast Expo ’05, April 16-19, 2005

22-05 Blas Melissari & Stavros A. Argyropoulous, Measurement of Magnitude and Direction of Velocity in High-Temperature Liquid Metals; Part I, Mathematical Modeling, Metallurgical and Materials Transactions B, Volume 36B, October 2005, pp. 691-700

21-05 M.R. Jolly, State of the Art Review of Use of Modeling Software for Casting, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 337-346

20-05 J-C Gebelin, M.R. Jolly & F-Y Hsu, ‘Designing-in’ Controlled Filling Using Numerical Simulation for Gravity Sand Casting of Aluminium Alloys, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 355-364

19-05 F-Y Hsu, M.R. Jolly & J Campbell, Vortex Gate Design for Gravity Castings, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 73-82

18-05 M.R. Jolly, Modelling the Investment Casting Process: Problems and Successes, Japanese Foundry Society, JFS, Tokyo, Sept. 2005

13-05 Xiaogang Yang, Xiaobing Huang, Xiaojun Dai, John Campbell and Joe Tatler, Numerical Modelling of the Entrainment of Oxide Film Defects in Filling of Aluminium Alloy Castings, International Journal of Cast Metals Research, 17 (6), 2004, 321-331

10-05 Carlos Evaristo Esparza, Martha P. Guerro-Mata, Roger Z. Ríos-Mercado, Optimal Design of Gating Systems by Gradient Search Methods, Computational Materials Science, October 2005

6-05 Birgit Hummler-Schaufler, Fritz Hirning, Jurgen Schaufler, A World First for Hatz Diesel and Schaufler Tooling, Die Casting Engineer, May 2005, pp. 18-21

4-05 Rolf Krack, The W35 Topic—A World First, Die Casting World, March 2005, pp. 16-17

3-05 Joerg Frei, Casting Simulations Speed Up Development, Die Casting World, March 2005, p. 14

2-05 David Goettsch and Michael Barkhudarov, Analysis and Optimization of the Transient Stage of Stopper-Rod Pour, Shape Casting: The John Campbell Symposium, The Minerals, Metals & Materials Society, 2005

36-04  Ik Min Park, Il Dong Choi, Yong Ho Park, Development of Light-Weight Al Scroll Compressor for Car Air Conditioner, Materials Science Forum, Designing, Processing and Properties of Advanced Engineering Materials, 449-452, 149, March 2004.

32-04 D.H. Kirkwood and P.J Ward, Numerical Modelling of Semi-Solid Flow under Processing Conditions, steel research int. 75 (2004), No. 8/9

30-04 Haijing Mao, A Numerical Study of Externally Solidified Products in the Cold Chamber Die Casting Process, thesis: The Ohio State University, 2004 (Available upon request)

28-04 Z. Cao, Z. Yang, and X.L. Chen, Three-Dimensional Simulation of Transient GMA Weld Pool with Free Surface, Supplement to the Welding Journal, June 2004.

23-04 State of the Art Use of Computational Modelling in the Foundry Industry, 3rd International Conference Computational Modelling of Materials III, Sicily, Italy, June 2004, Advances in Science and Technology,  Eds P. Vincenzini & A Lami, Techna Group Srl, Italy, ISBN: 88-86538-46-4, Part B, pp 479-490

22-04 Jerry Fireman, Computer Simulation Helps Reduce Scrap, Die Casting Engineer, May 2004, pp. 46-49

21-04 Joerg Frei, Simulation—A Safe and Quick Way to Good Components, Aluminium World, Volume 3, Issue 2, pp. 42-43

20-04 J.-C. Gebelin, M.R. Jolly, A. M. Cendrowicz, J. Cirre and S. Blackburn, Simulation of Die Filling for the Wax Injection Process – Part II Numerical Simulation, Metallurgical and Materials Transactions, Volume 35B, August 2004

14-04 Sayavur I. Bakhtiyarov, Charles H. Sherwin, and Ruel A. Overfelt, Hot Distortion Studies In Phenolic Urethane Cold Box System, American Foundry Society, 108th Casting Congress, June 12-15, 2004, Rosemont, IL, USA

13-04 Sayavur I. Bakhtiyarov and Ruel A. Overfelt, First V-Process Casting of Magnesium, American Foundry Society, 108th Casting Congress, June 12-15, 2004, Rosemont, IL, USA

5-04 C. Schlumpberger & B. Hummler-Schaufler, Produktentwicklung auf hohem Niveau (Product Development on a High Level), Druckguss Praxis, January 2004, pp 39-42 (in German).

3-04 Charles Bates, Dealing with Defects, Foundry Management and Technology, February 2004, pp 23-25

1-04 Laihua Wang, Thang Nguyen, Gary Savage and Cameron Davidson, Thermal and Flow Modeling of Ladling and Injection in High Pressure Die Casting Process, International Journal of Cast Metals Research, vol. 16 No 4 2003, pp 409-417

2-03 J-C Gebelin, AM Cendrowicz, MR Jolly, Modeling of the Wax Injection Process for the Investment Casting Process – Prediction of Defects, presented at the Third International Conference on Computational Fluid Dynamics in the Minerals and Process Industries, December 10-12, 2003, Melbourne, Australia, pp. 415-420

29-03 C. W. Hirt, Modeling Shrinkage Induced Micro-porosity, Flow Science Technical Note (FSI-03-TN66)

28-03 Thixoforming at the University of Sheffield, Diecasting World, September 2003, pp 11-12

26-03 William Walkington, Gas Porosity-A Guide to Correcting the Problems, NADCA Publication: 516

22-03 G F Yao, C W Hirt, and M Barkhudarov, Development of a Numerical Approach for Simulation of Sand Blowing and Core Formation, in Modeling of Casting, Welding, and Advanced Solidification Process-X”, Ed. By Stefanescu et al pp. 633-639, 2003

21-03 E F Brush Jr, S P Midson, W G Walkington, D T Peters, J G Cowie, Porosity Control in Copper Rotor Die Castings, NADCA Indianapolis Convention Center, Indianapolis, IN September 15-18, 2003, T03-046

12-03 J-C Gebelin & M.R. Jolly, Modeling Filters in Light Alloy Casting Processes,  Trans AFS, 2002, 110, pp. 109-120

11-03 M.R. Jolly, Casting Simulation – How Well Do Reality and Virtual Casting Match – A State of the Art Review, Intl. J. Cast Metals Research, 2002, 14, pp. 303-313

10-03 Gebelin., J-C and Jolly, M.R., Modeling of the Investment Casting Process, Journal of  Materials Processing Tech., Vol. 135/2-3, pp. 291 – 300

9-03 Cox, M, Harding, R.A. and Campbell, J., Optimised Running System Design for Bottom Filled Aluminium Alloy 2L99 Investment Castings, J. Mat. Sci. Tech., May 2003, Vol. 19, pp. 613-625

8-03 Von Alexander Schrey and Regina Reek, Numerische Simulation der Kernherstellung, (Numerical Simulation of Core Blowing), Giesserei, June 2003, pp. 64-68 (in German)

7-03 J. Zuidema Jr., L Katgerman, Cyclone separation of particles in aluminum DC Casting, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 607-614

6-03 Jean-Christophe Gebelin and Mark Jolly, Numerical Modeling of Metal Flow Through Filters, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 431-438

5-03 N.W. Lai, W.D. Griffiths and J. Campbell, Modelling of the Potential for Oxide Film Entrainment in Light Metal Alloy Castings, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 415-422

21-02 Boris Lukezic, Case History: Process Modeling Solves Die Design Problems, Modern Casting, February 2003, P 59

20-02 C.W. Hirt and M.R. Barkhudarov, Predicting Defects in Lost Foam Castings, Modern Casting, December 2002, pp 31-33

19-02 Mark Jolly, Mike Cox, Ric Harding, Bill Griffiths and John Campbell, Quiescent Filling Applied to Investment Castings, Modern Casting, December 2002 pp. 36-38

18-02 Simulation Helps Overcome Challenges of Thin Wall Magnesium Diecasting, Foundry Management and Technology, October 2002, pp 13-15

17-02 G Messmer, Simulation of a Thixoforging Process of Aluminum Alloys with FLOW-3D, Institute for Metal Forming Technology, University of Stuttgart

16-02 Barkhudarov, Michael, Computer Simulation of Lost Foam Process, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 319-324

15-02 Barkhudarov, Michael, Computer Simulation of Inclusion Tracking, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 341-346

14-02 Barkhudarov, Michael, Advanced Simulation of the Flow and Heat Transfer of an Alternator Housing, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 219-228

8-02 Sayavur I. Bakhtiyarov, and Ruel A. Overfelt, Experimental and Numerical Study of Bonded Sand-Air Two-Phase Flow in PUA Process, Auburn University, 2002 American Foundry Society, AFS Transactions 02-091, Kansas City, MO

7-02 A Habibollah Zadeh, and J Campbell, Metal Flow Through a Filter System, University of Birmingham, 2002 American Foundry Society, AFS Transactions 02-020, Kansas City, MO

6-02 Phil Ward, and Helen Atkinson, Final Report for EPSRC Project: Modeling of Thixotropic Flow of Metal Alloys into a Die, GR/M17334/01, March 2002, University of Sheffield

5-02 S. I. Bakhtiyarov and R. A. Overfelt, Numerical and Experimental Study of Aluminum Casting in Vacuum-sealed Step Molding, Auburn University, 2002 American Foundry Society, AFS Transactions 02-050, Kansas City, MO

4-02 J. C. Gebelin and M. R. Jolly, Modelling Filters in Light Alloy Casting Processes, University of Birmingham, 2002 American Foundry Society AFS Transactions 02-079, Kansas City, MO

3-02 Mark Jolly, Mike Cox, Jean-Christophe Gebelin, Sam Jones, and Alex Cendrowicz, Fundamentals of Investment Casting (FOCAST), Modelling the Investment Casting Process, Some preliminary results from the UK Research Programme, IRC in Materials, University of Birmingham, UK, AFS2001

49-01   Hua Bai and Brian G. Thomas, Bubble formation during horizontal gas injection into downward-flowing liquid, Metallurgical and Materials Transactions B, Vol. 32, No. 6, pp. 1143-1159, 2001. doi.org/10.1007/s11663-001-0102-y

45-01 Jan Zuidema; Laurens Katgerman; Ivo J. Opstelten;Jan M. Rabenberg, Secondary Cooling in DC Casting: Modelling and Experimental Results, TMS 2001, New Orleans, Louisianna, February 11-15, 2001

43-01 James Andrew Yurko, Fluid Flow Behavior of Semi-Solid Aluminum at High Shear Rates,Ph.D. thesis; Massachusetts Institute of Technology, June 2001. Abstract only; full thesis available at http://dspace.mit.edu/handle/1721.1/8451 (for a fee).

33-01 Juang, S.H., CAE Application on Design of Die Casting Dies, 2001 Conference on CAE Technology and Application, Hsin-Chu, Taiwan, November 2001, (article in Chinese with English-language abstract)

32-01 Juang, S.H. and C. M. Wang, Effect of Feeding Geometry on Flow Characteristics of Magnesium Die Casting by Numerical Analysis, The Preceedings of 6th FADMA Conference, Taipei, Taiwan, July 2001, Chinese language with English abstract

26-01 C. W. Hirt., Predicting Defects in Lost Foam Castings, December 13, 2001

21-01 P. Scarber Jr., Using Liquid Free Surface Areas as a Predictor of Reoxidation Tendency in Metal Alloy Castings, presented at the Steel Founders’ Society of American, Technical and Operating Conference, October 2001

20-01 P. Scarber Jr., J. Griffin, and C. E. Bates, The Effect of Gating and Pouring Practice on Reoxidation of Steel Castings, presented at the Steel Founders’ Society of American, Technical and Operating Conference, October 2001

19-01 L. Wang, T. Nguyen, M. Murray, Simulation of Flow Pattern and Temperature Profile in the Shot Sleeve of a High Pressure Die Casting Process, CSIRO Manufacturing Science and Technology, Melbourne, Victoria, Australia, Presented by North American Die Casting Association, Oct 29-Nov 1, 2001, Cincinnati, To1-014

18-01 Rajiv Shivpuri, Venkatesh Sankararaman, Kaustubh Kulkarni, An Approach at Optimizing the Ingate Design for Reducing Filling and Shrinkage Defects, The Ohio State University, Columbus, OH, Presented by North American Die Casting Association, Oct 29-Nov 1, 2001, Cincinnati, TO1-052

5-01 Michael Barkhudarov, Simulation Helps Overcome Challenges of Thin Wall Magnesium Diecasting, Diecasting World, March 2001, pp. 5-6

2-01 J. Grindling, Customized CFD Codes to Simulate Casting of Thermosets in Full 3D, Electrical Manufacturing and Coil Winding 2000 Conference, October 31-November 2, 20

20-00 Richard Schuhmann, John Carrig, Thang Nguyen, Arne Dahle, Comparison of Water Analogue Modelling and Numerical Simulation Using Real-Time X-Ray Flow Data in Gravity Die Casting, Australian Die Casting Association Die Casting 2000 Conference, September 3-6, 2000, Melbourne, Victoria, Australia

15-00 M. Sirvio, Vainola, J. Vartianinen, M. Vuorinen, J. Orkas, and S. Devenyi, Fluid Flow Analysis for Designing Gating of Aluminum Castings, Proc. NADCA Conf., Rosemont, IL, Nov 6-8, 1999

14-00 X. Yang, M. Jolly, and J. Campbell, Reduction of Surface Turbulence during Filling of Sand Castings Using a Vortex-flow Runner, Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August 2000

13-00 H. S. H. Lo and J. Campbell, The Modeling of Ceramic Foam Filters, Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August 2000

12-00 M. R. Jolly, H. S. H. Lo, M. Turan and J. Campbell, Use of Simulation Tools in the Practical Development of a Method for Manufacture of Cast Iron Camshafts,” Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August, 2000

14-99 J Koke, and M Modigell, Time-Dependent Rheological Properties of Semi-solid Metal Alloys, Institute of Chemical Engineering, Aachen University of Technology, Mechanics of Time-Dependent Materials 3: 15-30, 1999

12-99 Grun, Gerd-Ulrich, Schneider, Wolfgang, Ray, Steven, Marthinusen, Jan-Olaf, Recent Improvements in Ceramic Foam Filter Design by Coupled Heat and Fluid Flow Modeling, Proc TMS Annual Meeting, 1999, pp. 1041-1047

10-99 Bongcheol Park and Jerald R. Brevick, Computer Flow Modeling of Cavity Pre-fill Effects in High Pressure Die Casting, NADCA Proceedings, Cleveland T99-011, November, 1999

8-99 Brad Guthrie, Simulation Reduces Aluminum Die Casting Cost by Reducing Volume, Die Casting Engineer Magazine, September/October 1999, pp. 78-81

7-99 Fred L. Church, Virtual Reality Predicts Cast Metal Flow, Modern Metals, September, 1999, pp. 67F-J

19-98 Grun, Gerd-Ulrich, & Schneider, Wolfgang, Numerical Modeling of Fluid Flow Phenomena in the Launder-integrated Tool Within Casting Unit Development, Proc TMS Annual Meeting, 1998, pp. 1175-1182

18-98 X. Yang & J. Campbell, Liquid Metal Flow in a Pouring Basin, Int. J. Cast Metals Res, 1998, 10, pp. 239-253

15-98 R. Van Tol, Mould Filling of Horizontal Thin-Wall Castings, Delft University Press, The Netherlands, 1998

14-98 J. Daughtery and K. A. Williams, Thermal Modeling of Mold Material Candidates for Copper Pressure Die Casting of the Induction Motor Rotor Structure, Proc. Int’l Workshop on Permanent Mold Casting of Copper-Based Alloys, Ottawa, Ontario, Canada, Oct. 15-16, 1998

10-98 C. W. Hirt, and M.R. Barkhudarov, Lost Foam Casting Simulation with Defect Prediction, Flow Science Inc, presented at Modeling of Casting, Welding and Advanced Solidification Processes VIII Conference, June 7-12, 1998, Catamaran Hotel, San Diego, California

9-98 M. R. Barkhudarov and C. W. Hirt, Tracking Defects, Flow Science Inc, presented at the 1st International Aluminum Casting Technology Symposium, 12-14 October 1998, Rosemont, IL

5-98 J. Righi, Computer Simulation Helps Eliminate Porosity, Die Casting Management Magazine, pp. 36-38, January 1998

3-98 P. Kapranos, M. R. Barkhudarov, D. H. Kirkwood, Modeling of Structural Breakdown during Rapid Compression of Semi-Solid Alloy Slugs, Dept. Engineering Materials, The University of Sheffield, Sheffield S1 3JD, U.K. and Flow Science Inc, USA, Presented at the 5th International Conference Semi-Solid Processing of Alloys and Composites, Colorado School of Mines, Golden, CO, 23-25 June 1998

1-98 U. Jerichow, T. Altan, and P. R. Sahm, Semi Solid Metal Forming of Aluminum Alloys-The Effect of Process Variables Upon Material Flow, Cavity Fill and Mechanical Properties, The Ohio State University, Columbus, OH, published in Die Casting Engineer, p. 26, Jan/Feb 1998

8-97 Michael Barkhudarov, High Pressure Die Casting Simulation Using FLOW-3D, Die Casting Engineer, 1997

15-97 M. R. Barkhudarov, Advanced Simulation of the Flow and Heat Transfer Process in Simultaneous Engineering, Flow Science report, presented at the Casting 1997 – International ADI and Simulation Conference, Helsinki, Finland, May 28-30, 1997

