Fig. 2. Semi-Lagrangian cellwise advection. (a) Forward advection scheme, (b) Backward advection scheme.

Three-dimensional cellwise conservative unsplit geometric VOF schemes

3차원 셀별 보수 미분할 기하학적 VOF 체계

Raphaël Comminal, JonSpangenberg

Abstract

This work presents two unsplit geometric VOF schemes that extend the two-dimensional cellwise conservative unsplit (CCU) scheme [Comminal et al., J. Comput. Phys. 283 (2015) 582–608] to three dimensions. The novelty of the 3D-CCU schemes lies in the representation of the streaksurfaces of donating regions by polyhedral surfaces whose vertices are calculated with the 4th order Runge-Kutta scheme. Moreover, the advected liquid volumes are computed using a truncation algorithm [López et al., J. Comput. Phys. 392 (2019) 666–693] suited for arbitrary non-convex and self-intersecting polyhedra, which removes the need for tetrahedral decomposition. The 3D-CCU advection schemes were coupled to three interface reconstruction methods (Youngs’ method, the Mixed Youngs-Centered scheme, and the Least-Square Fit algorithm). The resulting VOF methods were tested in classical benchmark advection tests, including translation, rigid-body rotation, shear and deformation flows. The proposed 3D-CCU schemes conserve the liquid volume and maintain the physical boundedness of liquid volume fractions to the machine precision. The 3D-CCU schemes perform favorably compared to other unsplit geometric VOF schemes when coupled to Youngs’ interface reconstruction method. Moreover, the 3D-CCU schemes coupled to the Least-Square Fit algorithm are more accurate than most other VOF schemes that use a second-order accurate interface reconstruction, except those where a 3D extension of the Mosso-Swartz interface reconstruction is employed. The comparison of the different VOF schemes highlights the importance of coupling accurate interface reconstruction methods with accurate unsplit advection schemes.

이 연구는 2 차원 CCU (Cellwise Conservative Unsplit) 방식을 확장하는 두 가지 분할되지 않은 기하학적 VOF 방식을 제시합니다 [Comminal et al., J. Comput. Phys. 283 (2015) 582–608]을 3 차원으로 변경했습니다. 3D-CCU 체계의 참신함은 4 차 Runge-Kutta 체계로 정점이 계산되는 다면체 표면으로 기부 지역의 줄무늬 표면을 표현하는 데 있습니다.

더욱, 가변 액체 부피는 절단 알고리즘을 사용하여 계산됩니다 [López et al., J. Comput. Phys. 392 (2019) 666–693]은 임의의 볼록하지 않고 자기 교차하는 다면체에 적합하며, 이는 사면체 분해의 필요성을 제거합니다. 3D-CCU 이류 계획은 세 가지 인터페이스 재구성 방법 (Youngs의 방법, Mixed Youngs-Centered 계획 및 Least-Square Fit 알고리즘)과 결합되었습니다. 결과 VOF 방법은 평행 이동, 강체 회전, 전단 및 변형 흐름을 포함한 고전적인 벤치 마크 이류 테스트에서 테스트되었습니다.

제안된 3D-CCU 방식은 액체 부피를 보존하고 기계 정밀도에 대한 액체 부피 분율의 물리적 경계를 유지합니다. 3D-CCU 방식은 Youngs의 인터페이스 재구성 방식과 결합 할 때 다른 분할되지 않은 기하학적 VOF 방식에 비해 우수한 성능을 발휘합니다.

또한 Least-Square Fit 알고리즘과 결합 된 3D-CCU 체계는 Mosso-Swartz 인터페이스 재구성의 3D 확장이 사용되는 경우를 제외하고 2 차 정확한 인터페이스 재구성을 사용하는 대부분의 다른 VOF 체계보다 더 정확합니다. 서로 다른 VOF 체계의 비교는 정확한 인터페이스 재구성 방법과 정확한 분할되지 않은 이류 체계를 결합하는 것의 중요성을 강조합니다.

Keywords

Volume-of-fluid methodUnsplit geometric schemeCellwise advectionSemi-Lagrangian trackingVolume conservation

Fig. 1. Eulerian fluxwise advection. (a) Positive donating region with respect to the left cell; (b) Negative donating region; (c) Intersection of a donating region with the cell's face, yielding a positive and a negative region; (d) Temporally-consistent donating regions equivalent to a cellwise advection; (e) Temporal inconsistency of adjacent donating regions.
Fig. 1. Eulerian fluxwise advection. (a) Positive donating region with respect to the left cell; (b) Negative donating region; (c) Intersection of a donating region with the cell’s face, yielding a positive and a negative region; (d) Temporally-consistent donating regions equivalent to a cellwise advection; (e) Temporal inconsistency of adjacent donating regions.
Fig. 2. Semi-Lagrangian cellwise advection. (a) Forward advection scheme, (b) Backward advection scheme.
Fig. 2. Semi-Lagrangian cellwise advection. (a) Forward advection scheme, (b) Backward advection scheme.
Fig. 3. (a) Cartesian grid cell. (b) Images of the cell's vertices with ruled surfaces. (c) Polyhedral cell's image with triangulated faces.
Fig. 3. (a) Cartesian grid cell. (b) Images of the cell’s vertices with ruled surfaces. (c) Polyhedral cell’s image with triangulated faces.
Fig. 4. Construction of donating regions. (a) Streakline of a cell's vertex P0 represented by the 2-segment polygonal line P0–P1/2–P1. (b) Triangulated streaksurface of a cell's edge P0Q0. (c) Streaktube of a cell's face P0Q0R0S0. (d) Pyramidal volume flux correction  ⁎  capping the donating region of the face P0Q0R0S0.
Fig. 4. Construction of donating regions. (a) Streakline of a cell’s vertex P0 represented by the 2-segment polygonal line P0–P1/2–P1. (b) Triangulated streaksurface of a cell’s edge P0Q0. (c) Streaktube of a cell’s face P0Q