14-97 M. Ranganathan and R. Shivpuri, Reducing Scrap and Increasing Die Life in Low Pressure Die Casting through Flow Simulation and Accelerated Testing, Dept. Welding and Systems Engineering, Ohio State University, Columbus, OH, presented at 19th International Die Casting Congress & Exposition, November 3-6, 1997

13-97 J. Koke, Modellierung und Simulation der Fließeigenschaften teilerstarrter Metallegierungen, Livt Information, Institut für Verfahrenstechnik, RWTH Aachen, October 1997

10-97 J. P. Greene and J. O. Wilkes, Numerical Analysis of Injection Molding of Glass Fiber Reinforced Thermoplastics – Part 2 Fiber Orientation, Body-in-White Center, General Motors Corp. and Dept. Chemical Engineering, University of Michigan, Polymer Engineering and Science, Vol. 37, No. 6, June 1997

9-97 J. P. Greene and J. O. Wilkes, Numerical Analysis of Injection Molding of Glass Fiber Reinforced Thermoplastics. Part 1 – Injection Pressures and Flow, Manufacturing Center, General Motors Corp. and Dept. Chemical Engineering, University of Michigan, Polymer Engineering and Science, Vol. 37, No. 3, March 1997

8-97 H. Grazzini and D. Nesa, Thermophysical Properties, Casting Simulation and Experiments for a Stainless Steel, AT Systemes (Renault) report, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

7-97 R. Van Tol, L. Katgerman and H. E. A. Van den Akker, Horizontal Mould Filling of a Thin Wall Aluminum Casting, Laboratory of Materials report, Delft University, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

6-97 M. R. Barkhudarov, Is Fluid Flow Important for Predicting Solidification, Flow Science report, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

22-96 Grun, Gerd-Ulrich & Schneider, Wolfgang, 3-D Modeling of the Start-up Phase of DC Casting of Sheet Ingots, Proc TMS Annual Meeting, 1996, pp. 971-981

9-96 M. R. Barkhudarov and C. W. Hirt, Thixotropic Flow Effects under Conditions of Strong Shear, Flow Science report FSI96-00-2, to be presented at the “Materials Week ’96” TMS Conference, Cincinnati, OH, 7-10 October 1996

4-96 C. W. Hirt, A Computational Model for the Lost Foam Process, Flow Science final report, February 1996 (FSI-96-57-R2)

3-96 M. R. Barkhudarov, C. L. Bronisz, C. W. Hirt, Three-Dimensional Thixotropic Flow Model, Flow Science report, FSI-96-00-1, published in the proceedings of (pp. 110- 114) and presented at the 4th International Conference on Semi-Solid Processing of Alloys and Composites, The University of Sheffield, 19-21 June 1996

1-96 M. R. Barkhudarov, J. Beech, K. Chang, and S. B. Chin, Numerical Simulation of Metal/Mould Interfacial Heat Transfer in Casting, Dept. Mech. & Process Engineering, Dept. Engineering Materials, University of Sheffield and Flow Science Inc, 9th Int. Symposium on Transport Phenomena in Thermal-Fluid Engineering, June 25-28, 1996, Singapore

11-95 Barkhudarov, M. R., Hirt, C.W., Casting Simulation Mold Filling and Solidification-Benchmark Calculations Using FLOW-3D, Modeling of Casting, Welding, and Advanced Solidification Processes VII, pp 935-946

10-95 Grun, Gerd-Ulrich, & Schneider, Wolfgang, Optimal Design of a Distribution Pan for Level Pour Casting, Proc TMS Annual Meeting, 1995, pp. 1061-1070

9-95 E. Masuda, I. Itoh, K. Haraguchi, Application of Mold Filling Simulation to Die Casting Processes, Honda Engineering Co., Ltd., Tochigi, Japan, presented at the Modelling of Casting, Welding and Advanced Solidification Processes VII, The Minerals, Metals & Materials Society, 1995

6-95 K. Venkatesan, Experimental and Numerical Investigation of the Effect of Process Parameters on the Erosive Wear of Die Casting Dies, presented for Ph.D. degree at Ohio State University, 1995

5-95 J. Righi, A. F. LaCamera, S. A. Jones, W. G. Truckner, T. N. Rouns, Integration of Experience and Simulation Based Understanding in the Die Design Process, Alcoa Technical Center, Alcoa Center, PA 15069, presented by the North American Die Casting Association, 1995

2-95 K. Venkatesan and R. Shivpuri, Numerical Simulation and Comparison with Water Modeling Studies of the Inertia Dominated Cavity Filling in Die Casting, NUMIFORM, 1995

1-95 K. Venkatesan and R. Shivpuri, Numerical Investigation of the Effect of Gate Velocity and Gate Size on the Quality of Die Casting Parts, NAMRC, 1995.

15-94 D. Liang, Y. Bayraktar, S. A. Moir, M. Barkhudarov, and H. Jones, Primary Silicon Segregation During Isothermal Holding of Hypereutectic AI-18.3%Si Alloy in the Freezing Range, Dept. of Engr. Materials, U. of Sheffield, Metals and Materials, February 1994

13-94 Deniece Korzekwa and Paul Dunn, A Combined Experimental and Modeling Approach to Uranium Casting, Materials Division, Los Alamos National Laboratory, presented at the Symposium on Liquid Metal Processing and Casting, El Dorado Hotel, Santa Fe, New Mexico, 1994

12-94 R. van Tol, H. E. A. van den Akker and L. Katgerman, CFD Study of the Mould Filling of a Horizontal Thin Wall Aluminum Casting, Delft University of Technology, Delft, The Netherlands, HTD-Vol. 284/AMD-Vol. 182, Transport Phenomena in Solidification, ASME 1994

11-94 M. R. Barkhudarov and K. A. Williams, Simulation of ‘Surface Turbulence’ Fluid Phenomena During the Mold Filling Phase of Gravity Castings, Flow Science Technical Note #41, November 1994 (FSI-94-TN41)

10-94 M. R. Barkhudarov and S. B. Chin, Stability of a Numerical Algorithm for Gas Bubble Modelling, University of Sheffield, Sheffield, U.K., International Journal for Numerical Methods in Fluids, Vol. 19, 415-437 (1994)

16-93 K. Venkatesan and R. Shivpuri, Numerical Simulation of Die Cavity Filling in Die Castings and an Evaluation of Process Parameters on Die Wear, Dept. of Industrial Systems Engineering, Presented by: N.A. Die Casting Association, Cleveland, Ohio, October 18-21, 1993

15-93 K. Venkatesen and R. Shivpuri, Numerical Modeling of Filling and Solidification for Improved Quality of Die Casting: A Literature Survey (Chapters II and III), Engineering Research Center for Net Shape Manufacturing, Report C-93-07, August 1993, Ohio State University

1-93 P-E Persson, Computer Simulation of the Solidification of a Hub Carrier for the Volvo 800 Series, AB Volvo Technological Development, Metals Laboratory, Technical Report No. LM 500014E, Jan. 1993

13-92 D. R. Korzekwa, M. A. K. Lewis, Experimentation and Simulation of Gravity Fed Lead Castings, in proceedings of a TMS Symposium on Concurrent Engineering Approach to Materials Processing, S. N. Dwivedi, A. J. Paul and F. R. Dax, eds., TMS-AIME Warrendale, p. 155 (1992)

12-92 M. A. K. Lewis, Near-Net-Shaiconpe Casting Simulation and Experimentation, MST 1992 Review, Los Alamos National Laboratory

2-92 M. R. Barkhudarov, H. You, J. Beech, S. B. Chin, D. H. Kirkwood, Validation and Development of FLOW-3D for Casting, School of Materials, University of Sheffield, Sheffield, UK, presented at the TMS/AIME Annual Meeting, San Diego, CA, March 3, 1992

1-92 D. R. Korzekwa and L. A. Jacobson, Los Alamos National Laboratory and C.W. Hirt, Flow Science Inc, Modeling Planar Flow Casting with FLOW-3D, presented at the TMS/AIME Annual Meeting, San Diego, CA, March 3, 1992

12-91 R. Shivpuri, M. Kuthirakulathu, and M. Mittal, Nonisothermal 3-D Finite Difference Simulation of Cavity Filling during the Die Casting Process, Dept. Industrial and Systems Engineering, Ohio State University, presented at the 1991 Winter Annual ASME Meeting, Atlanta, GA, Dec. 1-6, 1991

3-91 C. W. Hirt, FLOW-3D Study of the Importance of Fluid Momentum in Mold Filling, presented at the 18th Annual Automotive Materials Symposium, Michigan State University, Lansing, MI, May 1-2, 1991 (FSI-91-00-2)

11-90 N. Saluja, O.J. Ilegbusi, and J. Szekely, On the Calculation of the Electromagnetic Force Field in the Circular Stirring of Metallic Melts, accepted in J. Appl. Physics, 1990

10-90 N. Saluja, O. J. Ilegbusi, and J. Szekely, On the Calculation of the Electromagnetic Force Field in the Circular Stirring of Metallic Molds in Continuous Castings, presented at the 6th Iron and Steel Congress of the Iron and Steel Institute of Japan, Nagoya, Japan, October 1990

9-90 N. Saluja, O. J. Ilegbusi, and J. Szekely, Fluid Flow in Phenomena in the Electromagnetic Stirring of Continuous Casting Systems, Part I. The Behavior of a Cylindrically Shaped, Laboratory Scale Installation, accepted for publication in Steel Research, 1990

8-89 C. W. Hirt, Gravity-Fed Casting, Flow Science Technical Note #20, July 1989 (FSI-89-TN20)

6-89 E. W. M. Hansen and F. Syvertsen, Numerical Simulation of Flow Behaviour in Moldfilling for Casting Analysis, SINTEF-Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology, Trondheim, Norway, Report No. STS20 A89001, June 1989

1-88 C. W. Hirt and R. P. Harper, Modeling Tests for Casting Processes, Flow Science report, Jan. 1988 (FSI-88-38-01)

2-87 C. W. Hirt, Addition of a Solidification/Melting Model to FLOW-3D, Flow Science report, April 1987 (FSI-87-33-1)

Lost Foam Casting Workspace, 소실모형주조

Lost Foam Casting Workspace Highlights, 소실모형주조

  • 최첨단 Foam 잔여물 추적
  • 진보된 Foam 증발 및 금속 유동 모델링
  • 응고, 다공성 및 표면 결함 분석

Workspace Overview

Lost Foam Casting Workspace(소실모형주조) 는 Lost Foam Casting에 필요한 충진, 응고 및 냉각 하위 프로세스를 시뮬레이션하는 모든 도구를 제공합니다. 각 하위 프로세스는 해석 엔지니어가 사용하기 쉬운 인터페이스를 제공하도록 맞춤화된 템플릿 디자인을 기반으로합니다.

Lost Foam Casting 의 결함은 충진 프로파일에서 추적할 수 있기 때문에  FLOW-3D  CAST 의 용탕유동 및 소실모형(foam)의 연소 시뮬레이션의 탁월한 정확도는 고품질의 Lost Foam Casting 주물을 생산하는 데 귀중한 통찰력을 제공합니다. 기포. 잔류물 형성과 같은 주입 결함은 최종 주조에서 정확하게 추적되고 처리됩니다.

Lost Foam Casting Workspace | FLOW-3D CAST
Lost Foam Residue Tracking – Filling Simulation | FLOW-3D CAST
Lost Foam Impeller Tree – Filling Simulation | FLOW-3D CAST
Lost Foam Residue Simulation | FLOW-3D CAST

PROCESSES MODELED

  • Filling
  • Solidification
  • Cooling

FLEXIBLE MESHING

  • Structured meshing for fast, easy generation
  • Multi-block meshing for localized accuracy control
  • Foam-conforming meshes for memory optimization

MOLD MODELING

  • Ceramic filters
  • Inserts – standard and porous
  • Air vents
  • Chills
  • Insulating and exothermic sleeves
  • Moving ladles and stoppers

ADVANCED SOLIDIFICATION

  • Chemistry-based solidification
  • Dimensionless Niyama criteria
  • Cooling rates, SDAS, grain size mechanical properties

FILLING ACCURACY

  • Foam/melt interface tracking
  • Gas/bubble entrapment
  • Automatic melt flow drag calculation in filters

DEFECT PREDICTION

  • Foam residue defect tracking
  • Cold shuts
  • Porosity prediction
  • Shrinkage
  • Hot spots

DYNAMIC SIMULATION CONTROL

  • Probe-controlled pouring control

COMPLETE ANALYSIS PACKAGE

  • Animations with multi-viewports – 3D, 2D, history plots, volume rendering
  • Porosity analysis tool
  • Side-by-side simulation results comparison
  • Sensors for measuring melt temperature, solid fraction
  • Particle tracers
  • Batch post-processing
  • Report generation

Additive manufacturing

LPBF 시뮬레이션 순서

  • Powder settling
  • Powder spreading
  • Laser scan tracks on a powder bed

선택적 레이저 용해(Melting) : 단일 트랙 모델링

  • Power Bed spreading : 파우더 베드(Bed)압축의 파라메트릭 분석
    – 블레이드(Blade) 모션
    – 롤러(Roller) 속도와 방향

용융 풀(Melt pool) 모델링

  • 용융 풀의 진화(Evolution of the melt pool)
  • 시뮬레이션 및 실험적 단면(Cross-section) 검증

다층 SLM프로세스 : TU덴마크

추가 특성 – 고객 요청

  • 두 재료의 온도 의존성 재료 특성
  • 유체 영역과 고체 영역 사이의 접촉각 설정

Coating field – Roll Coating

Roll Coating (롤 코팅)

  • 응용
    – 접착제
    – 밀폐제
    – 섬유 산업
  • 공정 파라미터
    – 롤 속도
    – 기질 속도
    – 유동학
  • 품질 관리
    – 코팅 두께
    – 결함 최소화

  • 손쉬운 설정의 시뮬레이션
    – STL 가져 오기 또는 기본 요소로 생성
    – 간단한 직사각형 격자

롤의 속도가 코팅에 미치는 영향

  • 전형적으로 유입구에 코팅액이 적당하게 있는 상황
  • 롤의 회전이 역으로 작동하는 상황
  • 유입구에 코팅액이 적게 들어오는 상황

공기가 유입된 롤 코팅


FLOW-3D What’s New Ver.12.0

FLOW-3D v12.0은 그래픽 사용자 인터페이스 (GUI)의 설계 및 기능에서 매우 큰 변화를 이룬 제품으로 모델 설정을 단순화하고 사용자 워크 플로를 향상시킵니다. 최첨단 Immersed Boundary Method(침수경계 방법)은 FLOW-3D v12.0 솔루션의 정확성을 높여줍니다. 다른 주요 기능으로는 슬러지 침강 모델, 2-Fluid 2-Temperature 모델 및 Steady State Accelerator가 있으며,이를 통해 사용자는 자유 표면 흐름을 더욱 빠르게 모델링 할 수 있습니다.

Physical and Numerical Model

Immersed boundary method

힘과 에너지 손실에 대한 정확한 예측은 고체 주위의 흐름과 관련된 많은 엔지니어링 문제를 모델링하는 데 중요합니다. 새 릴리스 FLOW-3 Dv1.2.0에는 이러한 문제점 해결을 위해 설계된 새로운 고스트 셀 기반 Immersed Boundary Method (IBM)가 있습니다. IBM은 내 외부 흐름 해석을 위해, 벽 근처에서 보다 정확한 해를 제공하여 드래그 앤 리프트 힘의 계산을 향상시킵니다.힘과 에너지 손실의 정확한 예측은 고체 주위의 흐름을 포함하는 많은 공학적 문제를 모델링 하는데 중요합니다.

Two-field temperature for the two-fluid model

2 유체 열전달 모델은 각 유체에 대한 에너지 전달 방정식을 분리하기 위해 확장되었습니다. 각 유체는 이제 자체 온도 변수를 가지므로 인터페이스 근처의 열 및 물질 전달 솔루션의 정확도가 향상됩니다. 인터페이스에서의 열전달은 이제 시간의 표 함수가 될 수 있는 사용자 정의 열전달 계수에 의해 제어됩니다.

블로그 보기

Sludge settling model

새로운 슬러지 정착 모델은 수처리 애플리케이션에 부가되어 사용자들이 수 처리 탱크와 클래리퍼의 고형 폐기물 역학을 모델링 할 수 있게 해 줍니다. 침전 속도가 분산상의 액적 크기의 함수 인 드리프트-플럭스 모델과 달리, 침전 속도는 슬러지 농도의 함수이며 기능 및 표 형식으로 입력 할 수 있습니다.

개발노트 읽기

Steady-state accelerator for free surface flows

이름에서 알 수 있듯이 정상 상태 가속기는 정상 상태 솔루션에 대한 접근을 빠르게합니다.
이것은 작은 진폭 중력과 모세관 표면파를 감쇠시킴으로써 달성되며 자유 표면 흐름에만 적용 할 수 있습니다.

개발노트 읽기

Void particles

Void particles 가 기포 및 상 변화 모델에 추가되었습니다. Void particles는 붕괴 된 Void 영역을 나타내며, 항력 및 압력을 통해 유체와 상호 작용하는 작은 기포로 작용합니다. 주변 유체 압력에 따라 크기가 변하고 시뮬레이션이 끝날 때의 최종 위치는 공기 유입 가능성을 나타냅니다.

Sediment scour model

퇴적물 수송 및 침식 모델은 정확성과 안정성을 향상시키기 위해 정비되었습니다. 특히 퇴적물 종의 질량 보존이 크게 개선되었습니다.

개발 노트 읽기>

Outflow pressure boundary condition

고정 압력 경계 조건에는 압력 및 유체 분율을 제외한 모든 유량이 해당 경계의 상류의 유량 조건을 반영하는 ‘유출’옵션이 포함됩니다. 유출 압력 경계 조건은 고정 압력 및 연속 경계 조건의 하이브리드입니다.

Moving particle sources

시뮬레이션 중에 입자 소스를 이동할 수 있습니다. 시간에 따른 병진 및 회전 속도는 표 형식으로 정의됩니다. 입자 소스의 운동은 소스에서 방출 된 입자의 초기 속도에 추가됩니다.

Variable center of gravity

기변 무게중심은 중력 및 비관 성 기준 프레임 모델에서, 시간의 함수로서 무게 중심의 위치는 외부 파일에서 테이블로서 정의 될 수있다. 이 기능은 연료를 소비하고 분리 단계를 수행하는 로켓과 같은 모형을 모델링 할 때 유용합니다.

공기 유입 모델

가장 간단한 부피 기반 공기 유입 모델 옵션이 기존 질량 기반 모델로 대체되었습니다. 질량 기반 모델은 부피와 달리 주변 유체 압력에 따라 부피가 변화하는 동안 흡입된 공기량이 보존되기 때문에 물리학적 모델입니다.

Tracer diffusion

유동 표면에서 생성된 추적 물질은 분자 및 난류 확산 과정에 의해 확산될 수 있으며, 예를 들어 실제 오염 물질의 동작을 모방한다.

Model Setup

Simulation units

온도를 포함하여 단위 시스템은 완전히 정의해야하는데 표준 단위 시스템이 제공됩니다. 또한 사용자는 다양한 옵션 중에서 질량, 시간 및 길이 단위를 정의 할 수 있으므로 사용자 정의가 가능한 편리한 단위를 사용할 수 있습니다. 사용자는 압력이 게이지 또는 절대 단위로 정의되는지 여부도 지정해야합니다. 기본 시뮬레이션 단위는 기본 설정에서 설정할 수 있습니다. 단위를 완전히 정의하면 FLOW-3D 가 물리량의 기본값을 정의하고 범용 상수를 설정하여 사용자가 요구하는 작업량을 최소화 할 수 있습니다.

Shallow water model

Manning’s roughness in shallow water model

Manning의 거칠기 계수는 지형 표면의 전단 응력 평가를 위해 얕은 물 모델에서 구현되었습니다. 표면 결함의 크기를 기반으로 기존 거칠기 모델을 보완하며 이 모델과 함께 사용할 수 있습니다. 표준 거칠기와 마찬가지로 매닝 계수는 구성 요소 또는 하위 구성 요소의 속성이거나 지형 래스터 데이터 세트에서 가져올 수 있습니다.

Mesh generation

하단 및 상단 경계 좌표의 정의만으로 수직 방향의 메시 설정이 단순화되었습니다.

Component transformations

사용자는 이제 여러 하위 구성 요소로 구성된 구성 요소에 회전, 변환 및 스케일링 변환을 적용하여 복잡한 형상 어셈블리 설정 프로세스를 단순화 할 수 있습니다. GMO (General Moving Object) 구성 요소의 경우, 이러한 변환을 구성 요소의 대칭 축과 정렬되도록 신체에 맞는 좌표계에 적용 할 수 있습니다.

Changing the number of threads at runtime

시뮬레이션 중에 솔버가 사용하는 스레드 수를 변경하는 기능이 런타임 옵션 대화 상자에 추가되어 사용 가능한 스레드를 추가하거나 다른 태스크에 자원이 필요한 경우 스레드 수를 줄일 수 있습니다.

Probe-controlled heat sources

활성 시뮬레이션 제어가 형상 구성 요소와 관련된 heat sources로 확장되었습니다. 히스토리 프로브로 열 방출을 제어 할 수 있습니다.

Time-dependent temperature at sources     

질량 및 질량 / 운동량 소스의 유체 온도는 이제 테이블 입력을 사용하여 시간의 함수로 정의 할 수 있습니다.

Emissivity coefficients

공극으로의 복사 열 전달을위한 방사율 계수는 이제 사용자가 방사율과 스테판-볼츠만 상수를 지정하도록 요구하지 않고 직접 정의됩니다. 후자는 이제 단위 시스템을 기반으로 솔버에 의해 자동으로 설정됩니다.

Output

  • 등속 필드 솔버 옵션을 사용할 때 유량 속도를 선택한 데이터 로 출력 할 수 있습니다 .
  • 벽 접착력으로 인한 지오메트리 구성 요소의 토크 는 기존 벽 접착력의 출력 외에도 일반 이력 데이터에 별도의 수량으로 출력됩니다.
  • 난류 모델 출력이 요청 될 때 난류 에너지 및 소산과 함께 전단 속도 및 y +가 선택된 데이터로 자동 출력됩니다 .
  • 공기 유입 모델 출력에 몇 가지 수량이 추가되었습니다. 자유 표면을 포함하는 모든 셀에서 혼입 된 공기 및 빠져 나가는 공기의 체적 플럭스가 재시작 및 선택된 데이터로 출력되어 사용자에게 공기가 혼입 및 탈선되는 위치 및 시간에 대한 자세한 정보를 제공합니다. 전체 계산 영역 및 각 샘플링 볼륨 에 대해이 두 수량의 시간 및 공간 통합 등가물 이 일반 히스토리 로 출력됩니다.
  • 솔버의 출력 파일 flsgrf 의 최종 크기 는 시뮬레이션이 끝날 때보 고됩니다.
  • 2 유체 시뮬레이션의 경우, 기존의 출력 수량 유체 체류 시간 및 유체 가 이동 한 거리는 이제 유체 # 1 및 # 2와 유체의 혼합물에 대해 별도로 계산됩니다.
  • 질량 입자의 경우 각 종의 총 부피와 질량이 계산되어 전체 계산 영역, 샘플링 볼륨 및 플럭스 표면에 대한 일반 히스토리 로 출력되어 입자 종 수에 대한 현재 출력을 보완합니다.
  • 예를 들어 사용자가 가스 미순환을 식별하고 연료 탱크의 환기 시스템을 설계하는 데 도움이 되도록 마지막 국부적 가스 압력이 옵션 출력량으로 추가되었습니다. 이 양은 유체가 채워지기 전에 셀의 마지막 간극 압력을 기록하며, 단열 버블 모델과 함께 사용됩니다.

New Customizable Source Routines

사용자 정의 가능한 새로운 소스 루틴이 추가되었으며 사용자의 개발 환경에서 액세스 할 수 있습니다.

소스 루틴 이름설명
cav_prod_cal캐비 테이션 생산 및 확산 속도
sldg_uset슬러지 정착 속도
phchg_mass_flux증발 및 응축에 의한 질량 흐름
flhtccl유체#1과#2사이의 열 전달 계수
dsize_cal2상 유동에서의 동적 낙하 크기 모델의 충돌 및 이탈율
elstc_custom.점탄성 유체에 대한 응력 방정식의 소스 용어

Brand New User Interface

FLOW-3D의 사용자 인터페이스가 완전히 재설계되어 사용자의 작업 흐름을 획기적으로 간소화하는 최신의 타일 구조를 제공합니다.

Dock widgets 설정

Physics, Fluids, Mesh 및 FAVOR ™를 포함한 모든 설정 작업이 형상 창 주위의 dock widgets으로 변환되어 모델 설정을 단일 탭으로 압축 할 수 있습니다. 이 전환을 통해 이전 버전의 복잡한 트리가 훨씬 깔끔하고 효율적인 메뉴 표시로 바뀌어 모델 설정 탭을 떠나지 않고도 모든 매개 변수에 쉽게 액세스 할 수 있습니다.

New Model Setup icons
With our new Model Setup design comes new icons, representing each step of the setup process.
New Physics icons
Our Physics icons are designed to be easily differentiated from one another at a glance, while providing clear visual representation of each model’s purpose and use.

RSS feed

새 RSS 피드부터 FLOW-3D v12.0 의 시뮬레이션 관리자 탭이 개선되었습니다 . FLOW-3D 를 시작하면 사용자에게 Flow Science의 최신 뉴스, 이벤트 및 블로그 게시물이 표시됩니다.

Configurable simulation monitor

시뮬레이션을 실행할 때 중요한 작업은 모니터링입니다. FLOW-3Dv12.0에서는 사용자가 시뮬레이션을 더 잘 모니터링할 수 있도록 Simulation Manager의 플로팅 기능이 향상되었습니다. 사용자는 시뮬레이션 런타임 그래프를 통해 모니터링할 사용 가능한 모든 일반 기록 데이터 변수를 선택하고 각 그래프에 여러 변수를 추가할 수 있습니다. 이제 런타임에서 사용할 수 있는 일반 기록 데이터는 다음과 같습니다.

  • 최소/최대 유체 온도
  • 프로브 위치의 온도
  • 유동 표면 위치에서의 유량
  • 시뮬레이션 진단(예:시간 단계, 안정성 한계)
Runtime plots of the flow rate at the gates of the large dam / Large dam with flux surfaces at the gates

Conforming mesh visualization

사용자는 이제 새로운 FAVOR ™ 독 위젯을 통해 적합한 메쉬 블록을 시각화 할 수 있습니다 .

Large raster and STL data

데이터를 처리하는 데 걸리는 시간으로 인해 큰 형상 데이터를 처리하는 것은 어려울 수 있습니다. 대형 지오메트리 데이터를 처리하는 데 여전히 상당한 시간이 소요될 수 있지만 FLOW-3D는 이제 이러한 대형 데이터 세트를 백그라운드 작업으로로드하여 사용자가 데이터를 처리하는 동안 완벽하게 응답하고 중단없는 인터페이스에서 계속 작업 할 수 있습니다.

Coating Application/코팅분야 응용

해석 조건

  • Viscosity(점도) = 0.204 Pa-s
  • Density(밀도) = 965 kg/m^3
  • Surface tension(표면 장력) = 0.035N/m
  • Roll coating

물리 모델

  • Surface tension(표면 장력) 모델
  • Viscosity(점도)
  • Moving Objects(운동)

Classic Inlet Flooded Regime

Revers Operating Regime

Inlet Starved Operating Regime

  • 2D 시뮬레이션은 작동 코팅 윈도우의 빠른 평가를 제공
  • 계단식, 공기 유입, 기아 및 런백을 식별
  • 리빙(Ribbing)은 3D 분석이 필요

해석 결과

FLOW-3D 교육 안내

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FLOW-3D CFD EDUCATION


(주)에스티아이씨앤디에서는 FLOW-3D 제품군의 사용자 교육을 지원하고 있습니다. 홈페이지에 안내되어 있는 교육 일정과 교육신청 절차를 참고하시어 교육을 받으실 수 있습니다.

  • 교육 과정명 : 수리 분야

댐, 하천의 여수로, 수문 등 구조물 설계 및 방류, 월류 등 흐름 검토를 하기 위한 유동 해석 방법을 소개하는 교육 과정입니다. 유입 조건(수위, 유량 등)과 유출 조건에 따른 방류량 및 유속, 압력 분포 등 유체의 흐름을 검토를 할 수 있도록 관련 예제를 통해 적절한 기능을 습득하실 수 있습니다.

  • 교육 과정명 : 수처리 분야

정수처리 및 하수처리 공정에서 각 시설물들의 특성에 맞는 최적 운영조건 검토 및 설계 검토을 위한 유동해석 방법을 소개하는 교육 과정입니다. 취수부터 시작하여 혼화지, 분배수로, 응집지, 침전지, 여과지, 정수지, 협기조, 호기조, 소독조 등 각 공정별 유동 특성을 검토하기 위한 해석 모델을 설정하는 방법에 대해 알려드립니다.

  • 교육 과정명 : 주조 분야

주조 분야 사용자들이 쉽게 접근할 수 있도록 각 공정별로 해석 절차 및 해석 방법을 소개하는 교육 과정입니다. 고압다이캐스팅, 저압다이캐스팅, 경동주조, 중력주조, 원심주조, 정밀주조 등 주조 공법 별 관련 예제를 통해 적절한 기능들을 습득할 수 있도록 도와 드립니다.

  • 교육 과정명 : Micro/Bio/Nano Fluidics 분야

점성력 및 모세관력 같은 유체 표면에 작용하는 힘이 지배적인 미세 유동의 특성을 정확하게 표현할 수 있는 해석 방법에 대해 소개하는 교육 과정입니다. 열적, 전기적 물리 현상을 구현할 수 있도록 관련 예제와 함께 해석 방법을 알려드립니다.

  • 교육 과정명 : 코팅 분야 과정

코팅 공정에 따른 코팅액의 두께, 균일도, 유동 특성 분석을 위한 해석 방법을 소개하는 교육 과정입니다. Slide coating, Dip coating, Spin coating, Curtain coating, Slot coating, Roll coating, Gravure coating 등 각 공정별 예제와 함께 적절한 기능을 습득하실 수 있도록 도와 드립니다.

  • 교육 과정명 : 레이저 용접 분야

레이저 용접 해석을 하기 위한 물리 모델과 용접 조건들을 설정하는 방법에 대해 소개하는 교육 과정입니다. 해석을 통해 용접 공정을 최적화할 수 있도록 관련 예제와 함께 적절한 기능들을 습득할 수 있도록 도와 드립니다.

  • 교육 과정명 : 3D프린팅 분야 과정

Powder Bed Fusion(PBF)와 Directed Energy Deposition(DED) 공정에 대한 해석 방법을 소개하는 교육 과정입니다. 파우더 적층 및 레이저 빔을 조사하면서 동시에 금속 파우더 용융지가 적층되는 공정을 해석하는 방법을 관련 예제와 함께 습득하실 수 있습니다.

  • 교육 과정명 : 해안/해양 분야

해안, 항만, 해양 구조물에 대한 파랑의 영향 및 유체의 수위, 유속, 압력의 영향을 예측할 수 있는 해석 방법을 소개하는 과정입니다. 항주파, 슬로싱, 계류 등 해안, 해양, 에너지, 플랜트 분야 구조물 설계 및 검토에 필요한 유동해석을 하실 수 있는 방법을 알려드립니다. 각 현상에 대한 적절한 예제를 통해 기능을 습득하실 수 있습니다.

  • 교육 과정명 : 우주/항공 분야

항공기 및 우주선의 연료 탱크와 추진체 관리장치의 내부 유동, 엔진 및 터빈 노즐 내부의 유동해석을 하실 수 있도록 관련 메뉴에 대한 설명, 설정 방법을 소개하는 과정입니다. 경계조건 설정, Mesh 방법 등 유동해석을 위한 기본적인 내용과 함께 관련 예제를 통해 기능들을 습득하실 수 있습니다.

기타 고객 맞춤형 과정

상기 과정 이외의 경우 고객의 사업 업무 환경에 적합한 사례를 중심으로 맞춤형 교육을 실시합니다. 필요하신 부분이 있으시면 언제든지 교육 담당자에게 연락하여 협의해 주시기 바랍니다.

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1. 연간교육 일정
FLOW-3D 연간교육일정

2. 교육 내용 : FLOW-3D Basic
  1. FLOW-3D 소개 및 이론
    • FLOW-3D 소개  – 연혁, 특징 등
    • FLOW-3D 기본 개념
      • VOF
      • FAVOR
    • 해석사례 리뷰
  2. GUI 소개 및 사용법
    • 해석 모델 작성법  – 물리 모델 설정
      • 모델 형상 정의
      • 격자 분할
      • 초기 유체 지정
      • 경계 조건 설정
    • 해석 결과 분석 방법  – 해석 모델 설명
  3. 해석 모델 작성 실습
    • 해석 모델 작성 실습  – 격자 분할
      • 물리 모델 설정
      • 모델 형상 및 초기 조건 정의
      • 경계 조건 설정
      • 해석 과정 모니터링
      • 해석 결과 분석
    • 질의 응답 및 토의

3. 교육 과정 : FLOW-3D Advanced
  1. Physics Ⅰ
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    • Drift flux
    • Scalars
    • Sediment scour
    • Shallow water
  2. Physics Ⅱ
    • Gravity and non-inertial reference frame
    • Heat transfer
    • Moving objects
    • Solidification
  3. FLOW-3D POST (Post-processor)
    • FLOW-3D POST 소개
    • Interface Basics
    • 예제 실습
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  • 이메일 : flow3d@stikorea.co.kr
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오시는 길

Additive Manufacturing & Welding Bibliography

Additive Manufacturing & Welding Bibliography

다음은 적층 제조 및 용접 참고 문헌의 기술 문서 모음입니다. 이 모든 논문에는 FLOW-3D AM 결과가 나와 있습니다. FLOW-3D AM을 사용하여 적층 제조, 레이저 용접 및 기타 용접 기술에서 발견되는 프로세스를 성공적으로 시뮬레이션하는 방법에 대해 자세히 알아보십시오.

2021년 6월 25일 update

34-21   Haokun Sun, Xin Chu, Cheng Luo, Haoxiu Chen, Zhiying Liu, Yansong Zhang, Yu Zou, Selective laser melting for joining dissimilar materials: Investigations ofiInterfacial characteristics and in situ alloying, Metallurgical and Materials Transactions A, 52; pp. 1540-1550, 2021. doi.org/10.1007/s11661-021-06178-9

32-21   Shanshan Zhang, Subin Shrestha, Kevin Chou, On mesoscopic surface formation in metal laser powder-bed fusion process, Supplimental Proceedings, TMS 150th Annual Meeting & Exhibition (Virtual), pp. 149-161, 2021. doi.org/10.1007/978-3-030-65261-6_14

22-21   Patiparn Ninpetch, Pruet Kowitwarangkul, Sitthipong Mahathanabodee, Prasert Chalermkarnnon, Phadungsak Rattanadecho, Computational investigation of thermal behavior and molten metal flow with moving laser heat source for selective laser melting process, Case Studies in Thermal Engineering, 24; 100860, 2021. doi.org/10.1016/j.csite.2021.100860

19-21   M.B. Abrami, C. Ransenigo, M. Tocci, A. Pola, M. Obeidi, D. Brabazon, Numerical simulation of laser powder bed fusion processes, La Metallurgia Italiana, February; pp. 81-89, 2021.

16-21   Wenjun Ge, Jerry Y.H. Fuh, Suck Joo Na, Numerical modelling of keyhole formation in selective laser melting of Ti6Al4V, Journal of Manufacturing Processes, 62; pp. 646-654, 2021. doi.org/10.1016/j.jmapro.2021.01.005

11-21   Mohamad Bayat, Venkata K. Nadimpalli, David B. Pedersen, Jesper H. Hattel, A fundamental investigation of thermo-capillarity in laser powder bed fusion of metals and alloys, International Journal of Heat and Mass Transfer, 166; 120766, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120766

10-21   Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Thermal properties of powder beds in energy absorption and heat transfer during additive manufacturing with electron beam, Powder Technology, 381; pp. 44-54, 2021. doi.org/10.1016/j.powtec.2020.11.082

9-21   Subin Shrestha, Kevin Chou, A study of transient and steady-state regions from single-track deposition in laser powder bed fusion, Journal of Manufacturing Processes, 61; pp. 226-235, 2021. doi.org/10.1016/j.jmapro.2020.11.023

6-21   Qian Chen, Yunhao Zhao, Seth Strayer, Yufan Zhao, Kenta Aoyagi, Yuichiro Koizumi, Akihiko Chiba, Wei Xiong, Albert C. To, Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment, Additive Manufacturing, 37; 101642, 2021. doi.org/10.1016/j.addma.2020.101642

04-21   Won-Ik Cho, Peer Woizeschke, Analysis of molten pool dynamics in laser welding with beam oscillation and filler wire feeding, International Journal of Heat and Mass Transfer, 164; 120623, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120623

121-20   Yufan Zhao, Yujie Cui, Haruko Numata, Huakang Bian, Kimio Wako, Kenta Yamanaka, Kenta Aoyagi, Akihiko Chiba, Centrifugal granulation behavior in metallic powder fabrication by plasma rotating electrode process, Scientific Reports, 10; 18446, 2020. doi.org/10.1038/s41598-020-75503-w

116-20   Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Modelling of 3D concrete printing based on computational fluid dynamics, Cement and Concrete Research, 138; 106256, 2020. doi.org/10.1016/j.cemconres.2020.106256

112-20   Peng Liu, Lijin Huan, Yu Gan, Yuyu Lei, Effect of plate thickness on weld pool dynamics and keyhole-induced porosity formation in laser welding of Al alloy, The International Journal of Advanced Manufacturing Technology, 111; pp. 735-747, 2020. doi.org/10.1007/s00170-020-05818-5

108-20   Fan Chen, Wentao Yan, High-fidelity modelling of thermal stress for additive manufacturing by linking thermal-fluid and mechanical models, Materials & Design, 196; 109185, 2020. doi.org/10.1016/j.matdes.2020.109185

104-20   Yunfu Tian, Lijun Yang, Dejin Zhao, Yiming Huang, Jiajing Pan, Numerical analysis of powder bed generation and single track forming for selective laser melting of SS316L stainless steel, Journal of Manufacturing Processes, 58; pp. 964-974, 2020. doi.org/10.1016/j.jmapro.2020.09.002

100-20   Raphaël Comminal, Sina Jafarzadeh, Marcin Serdeczny, Jon Spangenberg, Estimations of interlayer contacts in extrusion additive manufacturing using a CFD model, International Conference on Additive Manufacturing in Products and Applications (AMPA), Zurich, Switzerland, September 1-3: Industrializing Additive Manufacturing, pp. 241-250, 2020. doi.org/10.1007/978-3-030-54334-1_17

97-20   Paree Allu, CFD simulation for metal Additive Manufacturing: Applications in laser- and sinter-based processes, Metal AM, 6.4; pp. 151-158, 2020.

95-20   Yufan Zhao, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Role of operating and environmental conditions in determining molten pool dynamics during electron beam melting and selective laser melting, Additive Manufacturing, 36; 101559, 2020. doi.org/10.1016/j.addma.2020.101559

94-20   Yan Zeng, David Himmler, Peter Randelzhofer, Carolin Körner, Processing of in situ Al3Ti/Al composites by advanced high shear technology: influence of mixing speed, The International Journal of Advanced Manufacturing Technology, 110; pp. 1589-1599, 2020. doi.org/10.1007/s00170-020-05956-w

93-20   H. Hamed Zargari, K. Ito, M. Kumar, A. Sharma, Visualizing the vibration effect on the tandem-pulsed gas metal arc welding in the presence of surface tension active elements, International Journal of Heat and Mass Transfer, 161; 120310, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.120310

90-20   Guangxi Zhao, Jun Du, Zhengying Wei, Siyuan Xu, Ruwei Geng, Numerical analysis of aluminum alloy fused coating process, Journal of the Brazilian Society of Mechanical Science and Engineering, 42; 483, 2020. doi.org/10.1007/s40430-020-02569-y

85-20   Wenkang Huang, Hongliang Wang, Teresa Rinker, Wenda Tan, Investigation of metal mixing in laser keyhold welding of dissimilar metals, Materials & Design, 195; 109056, 2020. doi.org/10.1016/j.matdes.2020.109056

82-20   Pan Lu, Zhang Cheng-Lin, Wang Liang, Liu Tong, Liu Jiang-lin, Molten pool structure, temperature and velocity flow in selective laser melting AlCu5MnCdVA alloy, Materials Research Express, 7; 086516, 2020. doi.org/10.1088/2053-1591/abadcf

80-20   Yujie Cui, Yufan Zhao, Haruko Numata, Huakang Bian, Kimio Wako, Kento Yamanaka, Kenta Aoyagi, Chen Zhang, Akihiko Chiba, Effects of plasma rotating electrode process parameters on the particle size distribution and microstructure of Ti-6Al-4 V alloy powder, Powder Technology, 376; pp. 363-372, 2020. doi.org/10.1016/j.powtec.2020.08.027

78-20   F.Q. Liu, L. Wei, S.Q. Shi, H.L. Wei, On the varieties of build features during multi-layer laser directed energy deposition, Additive Manufacturing, 36; 101491, 2020. doi.org/10.1016/j.addma.2020.101491

75-20   Nannan Chen, Zixuan Wan, Hui-Ping Wang, Jingjing Li, Joshua Solomon, Blair E. Carlson, Effect of Al single bond Si coating on laser spot welding of press hardened steel and process improvement with annular stirring, Materials & Design, 195; 108986, 2020. doi.org/10.1016/j.matdes.2020.108986

72-20   Yujie Cui, Kenta Aoyagi, Yufan Zhao, Kenta Yamanaka, Yuichiro Hayasaka, Yuichiro Koizumi, Tadashi Fujieda, Akihiko Chiba, Manufacturing of a nanosized TiB strengthened Ti-based alloy via electron beam powder bed fusion, Additive Manufacturing, 36; 101472, 2020. doi.org/10.1016/j.addma.2020.101472

64-20   Dong-Rong Liu, Shuhao Wang, Wentao Yan, Grain structure evolution in transition-mode melting in direct energy deposition, Materials & Design, 194; 108919, 2020. doi.org/10.1016/j.matdes.2020.108919

61-20   Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Influence of processing parameters on the layer geometry in 3D concrete printing: Experiments and modelling, 2nd RILEM International Conference on Concrete and Digital Fabrication, RILEM Bookseries, 28; pp. 852-862, 2020. doi.org/10.1007/978-3-030-49916-7_83

60-20   Marcin P. Serdeczny, Raphaël Comminal, Md. Tusher Mollah, David B. Pedersen, Jon Spangenberg, Numerical modeling of the polymer flow through the hot-end in filament-based material extrusion additive manufacturing, Additive Manufacturing, 36; 101454, 2020. doi.org/10.1016/j.addma.2020.101454

58-20   H.L. Wei, T. Mukherjee, W. Zhang, J.S. Zuback, G.L. Knapp, A. De, T. DebRoy, Mechanistic models for additive manufacturing of metallic components, Progress in Materials Science, preprint, 2020. doi.org/10.1016/j.pmatsci.2020.100703

55-20   Masoud Mohammadpour, Experimental study and numerical simulation of heat transfer and fluid flow in laser welded and brazed joints, Thesis, Southern Methodist University, Dallas, TX, US; Available in Mechanical Engineering Research Theses and Dissertations, 24, 2020.

48-20   Masoud Mohammadpour, Baixuan Yang, Hui-Ping Wang, John Forrest, Michael Poss, Blair Carlson, Radovan Kovacevica, Influence of laser beam inclination angle on galvanized steel laser braze quality, Optics and Laser Technology, 129; 106303, 2020. doi.org/10.1016/j.optlastec.2020.106303

34-20   Binqi Liu, Gang Fang, Liping Lei, Wei Liu, A new ray tracing heat source model for mesoscale CFD simulation of selective laser melting (SLM), Applied Mathematical Modeling, 79; pp. 506-520, 2020. doi.org/10.1016/j.apm.2019.10.049

27-20   Xuesong Gao, Guilherme Abreu Farira, Wei Zhang and Kevin Wheeler, Numerical analysis of non-spherical particle effect on molten pool dynamics in laser-powder bed fusion additive manufacturing, Computational Materials Science, 179, art. no. 109648, 2020. doi.org/10.1016/j.commatsci.2020.109648

26-20   Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka and Akihiko Chiba, Isothermal γ → ε phase transformation behavior in a Co-Cr-Mo alloy depending on thermal history during electron beam powder-bed additive manufacturing, Journal of Materials Science & Technology, 50, pp. 162-170, 2020. doi.org/10.1016/j.jmst.2019.11.040

21-20   Won-Ik Cho and Peer Woizeschke, Analysis of molten pool behavior with buttonhole formation in laser keyhole welding of sheet metal, International Journal of Heat and Mass Transfer, 152, art. no. 119528, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.119528

06-20  Wei Xing, Di Ouyang, Zhen Chen and Lin Liu, Effect of energy density on defect evolution in 3D printed Zr-based metallic glasses by selective laser melting, Science China Physics, Mechanics & Astronomy, 63, art. no. 226111, 2020. doi.org/10.1007/s11433-019-1485-8

04-20   Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

02-20   Dongsheng Wu, Shinichi Tashiro, Ziang Wu, Kazufumi Nomura, Xueming Hua, and Manabu Tanaka, Analysis of heat transfer and material flow in hybrid KPAW-GMAW process based on the novel three dimensional CFD simulation, International Journal of Heat and Mass Transfer, 147, art. no. 118921, 2020. doi.org/10.1016/j.ijheatmasstransfer.2019.118921

01-20   Xiang Huang, Siying Lin, Zhenxiang Bu, Xiaolong Lin, Weijin Yi, Zhihong Lin, Peiqin Xie, and Lingyun Wang, Research on nozzle and needle combination for high frequency piezostack-driven dispenser, International Journal of Adhesion and Adhesives, 96, 2020. doi.org/10.1016/j.ijadhadh.2019.102453

88-19   Bo Cheng and Charles Tuffile, Numerical study of porosity formation with implementation of laser multiple reflection in selective laser melting, Proceedings Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, ASME 2019 14th International Manufacturing Science and Engineering Conference, Erie, Pennsylvania, USA, June 10-14, 2019. doi.org/10.1115/MSEC2019-2891

87-19   Shuhao Wang, Lida Zhu, Jerry Ying His Fuh, Haiquan Zhang, and Wentao Yan, Multi-physics modeling and Gaussian process regression analysis of cladding track geometry for direct energy deposition, Optics and Lasers in Engineering, 127:105950, 2019. doi.org/10.1016/j.optlaseng.2019.105950

78-19   Bo Cheng, Lukas Loeber, Hannes Willeck, Udo Hartel, and Charles Tuffile, Computational investigation of melt pool process dynamics and pore formation in laser powder bed fusion, Journal of Materials Engineering and Performance, 28:11, 6565-6578, 2019. doi.org/10.1007/s11665-019-04435-y

77-19   David Souders, Pareekshith Allu, Anurag Chandorkar, and Ruendy Castillo, Application of computational fluid dynamics in developing process parameters for additive manufacturing, Additive Manufacturing Journal, 9th International Conference on 3D Printing and Additive Manufacturing Technologies (AM 2019), Bangalore, India, September 7-9, 2019.

75-19   Raphaël Comminal, Marcin Piotr Serdeczny, Navid Ranjbar, Mehdi Mehrali, David Bue Pedersen, Henrik Stang, Jon Spangenberg, Modelling of material deposition in big area additive manufacturing and 3D concrete printing, Proceedings, Advancing Precision in Additive Manufacturing, Nantes, France, September 16-18, 2019.

73-19   Baohua Chang, Zhang Yuan, Hao Cheng, Haigang Li, Dong Du 1, and Jiguo Shan, A study on the influences of welding position on the keyhole and molten pool behavior in laser welding of a titanium alloy, Metals, 9:1082, 2019. doi.org/10.3390/met9101082

57-19     Shengjie Deng, Hui-Ping Wang, Fenggui Lu, Joshua Solomon, and Blair E. Carlson, Investigation of spatter occurrence in remote laser spiral welding of zinc-coated steels, International Journal of Heat and Mass Transfer, Vol. 140, pp. 269-280, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.06.009

53-19     Mohamad Bayat, Aditi Thanki, Sankhya Mohanty, Ann Witvrouw, Shoufeng Yang, Jesper Thorborg, Niels Skat Tieldje, and Jesper Henri Hattel, Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation, Additive Manufacturing, Vol. 30, 2019. doi.org/10.1016/j.addma.2019.100835

51-19     P. Ninpetch, P. Kowitwarangkul, S. Mahathanabodee, R. Tongsri, and P. Ratanadecho, Thermal and melting track simulations of laser powder bed fusion (L-PBF), International Conference on Materials Research and Innovation (ICMARI), Bangkok, Thailand, December 17-21, 2018. IOP Conference Series: Materials Science and Engineering, Vol. 526, 2019. doi.org/10.1088/1757-899X/526/1/012030

46-19     Hongze Wang and Yu Zou, Microscale interaction between laser and metal powder in powder-bed additive manufacturing: Conduction mode versus keyhole mode, International Journal of Heat and Mass Transfer, Vol. 142, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.118473

45-19     Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, and Akihiko Chiba, Manipulating local heat accumulation towards controlled quality and microstructure of a Co-Cr-Mo alloy in powder bed fusion with electron beam, Materials Letters, Vol. 254, pp. 269-272, 2019. doi.org/10.1016/j.matlet.2019.07.078

44-19     Guoxiang Xu, Lin Li, Houxiao Wang, Pengfei Li, Qinghu Guo, Qingxian Hu, and Baoshuai Du, Simulation and experimental studies of keyhole induced porosity in laser-MIG hybrid fillet welding of aluminum alloy in the horizontal position, Optics & Laser Technology, Vol. 119, 2019. doi.org/10.1016/j.optlastec.2019.105667

38-19     Subin Shrestha and Y. Kevin Chou, A numerical study on the keyhole formation during laser powder bed fusion process, Journal of Manufacturing Science and Engineering, Vol. 141, No. 10, 2019. doi.org/10.1115/1.4044100

34-19     Dae-Won Cho, Jin-Hyeong Park, and Hyeong-Soon Moon, A study on molten pool behavior in the one pulse one drop GMAW process using computational fluid dynamics, International Journal of Heat and Mass Transfer, Vol. 139, pp. 848-859, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.05.038

30-19     Mohamad Bayat, Sankhya Mohanty, and Jesper Henri Hattel, Multiphysics modelling of lack-of-fusion voids formation and evolution in IN718 made by multi-track/multi-layer L-PBF, International Journal of Heat and Mass Transfer, Vol. 139, pp. 95-114, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.05.003

29-19     Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka, and Akihiko Chiba, Comprehensive study on mechanisms for grain morphology evolution and texture development in powder bed fusion with electron beam of Co–Cr–Mo alloy, Materialia, Vol. 6, 2019. doi.org/10.1016/j.mtla.2019.100346

28-19     Pareekshith Allu, Computational fluid dynamics modeling in additive manufacturing processes, The Minerals, Metals & Materials Society (TMS) 148th Annual Meeting & Exhibition, San Antonio, Texas, USA, March 10-14, 2019.

24-19     Simulation Software: Use, Advantages & Limitations, The Additive Manufacturing and Welding Magazine, Vol. 2, No. 2, 2019

22-19     Hunchul Jeong, Kyungbae Park, Sungjin Baek, and Jungho Cho, Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis, International Journal of Heat and Mass Transfer, Vol. 138, pp. 729-737, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.04.089

07-19   Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Numerical analysis of arc driving forces and temperature distribution in pulsed TIG welding, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 41, No. 60, 2019. doi.org/10.1007/s40430-018-1563-0

04-19   Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, Vol. 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

03-19   Dongsheng Wu, Anh Van Nguyen, Shinichi Tashiro, Xueming Hua and Manabu Tanaka, Elucidation of the weld pool convection and keyhole formation mechanism in the keyhold plasma arc welding, International Journal of Heat and Mass Transfer, Vol. 131, pp. 920-931, 2019. doi.org/10.1016/j.ijheatmasstransfer.2018.11.108

97-18   Wentao Yan, Ya Qian, Wenjun Ge, Stephen Lin, Wing Kam Liu, Feng Lin, Gregory J. Wagner, Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation, Materials & Design, 2018. doi.org/10.1016/j.matdes.2017.12.031

84-18   Bo Cheng, Xiaobai Li, Charles Tuffile, Alexander Ilin, Hannes Willeck and Udo Hartel, Multi-physics modeling of single track scanning in selective laser melting: Powder compaction effect, Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium, pp. 1887-1902, 2018.

81-18 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka and Akihiko Chiba, Molten pool behavior and effect of fluid flow on solidification conditions in selective electron beam melting (SEBM) of a biomedical Co-Cr-Mo alloy, Additive Manufacturing, Vol. 26, pp. 202-214, 2019. doi.org/10.1016/j.addma.2018.12.002

77-18   Jun Du and Zhengying Wei, Numerical investigation of thermocapillary-induced deposited shape in fused-coating additive manufacturing process of aluminum alloy, Journal of Physics Communications, Vol. 2, No. 11, 2018. doi.org/10.1088/2399-6528/aaedc7

76-18   Yu Xiang, Shuzhe Zhang, Zhengying We, Junfeng Li, Pei Wei, Zhen Chen, Lixiang Yang and Lihao Jiang, Forming and defect analysis for single track scanning in selective laser melting of Ti6Al4V, Applied Physics A, 124:685, 2018. doi.org/10.1007/s00339-018-2056-9

74-18   Paree Allu, CFD simulations for laser welding of Al Alloys, Proceedings, Die Casting Congress & Exposition, Indianapolis, IN, October 15-17, 2018.

72-18   Hunchul Jeong, Kyungbae Park, Sungjin Baek, Dong-Yoon Kim, Moon-Jin Kang and Jungho Cho, Three-dimensional numerical analysis of weld pool in GMAW with fillet joint, International Journal of Precision Engineering and Manufacturing, Vol. 19, No. 8, pp. 1171-1177, 2018. doi.org/10.1007/s12541-018-0138-4

60-18   R.W. Geng, J. Du, Z.Y. Wei and G.X. Zhao, An adaptive-domain-growth method for phase field simulation of dendrite growth in arc preheated fused-coating additive manufacturing, IOP Conference Series: Journal of Physics: Conference Series 1063, 012077, 2018. doi.org/10.1088/1742-6596/1063/1/012077 (Available at http://iopscience.iop.org/article/10.1088/1742-6596/1063/1/012077/pdf and in shared drive)

59-18   Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Coupling analysis of molten pool during fused coating process with arc preheating, IOP Conference Series: Journal of Physics: Conference Series 1063, 012076, 2018. doi.org/10.1088/1742-6596/1063/1/012076 (Available at http://iopscience.iop.org/article/10.1088/1742-6596/1063/1/012076/pdf and in shared drive)

58-18   Siyuan Xu, Zhengying Wei, Jun Du, Guangxi Zhao and Wei Liu, Numerical simulation and analysis of metal fused coating forming, IOP Conference Series: Journal of Physics: Conference Series 1063, 012075, 2018. doi.org/10.1088/1742-6596/1063/1/012075

55-18   Jason Cheon, Jin-Young Yoon, Cheolhee Kim and Suck-Joo Na, A study on transient flow characteristic in friction stir welding with realtime interface tracking by direct surface calculation, Journal of Materials Processing Tech., vol. 255, pp. 621-634, 2018.

54-18   V. Sukhotskiy, P. Vishnoi, I. H. Karampelas, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic drop-on-demand liquid metal additive manufacturing: System overview and modeling, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 155, 2018. doi.org/10.11159/ffhmt18.155

52-18   Michael Hilbinger, Claudia Stadelmann, Matthias List and Robert F. Singer, Temconex® – Kontinuierliche Pulverextrusion: Verbessertes Verständnis mit Hilfe der numerischen Simulation, Hochleistungsmetalle und Prozesse für den Leichtbau der Zukunft, Tagungsband 10. Ranshofener Leichtmetalltage, 13-14 Juni 2018, Linz, pp. 175-186, 2018.

38-18   Zhen Chen, Yu Xiang, Zhengying Wei, Pei Wei, Bingheng Lu, Lijuan Zhang and Jun Du, Thermal dynamic behavior during selective laser melting of K418 superalloy: numerical simulation and experimental verification, Applied Physics A, vol. 124, pp. 313, 2018. doi.org/10.1007/s00339-018-1737-8

19-18   Chenxiao Zhu, Jason Cheon, Xinhua Tang, Suck-Joo Na, and Haichao Cui, Molten pool behaviors and their influences on welding defects in narrow gap GMAW of 5083 Al-alloy, International Journal of Heat and Mass Transfer, vol. 126:A, pp.1206-1221, 2018. doi.org/10.1016/j.ijheatmasstransfer.2018.05.132

16-18   P. Schneider, V. Sukhotskiy, T. Siskar, L. Christie and I.H. Karampelas, Additive Manufacturing of Microfluidic Components via Wax Extrusion, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 162 – 165, 2018.

09-18   The Furlani Research Group, Magnetohydrodynamic Liquid Metal 3D Printing, Department of Chemical and Biological Engineering, © University at Buffalo, May 2018.

08-18   Benjamin Himmel, Dominik Rumschöttel and Wolfram Volk, Thermal process simulation of droplet based metal printing with aluminium, Production Engineering, March 2018 © German Academic Society for Production Engineering (WGP) 2018.

07-18   Yu-Che Wu, Cheng-Hung San, Chih-Hsiang Chang, Huey-Jiuan Lin, Raed Marwan, Shuhei Baba and Weng-Sing Hwang, Numerical modeling of melt-pool behavior in selective laser melting with random powder distribution and experimental validation, Journal of Materials Processing Tech. 254 (2018) 72–78.

60-17   Pei Wei, Zhengying Wei, Zhen Chen, Yuyang He and Jun Du, Thermal behavior in single track during selective laser melting of AlSi10Mg powder, Applied Physics A: Materials Science & Processing, 123:604, 2017. doi.org/10.1007/z00339-017-1194-9

51-17   Koichi Ishizaka, Keijiro Saitoh, Eisaku Ito, Masanori Yuri, and Junichiro Masada, Key Technologies for 1700°C Class Ultra High Temperature Gas Turbine, Mitsubishi Heavy Industries Technical Review, vol. 54, no. 3, 2017.

49-17   Yu-Che Wu, Weng-Sing Hwang, Cheng-Hung San, Chih-Hsiang Chang and Huey-Jiuan Lin, Parametric study of surface morphology for selective laser melting on Ti6Al4V powder bed with numerical and experimental methods, International Journal of Material Forming, © Springer-Verlag France SAS, part of Springer Nature 2017. doi.org/10.1007/s12289-017-1391-2.

37-17   V. Sukhotskiy, I. H. Karampelas, G. Garg, A. Verma, M. Tong, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic Drop-on-Demand Liquid Metal 3D Printing, Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference

15-17   I.H. Karampelas, S. Vader, Z. Vader, V. Sukhotskiy, A. Verma, G. Garg, M. Tong and E.P. Furlani, Drop-on-Demand 3D Metal Printing, Informatics, Electronics and Microsystems TechConnect Briefs 2017, Vol. 4

14-17   Jason Cheon and Suck-Joo Na, Prediction of welding residual stress with real-time phase transformation by CFD thermal analysis, International Journal of Mechanical Sciences 131–132 (2017) 37–51.

91-16   Y. S. Lee and D. F. Farson, Surface tension-powered build dimension control in laser additive manufacturing process, Int J Adv Manuf Technol (2016) 85:1035–1044, doi.org/10.1007/s00170-015-7974-5.

84-16   Runqi Lin, Hui-ping Wang, Fenggui Lu, Joshua Solomon, Blair E. Carlson, Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys, International Journal of Heat and Mass Transfer 108 (2017) 244–256, Available online December 2016.

68-16   Dongsheng Wu, Xueming Hua, Dingjian Ye and Fang Li, Understanding of humping formation and suppression mechanisms using the numerical simulation, International Journal of Heat and Mass Transfer, Volume 104, January 2017, Pages 634–643, Published online 2016.

39-16   Chien-Hsun Wang, Ho-Lin Tsai, Yu-Che Wu and Weng-Sing Hwang, Investigation of molten metal droplet deposition and solidification for 3D printing techniques, IOP Publishing, J. Micromech. Microeng. 26 (2016) 095012 (14pp), doi: 10.1088/0960-1317/26/9/095012, July 8, 2016

29-16   Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Advances in Magnetohydrodynamic Liquid Metal Jet Printing, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

26-16   Y.S. Lee and W. Zhang, Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base superalloy fabricated by laser powder bed fusion, S2214-8604(16)30087-2, doi.org/10.1016/j.addma.2016.05.003, ADDMA 86.

123-15   Koji Tsukimoto, Masashi Kitamura, Shuji Tanigawa, Sachio Shimohata, and Masahiko Mega, Laser welding repair for single crystal blades, Proceedings of International Gas Turbine Congress, pp. 1354-1358, 2015.

116-15   Yousub Lee, Simulation of Laser Additive Manufacturing and its Applications, Ph.D. Thesis: Graduate Program in Welding Engineering, The Ohio State University, 2015, Copyright by Yousub Lee 2015

103-15   Ligang Wu, Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, CFD Simulations of GMA Welding of Horizontal Fillet Joints based on Coordinate Rotation of Arc Models, Journal of Materials Processing Technology, Available online December 29, 2015

96-15   Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, Thermal metallurgical analysis of GMA welded AH36 steel using CFD – FEM framework, Materials & Design, Volume 91, February 5 2016, Pages 230-241, published online November 2015

86-15   Yousub Lee and Dave F. Farson, Simulation of transport phenomena and melt pool shape for multiple layer additive manufacturing, J. Laser Appl. 28, 012006 (2016). doi: 10.2351/1.4935711, published online 2015.

63-15   Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Magnetohydrodynamic Liquid Metal Jet Printing, TechConnect World Innovation Conference & Expo, Washington, D.C., June 14-17, 2015

46-15   Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

25-15   Dae-Won Cho and Suck-Joo Na, Molten pool behaviors for second pass V-groove GMAW, International Journal of Heat and Mass Transfer 88 (2015) 945–956.

21-15   Jungho Cho, Dave F. Farson, Kendall J. Hollis and John O. Milewski, Numerical analysis of weld pool oscillation in laser welding, Journal of Mechanical Science and Technology 29 (4) (2015) 1715~1722, www.springerlink.com/content/1738-494x, doi.org/10.1007/s12206-015-0344-2.

82-14  Yousub Lee, Mark Nordin, Sudarsanam Suresh Babu, and Dave F. Farson, Effect of Fluid Convection on Dendrite Arm Spacing in Laser Deposition, Metallurgical and Materials Transactions B, August 2014, Volume 45, Issue 4, pp 1520-1529

59-14   Y.S. Lee, M. Nordin, S.S. Babu, and D.F. Farson, Influence of Fluid Convection on Weld Pool Formation in Laser Cladding, Welding Research/ August 2014, VOL. 93

18-14  L.J. Zhang, J.X. Zhang, A. Gumenyuk, M. Rethmeier, and S.J. Na, Numerical simulation of full penetration laser welding of thick steel plate with high power high brightness laser, Journal of Materials Processing Technology (2014), doi.org/10.1016/j.jmatprotec.2014.03.016.

36-13  Dae-Won Cho,Woo-Hyun Song, Min-Hyun Cho, and Suck-Joo Na, Analysis of Submerged Arc Welding Process by Three-Dimensional Computational Fluid Dynamics Simulations, Journal of Materials Processing Technology, 2013. doi.org/10.1016/j.jmatprotec.2013.06.017

12-13 D.W. Cho, S.J. Na, M.H. Cho, J.S. Lee, A study on V-groove GMAW for various welding positions, Journal of Materials Processing Technology, April 2013, doi.org/10.1016/j.jmatprotec.2013.02.015.

01-13  Dae-Won Cho & Suck-Joo Na & Min-Hyun Cho & Jong-Sub Lee, Simulations of weld pool dynamics in V-groove GTA and GMA welding, Weld World, doi.org/10.1007/s40194-012-0017-z, © International Institute of Welding 2013.

63-12  D.W. Cho, S.H. Lee, S.J. Na, Characterization of welding arc and weld pool formation in vacuum gas hollow tungsten arc welding, Journal of Materials Processing Technology, doi.org/10.1016/j.jmatprotec.2012.09.024, September 2012.

77-10  Lim, Y. C.; Yu, X.; Cho, J. H.; et al., Effect of magnetic stirring on grain structure refinement Part 1-Autogenous nickel alloy welds, Science and Technology of Welding and Joining, Volume: 15 Issue: 7, Pages: 583-589, doi.org/10.1179/136217110X12720264008277, October 2010

18-10 K Saida, H Ohnishi, K Nishimoto, Fluxless laser brazing of aluminium alloy to galvanized steel using a tandem beam–dissimilar laser brazing of aluminium alloy and steels, Welding International, 2010

58-09  Cho, Jung-Ho; Farson, Dave F.; Milewski, John O.; et al., Weld pool flows during initial stages of keyhole formation in laser welding, Journal of Physics D-Applied Physics, Volume: 42 Issue: 17 Article Number: 175502 ; doi.org/10.1088/0022-3727/42/17/175502, September 2009

57-09  Lim, Y. C.; Farson, D. F.; Cho, M. H.; et al., Stationary GMAW-P weld metal deposit spreading, Science and Technology of Welding and Joining, Volume: 14 Issue: 7 ;Pages: 626-635, doi.org/10.1179/136217109X441173, October 2009

1-09 J.-H. Cho and S.-J. Na, Three-Dimensional Analysis of Molten Pool in GMA-Laser Hybrid Welding, Welding Journal, February 2009, Vol. 88

52-07   Huey-Jiuan Lin and Wei-Kuo Chang, Design of a sheet forming apparatus for overflow fusion process by numerical simulation, Journal of Non-Crystalline Solids 353 (2007) 2817–2825.

50-07  Cho, Min Hyun; Farson, Dave F., Understanding bead hump formation in gas metal arc welding using a numerical simulation, Metallurgical and Mateials Transactions B-Process Metallurgy and Materials Processing Science, Volume: 38, Issue: 2, Pages: 305-319, doi.org/10.1007/s11663-007-9034-5, April 2007

49-07  Cho, M. H.; Farson, D. F., Simulation study of a hybrid process for the prevention of weld bead hump formation, Welding Journal Volume: 86, Issue: 9, Pages: 253S-262S, September 2007

48-07  Cho, M. H.; Farson, D. F.; Lim, Y. C.; et al., Hybrid laser/arc welding process for controlling bead profile, Science and Technology of Welding and Joining, Volume: 12 Issue: 8, Pages: 677-688, doi.org/10.1179/174329307X236878, November 2007

47-07   Min Hyun Cho, Dave F. Farson, Understanding Bead Hump Formation in Gas Metal Arc Welding Using a Numerical Simulation, Metallurgical and Materials Transactions B, Volume 38, Issue 2, pp 305-319, April 2007

36-06  Cho, M. H.; Lim, Y. C.; Farson, D. F., Simulation of weld pool dynamics in the stationary pulsed gas metal arc welding process and final weld shape, Welding Journal, Volume: 85 Issue: 12, Pages: 271S-283S, December 2006

CFD에 대해서

What You Should Know About CFD Modeling when Selecting a CFD Package

유체 흐름 및 열 전달 해석용 소프트웨어 패키지에는 여러 형태가 있습니다. 물리적 근사와 수치 해법의 기법이 패키지마다 크게 다르기 때문에 적절한 패키지를 선택하는 것은 매우 어렵습니다. 다음 설명에서는 열유동 시뮬레이션 소프트웨어를 선택할 때 고려해야 할 중요한 몇 가지를 소개합니다.

Software packages for fluid flow and heat transfer analysis come in many forms. These packages differ greatly in their physical approximations and numerical solution techniques, which makes the selection of a suitable package a challenging proposition. The following discussion covers some important items to consider when choosing flow simulation software.

Meshing and Geometry

유한 요소 또는 “body-fitted coordinates”를 채용하고 있는 수치해석 방법은 유체 영역의 기하학적 형상에 적합한 격자를 생성해야 합니다. 정확한 수치 근사치를 얻기 위해 허용 할 수 있는 요소 크기 및 형상에서 이러한 격자를 생성하는 것은 매우 중요한 작업입니다.

복잡한 경우에는 이와 같은 방법으로 격자를 생성하면 며칠 또는 몇 주가 걸릴 수 있습니다.  어떤 프로그램은 사각형의 격자 요소만을 사용함으로써 문제를 해결하려고 하지만, 그럴 경우에는 경계부분에 계단이 생기고 흐름과 열전달 특성이 달라지는 문제에 직면하게 됩니다.

FLOW-3D는 FAVOR™(면적율 / 부피 비율)법 을 사용하여 지오메트리의 특성을 원활하게 포함하므로써, 간단한 사각형 격자만으로도 두 문제를 해결할 수 있습니다.  또한, 간단하고 강력한 솔리드 모델러가 FLOW-3D 패키지에 기본 포함되어 있으며, CAD 프로그램에서 생성한 기하형상 데이터를 가져올 수 있습니다.

Solution methods that employ finite-element or “body-fitted coordinates” require the generation of a solution grid that conforms to the geometry of the flow region. It is a non-trivial task to generate these grids with acceptable element sizes and shapes for accurate numerical approximations. In complicated cases this type of grid generation may consume days or even weeks of effort. Some programs attemptto eliminate this generation problem by using only rectangular grid elements, but then they must contend with “stair-step” boundaries that alter flow and heat-transfer properties. FLOW-3D solves both problems by using easy-to-generate rectangular grids in which geometric features are smoothly embedded using the FAVOR™ (fractional area/volume) method. A simple and powerful solids modeler is packaged with FLOW-3D or users may import geometric data from a CAD program.

Momentum Equation vs. Approximate Flow Models

유체 운동량의 정확한 처리가 중요한 몇 가지 이유가 있습니다.  첫째, 이것은 복잡한 기하학적 형상에서 유체가 어떻게 흐르는지를 예측하는 유일한 방법입니다.  둘째, 액체에 의하여 걸린 동적인 힘(압력)은 운동량에서만 계산할 수 있습니다.  마지막으로, 열 에너지의 대류 수송을 계산하려면 다른 유체 입자 및 경계에 대한 개별 유체 입자의 상대적인 움직임을 정확하게 파악하는 것이 필요합니다. 이것은 운동량의 정확한 처리를 의미합니다.  운동량 보존을 대충 근사하기만 한 CFD 모델은 FLOW-3D에서는 사용되지 않습니다.  이러한 모델은 현실적인 유체 구성 및 온도 분포 예측에 사용할 수 없기 때문입니다.

An accurate treatment of fluid momentum is important for several reasons. First, it is the only way to predict how fluid will flow through complicated geometry. Second, the dynamic forces (i.e., pressures) exerted by the fluid can only be computed from momentum considerations. Finally, to compute the convective transport of thermal energy, it is necessary to have an accurate picture of how individual fluid particles move in relation to other fluid particles and confining boundaries. This implies an accurate treatment of momentum. Simplified flow models that only crudely approximate the conservation of momentum are not used in FLOW-3D because they cannot be used to predict realistic fluid configurations and temperature distributions.

Liquid-Solid Heat Transfer Area

액체와 고체 사이 (금속 주형 등)의 열전달은 경계면 면적의 정확한 추정이 필요합니다.  경계가 계단 모양으로 되어 있는 경우, 보통 이 면적이 크게 추정됩니다.  예를 들어, 실린더의 표면적은 약 27 %정도 크게 추정됩니다.  FLOW-3D의 경우 정확한 경계면 면적은 FAVOR™법에 따라 FLOW-3D 전처리기에서 컨트롤 볼륨마다 자동으로 계산됩니다.

Heat transfer between a liquid and a solid (e.g., metal-to-mold) requires an accurate estimate of the interfacial area. Stair-step boundaries over-estimate this area; for example, the surface area of a cylinder would be over-estimated by a factor of 27%. Accurate interfacial areas are automatically computed by the FAVOR™ method for each control volume in the FLOW-3D pre-processor.

Control Volume Effects on Liquid-Solid Heat Transfer

컨트롤 볼륨의 크기가 액체와 고체 사이에서 교환되는 열 비율과 양에 영향을 줄 수 있습니다.  이것은 열이 액체와 고체의 경계면을 포함하는 컨트롤 볼륨을 흐를 필요가 있기 때문입니다.  FLOW-3D는 액체와 고체의 경계면에 걸쳐 열 전달률을 계산할 때 컨트롤 볼륨의 크기와 전도율이 고려됩니다.

The size of control volumes can influence the rate and amount of heat exchanged between a liquid and solid because heat must also flow in the control volumes containing the liquid/solid interface. In FLOW-3D control volume sizes and their conductivities are accounted for when computing heat transfer rates across liquid-solid interfaces.

Implicitness and Accuracy

비선형 방정식과 결합 방정식의 Implicit 방법은 반복 될 때마다 under-relaxation 특성을 갖는 반복적 해법이 필요합니다.  이 동작은 상황에 따라 심각한 오류 (또는 수렴 속도의 급격한 하락)가 발생할 수 있습니다.  예를 들어, 비율이 큰 컨트롤 볼륨을 사용하는 경우나, 실제로는 중요하지 않은 효과를 예상하고 암시적인 해법을 사용하는 경우 등입니다.  FLOW-3D는 가능한 명시적인 수치해법이 사용되고 있습니다.  이것은 필요한 계산량이 적고, 수치 안정성의 요구 사항이 요구된 정밀도에 상응하기 때문입니다.  자세한 내용은 “암시적인 수치해법과 명시적인 수치해법“을 참조하십시오.

Implicit methods for nonlinear and coupled equations require iterative solution methods that have the character of an under-relaxation in each iteration. This behavior can cause significant errors (or very slow convergence) in some situations, for example, when using control volumes with large aspect ratios or when the implicitness is used in anticipation of an effect that is not actually significant. In FLOW-3D explicit numerical methods are used whenever possible because they require less computational effort, and their numerical stability requirements are equivalent to accuracy requirements. Read more in the Implicit vs. Explicit Numerical Methods article.

Implicit Numerical Methods For Convective Transport

모든 크기의 타임 스텝 크기를 계산에 사용할 수 있는 암시적인 수치 기법은 CPU 시간을 줄이기 위해 많이 사용되는 방법입니다.  불행하게도, 이 방법은 대류 현상 해석에 대해 정확하지 않습니다.  암시적인 해법은 근사 방정식에 확산 효과를 도입함으로써 시간 단계의 독립성을 획득합니다.  수치 확산을 물리적 확산 (열전도 등)에 추가해도 확산율이 변경될 뿐이므로 심각한 문제가 되지 않을 수 있습니다.  그러나 수치 확산(발산)을 대류 과정에 추가하면 모델링 대상의 물리 현상의 특성은 완전히 다르게 됩니다.  FLOW-3D는 시간의 정확한 근사치를 보장하기 위해 프로그램에 의해 time step이 자동으로 제어됩니다.

Implicit numerical techniques that allow arbitrarily large time-step sizes to be used in calculations are a popular way to reduce CPU time requirements. Unfortunately, these methods are not accurate for convective processes. Implicit methods gain their time-step independence by introducing diffusive effects into the approximating equations. The addition of numerical diffusion to physical diffusion, e.g., to heat conduction, may not cause a serious problem as it only modifies the diffusion rate. However, adding numerical diffusion to convective processes completely changes the character of the physical phenomena being modeled. In FLOW-3D time steps are automatically controlled by the program to ensure time-accurate approximations.

Relaxation and Convergence Parameters

암시적으로 근사치를 사용하는 수치법은 하나 이상의 수렴 및 완화(이완)의 매개 변수를 선택해야 합니다.  이러한 매개 변수를 신중하게 선택하지 않으면 발산하거나 수렴에 시간이 걸리는 경우가 있습니다.  FLOW-3D를 융합하는 매개 변수와 완화(이완) 매개 변수를 하나씩만 사용하여 두 매개 변수는 프로그램에 의해 동적으로 선택됩니다.  수치 해법을 제어하는 매개 변수를 사용자가 설정할 필요는 없습니다.

Numerical methods that use implicit approximations also require the selection of one or more convergence and relaxation parameters. Making poor choices for these parameters can lead to either divergences or slow convergence rates. Only one convergence and one relaxation parameter are used in FLOW-3D, and both parameters are dynamically selected by the program. Users are not required to set any parameters controlling the numerical solver.

Free-Surface Tracking

액체와 기체의 경계면 (자유 표면 등)의 모델링에 사용되는 방법은 두 가지가 있습니다.  하나는 액체, 기체 두 영역의 흐름을 계산하고 경계면을 유체 밀도의 급격한 변화로 처리하는 방법입니다.

일반적으로 밀도의 불연속은 고차 수치 근사를 사용하여 모델링됩니다.  불행하게도 이 프로세스는 소수의 격자 셀에서 경계면이 평탄화되고, 이러한 경계면에 보통 존재하는 유체흐름의 접선 속도의 급격한 변화는 고려되지 않습니다.

기체가 계산 영역에 들어가는 액체로 대체되는 경우에는 이 방법에는 기체의 출구 포트 또는 출구 싱크도 보충 할 필요가 있습니다.  또한 이러한 방법은 일반적으로 유체의 비압축성를 충족하기 위해 더 많은 노력이 필요합니다.  이것이 발생하는 기체 영역에 거의 균일 한 압력 조정이 필요하며, 이를 통해 계산 수렴 시간이 소요되기 때문입니다.

FLOW-3D는 VOF (Volume-of-Fluid) 법 이라는 독창적인 방법이 사용되고 있습니다.  이것은 진정한 3 차원 경계면 추적 방식으로, 경계면을  3 차원 인터페이스로 추적하는 체계입니다.  또한 옵션의 표면 장력을 포함한 일반적인 접선 응력 경계 조건은 경계면에 적용됩니다.  기체 영역은 모델에 포함하도록 사용자가 요청하지 않는 한 계산되지 않습니다.

There are two methods used to model liquid-gas interfaces (i.e., free surfaces). One of these is to compute flow in both the liquid and gas regions and to treat the interface as a sharp change in fluid density. Typically, the density discontinuity is modeled using higher-order numerical approximations. Unfortunately, this treatment allows the interface to smooth out over a few grid cells and does not account for a corresponding sharp change in tangential flow velocity that generally exists at such interfaces. This technique must also be supplemented with escape ports or sinks for the gas if it is to be replaced by liquid entering a computational region. Further, such methods must typically work harder to satisfy the incompressibility of the fluids. This happens because gas regions must have nearly uniform pressure adjustments which tend to slow down the solution convergence rate. A different technique, the Volume-of-Fluid (VOF) method, is used in FLOW-3D. This is a true three-dimensional interface tracking scheme in which the interface is closely maintained as a step discontinuity. Moreover, normal and tangential stress boundary conditions, including optional surface tension forces, are applied at the interface. Gas regions are not computed unless the user requests these regions to be included in the model.

본 자료는 국내 사용자들의 편의를 위해 원문 번역을 해서 제공하기 때문에 일부 오역이 있을 수 있어서 원문과 함께 수록합니다. 자료를 이용하실 때 참고하시기 바랍니다.

컨설팅 절차

컨설팅 절차

  • 해석 컨설팅을 저희에게 의뢰하시면, 상세한 상담 후 견적을 작성하여 보내 드립니다. 상담은 전화, 이메일, 방문 등의 방법으로 진행됩니다.
  • 계약이 체결된 후 수치해석을 위한 자료 및 데이터를 받아, 협의된 안으로 수치해석을 수행합니다.
  • 컨설팅 진행 과정 중에 수시로 해석 결과 및 진행 상황에 대해 연락 드리며, 변경, 수정 사항을 협의하여 반영할 수 있습니다.
  • 수치해석이 완료되면 최종 보고서를 작성하여 제출하며, 필요시 방문하여 결과를 상세히 설명 드립니다.
  • 수치해석 기술 전수가 포함된 계약일 경우, 최종 보고서 제출 이후에 기술 전수 교육을 진행합니다.
  • 모든 기술 자료는 대외비로 취급되며, 철저하게 보안을 유지해드립니다.

컨설팅 분야

수자원 분야

  • 댐체, 수문, 제반 구조물 안정성 검토
  • 댐, 여수로 유동 해석
  • 여수로 수위별 방류량 해석
  • 여수로 월류 및 수위 검토 해석
  • 발전소 취수로 유동 해석
  • 배수터널 방류향 해석
  • 취수탑 유입 유량 해석
  • 교각주위 세굴 해석
  • 수문 수차 유량 해석
  • 저수지 수위별 유동해석
  • 배수암거 부정류 해석
  • 저수지 연결 터널 유동 해석
  • 교각 유동 작용 힘 검토
  • 도수터널 통수 능력 해석
  • 부유사 확산 검토
  • 냉각수 취수로 유량 해석
  • 수문 유동 양상 분석
  • 배수터널 방류량 해석
  • 월류 수위별 유량 유속 해석

수처리 분야

  • 정수지 유동해석
  • 분배수로 유량분배 해석
  • 침전지 유동 및 유속 분포 해석
  • 반응조 농도 및 반응시간 해석
  • 응집지 유동해석
  • 하수처리시설 슬러지 농도 해석
  • DAF 응집제 농도 해석
  • 수조 최적 교반 해석
  • 여과지 유동해석
  • 혼화지 유동해석
  • 호기조 담체 거동해석
  • 수처리 구조물 유동 양상 분석
  • 하수처리시설 유동해석
  • 분말활성탄 접촉조 해석
  • PSBR 반응조 해석
  • 지하수 ICE RING 형성 해석
  • 절리면 모세관 열유동 해석
  • DAF 실증시설 부상조 해석
  • 착수정 유량 분배 해석

우주 항공분야

  • 발사체 탱크 슬로싱 댐핑 평가 해석
  • 항공기 비행 및 급유 시 연료 탱크 내부 유동 해석
  • 항공기 날개 연료 탱크 내부 유동 해석
  • 항공기 연료 탱크 내부 유동 해석
  • 추진체 관리 장치 내부 유동 해석
  • 엔진 및 터빈 노즐 내부 유동 및 캐비테이션 해석

자동차 분야

FLOW-3D POST Gears
  • 자동차 연료 탱크에 연료 주입 시 탱크 내부 유동 해석
  • 피스톤 쿨링젯 시스템 해석
  • 전착 도장 해석
  • 자동차 연료 주입구의 주입 유량별 유동 특성 분석
  • 기어 펌프의 로터 회전에 따른 오일 유동 양상 분석
  • 엔진 실린더 내 피스톤 운동과 배기가스 유동 패턴 해석
  • 베어링 내 윤활을 위한 오일의 유동 양상 해석

해양분야

  • 해양 컨테이너 연료 탱크 슬로싱 해석
  • 방파제 구조물 주변 유동 해석
  • 선박 운항에 따른 항주파 및 유동 특성 분석
  • 사석 방파제 등 구조물 주변 유동 해석
  • 진동수주형 파력 발전 구조물 최적화 모델 해석
  • 선박 및 부유체 계류 시 계류 안정성 및 계류력 해석
  • 발전소 부근 해역 온배수 영향 예측
  • 지진 해일에 의한 영향 해석

주조 분야

  • 고압다이캐스팅  충진 거동 및 응고 해석
  • 저압주조 충진 거동 및 응고 해석
  • 경동주조 충진 거동 및 응고 해석
  • 중력주조 충진 거동 및 응고 해석
  • 원심주조 충진 거동 및 응고 해석
  • 금형온도 분포 해석
  • 제품 및 금형 열응력, 변형 해석
  • 주조 공법 별 온도 분포, 산화물 분포 및 결함 분석
  • 금형 및 몰드 냉각방안 최적화 검토

Micro/Bio/Nano Fluidics 분야

  • Slit 및 Slot 코팅 해석
  • Roll 코팅 해석
  • Gravure / Gravure-offset 프린팅 해석
  • Curtain 코팅 해석
  • Multi-layer Slide 코팅 해석
  • 전기 삼투를 이용한 마이크로 펌프 전위 및 유동해석
  • 마이크로 채널 액적 생성 연속성 및 혼합 해석
  • 잉크젯 헤드 조건에 따른 잉크 분사 성능 해석
  • 열모데관 유동해석과 모세관 충진 해석
  • 유전 영동 현상을 이용한 액적 융합 해석

레이저 용접 분야

  • 이종재 레이저 용접 해석
  • 용접속도와 경사도에 따른 키홀 내부의 기공 거동 해석
  • 이종재의 레이저 용접 시 wobbling 해석
  • 레이저 용접 Melt Pool 거동 해석
  • 레이저 파워, 속도에 따른 balling 결함 영향 해석

HVAC System Designs

HVAC(난방, 냉방 및 환기)시스템 엔지니어가 고려해야 하는 최적 설계 배치에 대한 검토를 수행

발전소의 경우 대형(길이 90m, 너비 33m, 높이 26m)건물로 변압기, 전력선, 조명 등 열 발생 장비를 갖추고 있어서 여러가지 시설물의 상황을 고려할 수 있음

건물 내 공기를 올바르게 분배하고 적절한 쾌적한 온도를 확보하기 위해 건물 구조와 흡입그 크기 등의 검토 가능

수치해석 기술 컨설팅 안내

FLOW-3D Case Studies

수치해석 기술 컨설팅 안내

(주)에스티아이씨앤디에서는 고객이 수치해석을 직접 수행하고 싶지만 경험이 없거나, 시간이 없어서 용역을 통해 수치해석 결과를 얻고자 하는 경우 전문 엔지니어를 통해 CFD 컨설팅 서비스를 제공합니다. 귀하께서 당면하고 있는 연구프로젝트를 최소의 비용으로, 최적의 해결방안을 찾을 수 있도록 지원합니다.
상담에는 비용은 전혀 들지 않습니다.

CFD는 엔지니어가 공기, 물 또는 모든 유체와의 상호 작용을 이해할 수 있게 하는 매우 효과적인 기술로 대부분의 유동현상에 해답을 제시 할 수있는 막대한 잠재력을 가지고 있습니다.
다양한 유체 흐름 현상이나 온도 및 열전달 분석 등 필요한 시나리오에 대한 맞춤 솔루션을 제공합니다.

당사에는 20년 이상 수치해석 연구에 전념하고 있는 전문 연구인력과 다양한 기술적 경험과 전문 시뮬레이션 기술을 제공하는 숙련된 기술컨설팅팀이 준비되어 있습니다.
귀하의 프로젝트 성공 가능성을 기술시연을 통해 제공 할 수 있습니다.
프로그램 소개나 자문이 필요하신 분들은 언제든지 아래 연락처로 문의하시기 바랍니다.

  • 전화 :   02-2026-0455
  • Email : flow3d@stikorea.co.kr

컨설팅 형태

수치해석 의뢰

  • 고객이 당면한 문제를 분석 /검토/협의 후, 가장 적절한 수치해석 방법을 수립합니다.
  • 주로 상호 협의된 설계안 및 해석 조건에 대해 수치해석을 수행하여 결과를 도출 분석, 검토합니다.
  • 설계 변경 인자 및 해석 횟수는 고객과 협의하여 진행합니다. 수치해석 결과를 분석 검토하여 설계에 반영하기 위한 의견을 제시하여 드립니다.

해석 대행 의뢰

  • 고객사에 해석 프로세스가 정립되어 있는 경우에 대해, 계산 장비와 수치해석 인력을 이용하여 해석 대행 및 해석 결과물을 제출합니다.

컨설팅 절차

  • 해석 컨설팅을 저희에게 의뢰하시면, 상세한 상담 후 견적을 작성하여 보내 드립니다. 상담은 전화, 이메일, 방문 등의 방법으로 진행됩니다.
  • 계약이 체결된 후 수치해석을 위한 자료 및 데이터를 받아, 협의된 안으로 수치해석을 수행합니다.
  • 컨설팅 진행 과정 중에 수시로 해석 결과 및 진행 상황에 대해 연락 드리며, 변경, 수정 사항을 협의하여 반영할 수 있습니다.
  • 수치해석이 완료되면 최종 보고서를 작성하여 제출하며, 필요시 방문하여 결과를 상세히 설명 드립니다.
  • 수치해석 기술 전수가 포함된 계약일 경우, 최종 보고서 제출 이후에 기술 전수 교육을 진행합니다.
  • 모든 기술 자료는 대외비로 취급되며, 철저하게 보안을 유지해드립니다.

주요 컨설팅 의뢰 분야

수자원 분야

  • 댐체, 수문, 제반 구조물 안정성 검토
  • 댐, 여수로 유동 해석
  • 여수로 수위별 방류량 해석
  • 여수로 월류 및 수위 검토 해석
  • 발전소 취수로 유동 해석
  • 배수터널 방류향 해석
  • 취수탑 유입 유량 해석
  • 교각주위 세굴 해석
  • 수문 수차 유량 해석
  • 저수지 수위별 유동해석
  • 배수암거 부정류 해석
  • 저수지 연결 터널 유동 해석
  • 교각 유동 작용 힘 검토
  • 도수터널 통수 능력 해석
  • 부유사 확산 검토
  • 냉각수 취수로 유량 해석
  • 수문 유동 양상 분석
  • 배수터널 방류량 해석
  • 월류 수위별 유량 유속 해석

수처리 분야

Wastewater Treatment Plant
Wastewater Treatment Plant
  • 정수지 유동해석
  • 분배수로 유량분배 해석
  • 침전지 유동 및 유속 분포 해석
  • 반응조 농도 및 반응시간 해석
  • 응집지 유동해석
  • 하수처리시설 슬러지 농도 해석
  • DAF 응집제 농도 해석
  • 수조 최적 교반 해석
  • 여과지 유동해석
  • 혼화지 유동해석
  • 호기조 담체 거동해석
  • 수처리 구조물 유동 양상 분석
  • 하수처리시설 유동해석
  • 분말활성탄 접촉조 해석
  • PSBR 반응조 해석
  • 지하수 ICE RING 형성 해석
  • 절리면 모세관 열유동 해석
  • DAF 실증시설 부상조 해석
  • 착수정 유량 분배 해석

우주 항공분야

  • 발사체 탱크 슬로싱 댐핑 평가 해석
  • 항공기 비행 및 급유 시 연료 탱크 내부 유동 해석
  • 항공기 날개 연료 탱크 내부 유동 해석
  • 항공기 연료 탱크 내부 유동 해석
  • 추진체 관리 장치 내부 유동 해석
  • 엔진 및 터빈 노즐 내부 유동 및 캐비테이션 해석

자동차 분야

FLOW-3D POST Gears
  • 자동차 연료 탱크에 연료 주입 시 탱크 내부 유동 해석
  • 피스톤 쿨링젯 시스템 해석
  • 전착 도장 해석
  • 자동차 연료 주입구의 주입 유량별 유동 특성 분석
  • 기어 펌프의 로터 회전에 따른 오일 유동 양상 분석
  • 엔진 실린더 내 피스톤 운동과 배기가스 유동 패턴 해석
  • 베어링 내 윤활을 위한 오일의 유동 양상 해석

해양분야

  • 해양 컨테이너 연료 탱크 슬로싱 해석
  • 방파제 구조물 주변 유동 해석
  • 선박 운항에 따른 항주파 및 유동 특성 분석
  • 사석 방파제 등 구조물 주변 유동 해석
  • 진동수주형 파력 발전 구조물 최적화 모델 해석
  • 선박 및 부유체 계류 시 계류 안정성 및 계류력 해석
  • 발전소 부근 해역 온배수 영향 예측
  • 지진 해일에 의한 영향 해석

주조 해석 분야

  • 고압다이캐스팅  충진 거동 및 응고 해석
  • 저압주조 충진 거동 및 응고 해석
  • 경동주조 충진 거동 및 응고 해석
  • 중력주조 충진 거동 및 응고 해석
  • 원심주조 충진 거동 및 응고 해석
  • 금형온도 분포 해석
  • 제품 및 금형 열응력, 변형 해석
  • 주조 공법 별 온도 분포, 산화물 분포 및 결함 분석
  • 금형 및 몰드 냉각방안 최적화 검토

Micro/Bio/Nano Fluidics 분야

  • Slit 및 Slot 코팅 해석
  • Roll 코팅 해석
  • Gravure / Gravure-offset 프린팅 해석
  • Curtain 코팅 해석
  • Multi-layer Slide 코팅 해석
  • 전기 삼투를 이용한 마이크로 펌프 전위 및 유동해석
  • 마이크로 채널 액적 생성 연속성 및 혼합 해석
  • 잉크젯 헤드 조건에 따른 잉크 분사 성능 해석
  • 열모데관 유동해석과 모세관 충진 해석
  • 유전 영동 현상을 이용한 액적 융합 해석

레이저 용접 분야

  • 이종재 레이저 용접 해석
  • 용접속도와 경사도에 따른 키홀 내부의 기공 거동 해석
  • 이종재의 레이저 용접 시 wobbling 해석
  • 레이저 용접 Melt Pool 거동 해석
  • 레이저 파워, 속도에 따른 balling 결함 영향 해석

공기/열 흐름 분야 (HVAC System Designs)

HVAC(난방, 냉방 및 환기)시스템 엔지니어가 고려해야 하는 최적 설계 배치에 대한 검토를 수행

발전소의 경우 대형(길이 90m, 너비 33m, 높이 26m)건물로 변압기, 전력선, 조명 등 열 발생 장비를 갖추고 있어서 여러가지 시설물의 상황을 고려할 수 있음

건물 내 공기를 올바르게 분배하고 적절한 쾌적한 온도를 확보하기 위해 건물 구조와 흡입그 크기 등의 검토 가능

조선/해양 분야

Coastal & Maritime

FLOW-3D 는 선박설계, 슬로싱 동역학, 파도에 미치는 영향 및 환기를 포함하여 해안 및 해양 관련 분야에 사용할 수 있는 이상적인 소프트웨어입니다.

자유 표면 유체 역학, 파동 생성, 움직이는 물체, 계선 및 용접 공정과 관련한 FLOW-3D 의 기능은 해양 및 해양 산업에서 CFD 공정을 모델링하는 데 매우 적합한 도구입니다. 해안 응용 분야의 경우  FLOW-3D  해안 응용 분야의 경우 FLOW-3D  는 해안 구조물에 대한 심한 폭풍 및 쓰나미 파동의 세부 사항을 정확하게 예측하고 돌발 홍수 및 중요 구조물 홍수 및 피해 분석에 사용됩니다. 기능은 다음과 같습니다.

  • 자유 표면 – 파동 유체 역학 및 오버 토핑 : 규칙 및 불규칙파 및 파동 스펙트럼 (Pierson Moskowitz, JONSWAP)
  • Seakeeping – slamming, planing, porpoising 및 선체 선체 변위 : 완전히 결합된 선박 및 수중 차량 유체 역학
  • 선체 – Resistance, stability and dynamics: surging, heaving, pitching and rolling motion (response amplitude operators or RAOs)
  • 슬로싱 – LNG / 밸러스트 탱크
  • 해양 공학 – 파동 에너지 변환기
 

해안 응용 분야의 경우, FLOW-3D 는 강력한 폭풍과 쓰나미 현상에 의한 해안 구조물이 받는 영향에 대한 세부 사항 예측, 돌발 홍수에 의한 중요한 시설물에 대한 정확한 피해 분석 등을 위해 사용됩니다.

Mooring Lines, Springs and Ropes

FLOW-3D (계류선 및 스프링 등)의 특수 물체를 다른 움직이는 물체에 부착하면 엔지니어가 선박 런칭, 부유 장애물 역학, 부표, 파도에너지 변환기 등을 정확하게 포착할 수 있습니다.

Welding

FLOW-3D 용접 모듈이 추가되면서 조선업계의 용접분야에서는 다공성 등 용접 결함을 최소화할 수 있어 선체의 품질을 크게 높이는 동시에 생산 시간을 최적화할 수 있습니다.

Coastal & Maritime Case Studies

FLOW-3D 사용자들은 연약한 해안선 보호, 구조물에 대한 파장 시뮬레이션, 선체 설계 최적화, 선박 내 환기 연구 등 해안 및 해양 애플리케이션에 FLOW-3D를 사용합니다.

우리는 보트가 세계 항해를 하면서 마주칠 것 같은 다양한 조건에서 항해를 할 수 있는지를 볼 수 있었습니다. 그리고 속도뿐만 아니라 연료 효율과 안전도 고려하도록 설계를 수정할 수 있었습니다.
– Pete Bethune, skipper of Earthrace

Lateral wave impact in waterWave resultsEarthrace vessel
Validation of Sloshing Simulations in Narrow Tanks / Aerial Landslide Generated Wave Simulations / Earthrace: Speed, Fuel Efficiency and Safety
Wave impact vertical displacementEmerged breakwater accropodeStokes theory horizontal velocity
Wave Impact on Offshore StructuresInteraction Between Waves and BreakwatersWave Forces on Coastal Bridges

기타

Bibliography

Models


관련 기술자료

Fig. 3. Breakwaters model in Flow-3D with meshing geometry and boundary (a) circular slots (b) square slots.

Study of Unconventional Alternatives to Vertical Breakwater

수직 방파제에 대한 비전통적 대안 연구 Karim Badr Hussein and Mohamed IbrahimLecturer of Irrigation and Hydraulics, Faculty of Engineering, Al-Azhar ...
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Figure 3. Flow velocity on seawall in A2-3 modeling.

Modeling of the Changes in Flow Velocity on Seawalls under Different Conditions Using FLOW-3D Software

Open Journal of Marine ScienceVol.06 No.02(2016), Article ID:65874,6 pages10.4236/ojms.2016.62026 FLOW-3D 소프트웨어를 사용하여 다양한 조건에서 Seawalls의 흐름 속도 변경 모델링 Maryam Deilami-Tarifi1, ...
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Interaction between oblique waves and arc-shaped breakwater

Interaction between oblique waves and arc-shaped breakwater: Wave action on the breakwater and wave transformation behind it

XinyuHanaShengDongaYizhiWangbaCollege of Engineering, Ocean University of China, Qingdao, 266100, ChinabShandong Harbour Engineering Group Co., Ltd., Rizhao, 276826, China Highlights Interaction ...
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Figure 6. Maximum inundation field in simulations with (a) no barrier on the seawall (red line), (b) a 1 m barrier across the entire sea wall, and (c) a 1.7 m barrier partially installed on the seawall.

Storm surge inundation simulations comparing three-dimensional with two-dimensional models based on Typhoon Maemi over Masan Bay of South Korea

Jae-Seol Shim†, Jinah Kim†, Dong-Chul Kim‡, Kiyoung Heo†, Kideok Do†, Sun-Jung Park ‡† Coastal Disaster Research Center,Korea Institute of Ocean ...
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Fig. 3. Mesh and depth map for the storm surge model of ADCSWAN model.

ADCSWAN과 FLOW-3D 모델을 이용한 태풍 차바 내습 시 부산 마린시티의 침수범람 재현

최흥배․엄호식†․박종집․강태욱*, *** ㈜지오시스템리서치 선임, ** ㈜지오시스템리서치 책임, **** 부경대학교 박사 Reproduction of Flood Inundation in Marine City, Busan During the ...
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A photo of HeMOSU-1.

FLOW-3D를 이용한 해상 자켓구조물 주변의 세굴 수치모의 실험

Numerical Simulation Test of Scour around Offshore Jacket Structure using FLOW-3D J Korean Soc Coast Ocean Eng. 2015;27(6):373-381Publication date (electronic) ...
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Figure 6. Scour depth (in negative value) at different views around pier

Three-dimensional numerical simulation of local scour around circular bridge pier using Flow-3D software

Flow-3D 소프트웨어를 이용한 원형 교각 주변 지역 scour의 3 차원 수치 시뮬레이션 To cite this article: Halah Kais Jalal and ...
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圖1. 1 南海孤立內波空間分布圖(Hsu et al., 2000)

Numerical Modeling on Internal Solitary Wave propagation over an obstacle using Flow-3D

Keyword: Internal solitary waves, Numerical, Flow-3D, Computational Fluid Dynamics 연구자 : Yu-Ren Chen지도교수 : Dr John R C HsuJune 2012 ...
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유압 헤드 계산에서는 유선이 평행하다고 가정

FLOW-3D Output variables(출력 변수)

Output variables(출력 변수) FLOW-3D에서 주어진 시뮬레이션의 정확한 출력은 어떤 물리적 모델, 출력 위젯에 정의된 추가 출력 및 특정 구성 요소별 ...
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Picture of scoured bed surface

EXPERIMENTAL STUDY AND NUMERICAL SIMULATION OF FLOW AND SEDIMENT TRANSPORT AROUND A SERIES OF SPUR DIKES

유동 시뮬레이션의 실험적 연구와 일련의 SPUR DIKES 주변의 침전물 수송 byANU ACHARYACopyright © Anu Acharya 2011A Dissertation Submitted to the ...
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[FLOW-3D 물리모델]General Moving Objects / 일반이동물체

General Moving Objects / 일반이동물체

Basics / 기초

The general moving objects (GMO) model in FLOW-3D can simulate rigid body motion, which is either userprescribed (prescribed motion) or dynamically coupled with fluid flow (coupled motion). If an object’s motion is prescribed, fluid flow is affected by the object’s motion, but the object’s motion is not affected by fluid flow. If an object has coupled motion, however, the object’s motion and fluid flow are coupled dynamically and affect each other. In both cases, a moving object can possess six degrees of freedom (DOF), or rotate about a fixed point or a fixed axis. The GMO model allows the location of the fixed point or axis to be arbitrary (it can be inside or outside the object and the computational domain), but the fixed axis must be parallel to one of the three coordinate axes of the space reference system. In one simulation, multiple moving objects with independent motion types can exist (the total number of moving and non-moving components cannot exceed 500). Any object under coupled motion can undergo simultaneous collisions with other moving and non-moving objects and wall and symmetry mesh boundaries (See Collision). The model also allows the existence of multiple (up to 100) elastic linear and torsion springs, elastic ropes and mooring lines which are attached to moving objects and apply forces or torques to them (See Elastic Springs & Ropes and Mooring Lines).

FLOW-3D에서 일반 이동물체인 GMO 모델은 강체운동을 모사(simulate)할 수 있는데, 이는 사용자가 기술하는 운동(지정운동)이거나 유체 유동과 동력학적인(결합된) 운동일 수 있다. 물체의 운동이 지정되면 유체 유동은 이 운동에 의해 영향을 받으나, 물체의 운동은 유체에 의해 영향을 받지 않는다. 그러나 물체가 결합된 운동을 하면 물체와 유체는 동역학적으로 연결되어 서로 영향을 미친다.

이 두 경우에 물체는6 자유도 운동을 할 수 있고, 고정된 점이나 축에 대해 회전할 수가 있다. GMO모델은 고정점이나 고정축의 위치를 임의로 설정할 수 있으나(이는 물체나 계산영역의 내부 또는 외부가 될 수 있다) 고정축은 공간좌표계의 좌표중의 하나에 평행하여야 한다.

어떤 모사(simulate)에서 고유의 운동형태를 갖는 다수의 운동물체가 존재할 수 있다(이동 및 고정된 물체의 전체수는500개를 초과하지 못한다). 결합운동을 하는 물체는 다른 이동/비이동 물체 그리고 벽과 대칭 경계 격자면에서 충돌할 수가 있다(충돌참조). 이 모델은 (100개까지) 다수의 탄성선형과 비틀림 스프링, 탄성로프와 이동 물체에 부착된 탄성력과 회전력을 갖는 계류선들을 표현할 수 있다(Elastic Springs & Ropes 와 Mooring Lines참조). .

In general, the motion of a rigid body can be described with six velocity components: three for translation and three for rotation. In the most general cases of coupled motion, all the available velocity components are coupled with fluid flow. However, the velocity components can also be partially prescribed and partially coupled in complex coupledmotion problems (e.g., a ship in a stream can have its pitch, roll and heave to be coupled but yaw, sway and surge prescribed). For coupled motion only, in addition to the hydraulic, gravitational, inertial and spring forces and torques which are calculated by the code, additional control forces can be prescribed by the user. The control forces can be defined either as up to five forces with their application points fixed on the object or as a net control force and torque. The net control force is applied to the GMO’s mass center, while the control torque is applied about the mass center for 6-DOF motion, and about the fixed point or fixed axis for those kinds of motions. The inertial force and torque exist only if the Non-inertial Reference Frame model is activated.

일반적으로 강체의 운동은 6개의 속도 성분으로 기술될 수 있다: 3개의 이동과3개의 회전. 가장 일반적인 결합 운동의 경우에, 모든 가능한 속도성분들은 유동과 연결되어 있다. 그러나 속도 성분들은 복잡한 결합운동 문제에서는 부분적으로 지정되고 일부는 결합될 수 있다(즉 유속내의 선박에서 pitch, roll and heave는 결합된 운동을 하고 yaw, sway and surge 는 지정될 수있다). 단 결합운동 문제에서는 코드 내에서 계산되는 수력, 중력, 관성 그리고 스프링 힘과 토크에 추가적인 조절할 수 있는 힘(control force) 들이 사용자에 의해 기술될 수 있다. 조절 힘(control force)들은 물체의 지정된 위치에 작용하는5개까지의 힘이나 또는 순수 힘과 토크로 정의 될 수 있다. 순수 조절힘은 GMO의 질량 중심에 작용하지만, 조절토크는6 자유도 운동의 질량중심에 대해 이런 운동을 하기 위한 고정축이나 점들에 대해 적용된다. 관성력과 토크는 단지 비 관성계 모델이 활성화되면 존재한다.

In FLOW-3D, a GMO is classified as a geometry component that is either porous or non-porous. As with stationary components, a GMO can be composed of a number of geometry subcomponents. Each subcomponent can be defined either by quadratic functions and primitives, or by STL data, and can be solid, hole or complement. If STL files are used, since GMO geometry is re-generated at every time step in the computation, the user should strive to minimize the number of triangle facets used to define the GMO to achieve faster execution of the solver while maintaining the necessary level of the geometry resolution. For mass properties, different subcomponents of an object can possess different mass densities.

FLOW-3D 에서 한 개의 GMO 는 다공질 또는 비 다공질의 형상요소로 간주된다. 정지된 구성요소에서와 같이 한 개의 GMO 는 다수의 형상 서브구성요소로 구성될 수 있다. 각 서브구성요소는 2차 함수와 기초 요소 또는 STL 데이터로 정의될 수 있고 고체, 공간 또는 이의 보완일 수 있다. 만약 STL 파일이 사용된다면 GMO 형상은 계산 중에 매 시간에서 재 생성되므로 사용자는 형상 정밀도에 필요한 수준을 유지하는 한편, 빠른 계산을 위해 GMO를 정의하는데 사용되는 삼각면의 수를 줄이려고 노력해야 한다. 질량물성을 위해 한 물체의 다른 서브구성요소는 다른 질량밀도를 가질 수 있다.

In order to define the motion of a GMO and interpret the computational results correctly, the user needs to understand the body-fixed reference system (body system) which is always fixed on the object and experiences the same motion. In the FLOW-3D preprocessor, the body system (x’, y’, z’) is automatically set up for each GMO. The initial directions of its coordinate axes (at t = 0) are the same as those of the space system (x, y, z). The origin of the body system is fixed at the GMO’s reference point which is a point automatically set on each moving object in accordance with the object’s motion type.

GMO 의 운동을 정의하고 계산결과를 정확히 이해하기 위해, 사용자는 항상 물체에 고정되고, 물체와 같은 운동을 하는 물체에, 고정된 기준계(물체계)를 이해할 필요가 있다. FLOW-3D 의 전처리에서 물체계(x’, y’, z’) 가 자동으로 각 GMO 에 대해 설정된다. 좌표축(t = 0에서) 의 초기방향은 공간계(x, y, z) 의 것과 같다. 물체계의 원점은 물체의 이동형상에 일치하는 각 이동체 상에 자동으로 설정된 GMO 의 기준점에 고정되어 있다.

 

The reference point is: 기준점은 다음과 같다.

  • the object’s mass center for the coupled 6-DOF motion;

결합된6자유도 운동의 질량중심

  • the fixed point for the fixed-point motion;

고정점 운동을 위한 고정점

  • a point on the fixed axis for the fixed-axis rotation;

고정축 회전을 위한 고정축 상의 점

  • a user-defined reference point for the prescribed 6-DOF motion.

기술된6자유도 운동을 위한 사용자 지정의 기준점

  • If the reference point is not given by users for the prescribed 6-DOF motion, it is set by the code at the mass center (if mass properties are given) or the geometry center (if mass properties are not given) of the object.

기준점이 기술된6자유도 운동을 위해 사용자가 지정하지 않으면 코드에 의해 질량중심 (질량물성이 주어지면) 또는 형상중심(질량물성이 안 주어지면)에 지정된다.

 

The GMO’s motion can be defined through the GUI using four steps:

GMO 운동은 4단계를 거쳐 GUI 를통하여 정의될수있다.

  1. Activate the GMO model;

GMO 모델을 활성화한다

  1. Create the GMO’s initial geometry;

GMO의 초기형상을 생성한다

  1. Specify the GMO’s motion-related parameters, and

GMO의 운동관련 변수들을 지정하고.

  1. Define the GMO’s mass properties.

GMO 질량물성을 정의한다

Without the activation of the GMO model in step 1, the object created as a GMO will be treated as a non-moving object, even if steps 2 to 4 are accomplished.

1단계의 GMO 모델 활성화가 없으면 2~4의 단계가 이루어져도 GMO 로 생성된 물체는 비 이동 물체로 간주될 것이다.

Step 1: Activate the GMO Model GMO 모델활성화

To activate the GMO model, go to Model Setup Physics Moving and simple deforming objects and check the Activate general moving objects (GMO) model box.

GMO 모델을 활성화하기 위해 Model Setup Physics Moving and simple deforming objects 로 가서 Activate general moving objects (GMO) model 박스를 체크한다.

The GMO model has two numerical methods to treat the interaction between fluid and moving objects: an explicit and an implicit method. If no coupled motion exists, the two methods are identical. For coupled motion, the explicit method, in general, works only for heavy GMO problem, i.e., all moving objects under coupled motion have larger mass densities than that of fluid and their added mass is relatively small. The implicit method, however, works for both heavy and light GMO problems. A light GMO problem means at least one of the moving objects under coupled motion has smaller mass densities than that of fluid or their added mass is large. The user may change the selection on the Moving and deforming objects panel or on the Numerics tab Moving object/fluid coupling.

GMO 모델은 유체와 움직이는 물체간의 상호작용을 다루기위해 두 수치해석법을 이용한다: explicit 방법과implicit 방법. 결합 운동이 없으면 두 방법은 동일하다. 결합된 운동에서는 외재적 방법은 일반적으로 무거운 GMO 문제에 사용된다, 즉 결합된 운동을 하는 모든 이동물체는 유체밀도보다 크고 이의 부가질량이 작을 경우이다. 그러나 내재적 방법은 무겁거나 가벼운 GMO 문제에 모두 사용된다. 가벼운 GMO 문제는 결합운동 시에 최소한 하나의 이동물체가 유체밀도보다 작고 이의 부가질량이 클 경우이다. 사용자는 Moving and deforming objects패널이나 Numerics tab Moving object/fluid coupling 상에서 선택을 바꿀 수 있다.

  1. Step 2: Create the GMO’s Initial Geometry GMO의 초기형상을 생성한다

 

In the Meshing & Geometry tab, create the desired geometry for the GMO components using either primitives and/or imported STL files in the same way as is done for any stationary component. The component can be either standard or porous. To set up a porous component, refer to Porous Media. Note that the Copy function cannot be used with geometry components representing GMOs.

정지상태의 구성요소 생성의 경우와 마찬가지로 Meshing & Geometry 탭에서 기초 요소와/또는 외부로부터의 STL 파일을 이용하여 GMO 구성요소의 원하는 형상을 생성한다. 구성요소는 standard이거나porous일 수 있다. 다공성요소를 설정하기 위해 Porous Media 를 참조하라. Copy 기능은 GMO를 나타내는 형상 구성요소에 사용할 수 없음에 주목한다.

Step 3: Specify the GMO’s Motion Related Parameters GMO의 운동관련변수들을 지정한다

The following section discusses how to set up parameters for prescribed and coupled 6-DOF motion, fixed-point motion and fixed-axis motion. The user can go directly to the appropriate part.

다음 섹션은 “지정되고 결합된 6자유도운동”, “고정점 운동과 고정축 운동을 위한 매개변수를 어떻게 설정하는지”에 대해 논한다. 사용자는 직접 해당부분을 참조할 수 있다.

Prescribed 6-DOF Motion 지정된 6자유도운동

In Meshing & Geometry Geometry Component (the desired GMO component) Type of Moving Object, select Prescribed motion. Go to Component Properties Type of Moving Object Moving Object Properties Edit Motion Constraints. Under Type of Constraint, select 6 Degrees of Freedom in the combo box.

Meshing & Geometry Geometry Component (the desired GMO component) Type of Moving Object 에서 Prescribed motion 을 선택한다. Component Properties Type of Moving Object Moving Object Properties Edit Motion Constraints 로 가서 Type of Constraint 밑에서 combo 박스에 있는 6 Degrees of Freedom 를 선택한다.

To define the object’s velocity, go to the Initial/Prescribed Velocities tab in the Moving object setup window. The prescribed 6-DOF motion is described as a superimposition of a translation of a reference point and a rotation about the reference point. The reference point can be anywhere inside or outside the moving object and the computational domain. The user needs to enter its initial x, y and z coordinates (at t = 0) in the provided edit boxes. By default, the reference point is determined by the preprocessor in two different ways depending on whether the object’s mass properties are given: if mass properties (either mass density or integrated mass properties) are given, then the mass center of the moving object is used as the reference point; otherwise, the object’s geometric center will be calculated and used as the reference point.

물체의 속도를 정의하기 위해 Moving object setup 의 창에 있는 Initial/Prescribed Velocities 탭으로 이동한다. 지정된 6자유도 운동은 기준점의 이동과 기준점에 대한 회전의 중첩으로 기술된다. 기준점은 이동체의 내부 또는 외부 그리고 계산영역 외부일 수도 있다. 사용자는 주어진 편집박스 내에 이의 초기 x, y 와 z 좌표값(t = 0에서)을 입력할 필요가 있다. 디폴트로 기준점은 물체의 질량 물성이 주어지는가에 따라 두 가지로 전처리 과정에서 결정된다: 질량물성(질량밀도나 전체질량물성)이 주어지면 이동체의 질량중심이 기준점으로 사용되고 아니면 이동체의 형상중심이 계산되고 기준점으로 이용된다.

With the reference point provided (or left for the code to calculate), users can define the translational velocity components for the reference point in space system and the angular velocity components (in radians/time) in body system. Each velocity component can be defined either as a sinusoidal or a piecewise linear function of time by making a selection in the corresponding combo box. For a constant velocity component, choose Non-Sinusoidal and simply enter its value in the corresponding input box (the default value is 0.0). If a velocity component is Non-sinusoidal and time-dependent, click on the corresponding Tabular button to open a data table and enter values for the velocity component and time. Alternatively, the user can also import a data file for the velocity component versus time by clicking Tabular Import Values. The file must have two columns of data which represent time and velocity from left to right and must have a csv extension. If the velocity component is sinusoidal in time, then enter the values for Amplitude, Frequency (in Hz) and Initial Phase (in degrees) in the input boxes.

기준점이 주어지면(또는 코드 내에서 계산이 되면) 사용자는 공간계 기준점에 대해 translational velocity components 를 그리고 물체계에서angular velocity components (radians/시간으로)를 정의할 수 있다. 각 속도 성분은 상응하는 combo box 에서 선택함으로써 사인파 또는 구간적 시간함수로써 정의될 수 있다. 일정 속도 성분에 대해서 Non-Sinusoidal 을 선택하고 단순히 상응하는 combo 박스에 값을 넣는다(디폴트 값은0이다). 속도성분이 Non-Sinusoidal 이고 시간의 함수이면 데이터 테이블을 열고 상응하는 Tabular 버튼을 클릭하고 속도성분과 시간을 넣는다. 다른 방법으로는 사용자가 Tabular Import Values를 클릭함으로써 속도성분대 시간의 데이터파일을 읽어 들일 수가 있다. 이 파일은 시간과 속도를 나타내는 좌로부터 우로의 두 데이터 열이 있어야 하며 csv 확장자를 가져야 한다. 속도 성분이 시간에 따른 사인파이면 입력박스에서의 Amplitude, Frequency (in Hz) 그리고 Initial Phase (in degrees) 값을 입력한다.

The expression for the sinusoidal velocity component is

사인파 속도의 식은

v = Asin(2πft + ϕ0)

where: 여기서

  • A is the amplitude, 진폭
  • f is the frequency, and주기이며
  • ϕ0 is the initial phase. 초기위상이다.
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  • Users can set limits for the translational displacements of the object’s reference point in both negative and positive x, y and z directions in space system. The displacements are measured from the initial location of the reference point. During motion, the reference point cannot go beyond these limits but can move back to the allowed range after it reaches a limit. To set the limits for translation, go to the Motion Constraints tab and enter the maximum displacements allowed in the corresponding input boxes, using absolute values. By default, these values are infinite. Note the Limits for rotation is only for fixed-axis rotation thus cannot be set for 6-DOF motion.사용자는 공간계에서 음이나 양의 x, y 그리고 z 방향으로 물체 기준점의 이동변위를 제한할 수 있다. 변위는 기준점의 초기위치로부터 정해진다. 운동중에 기준점은 이 제한을 넘어갈 수 없지만 이 제한에 도달한 후에 허용된 범위만큼 돌아올 수 있다. 이동의 제한을 설정하기 위해 Motion Constraints 탭으로가서 절대값을 사용하여 상응하는 입력박스 안에 허용된 최대변위를 넣는다. the Limits for rotation 는 고정축 회전에만 해당하므로 6자유도 운동에는 지정될 수 없다.Prescribed Fixed-point Motion지정된 고정점운동In Meshing & Geometry Geometry Component (the desired GMO component) Component Properties Type of Moving Object, select Prescribed motion. Go to Moving object properties Edit Motion Constraints. Under Type of Constraint, select Fixed point rotation in the combo box and enter the x, y and z coordinates of the fixed point in the corresponding input boxes.Meshing & Geometry Geometry Component (the desired GMO component) Component Properties Type of Moving Object 에서 Prescribed motion 을 선택한다. Moving object properties Edit Motion Constraints 로 가서 Type of Constraint 밑에서 combo box 에있는 Fixed point rotation을 선택하고 상응하는 입력박스에서 고정점의 the x, y 및 z 좌표를 입력한다.To define the velocity of the object, go to the Initial/Prescribed Velocities tab in the Moving object setup window. The velocity components to be defined are the x, y and z components of the angular velocity (in radians/time) in the body system. Each velocity component can be defined as either a sinusoidal or a piecewise linear function of time by making a selection in the corresponding combo box. For a constant velocity component, choose Non-Sinusoidal and simply enter its value in its input box (the default value is 0.0). If a velocity component is time-variant and Non-sinusoidal, click on the Tabular button to open a data table and enter the values for the velocity component and time. Alternatively, the user can also import a data file for the velocity component versus time by clicking Tabular Import Values. The file must have two columns of data which represent time and velocity component from left to right and must have a csv extension. If the velocity component is sinusoidal in time, then enter the values for Amplitude, Frequency (in cycles/time) and Initial Phase (in degrees) in the corresponding input boxes.

    물체의 속도를 정의하기 위해 Moving object setup 의 창에 있는 Initial/Prescribed Velocities 탭으로 간다. 정의되어야 할 속도성분은 물체계에서 각속도  (radians/시간으로) 를 x, y 및 z 성분으로 정의할 수 있다

    각 속도 성분은 상응하는 combo box 에서 사인파 또는 구간적 시간함수로써 정의될 수 있다.

    일정속도 성분에 대해서 Non-Sinusoidal 을 선택하고 단순히 상응하는 combo box 박스에 값을 넣는다(디폴트 값은0이다). 속도성분이 Non-Sinusoidal 이고 시간의 함수이면 데이터 테이블을 열고 상응하는 Tabular 버튼을 클릭하고 속도성분과 시간을 넣는다. 그렇지 않으면 사용자가 Tabular Import Values 를 클릭함으로써 속도성분대 시간의 데이터 파일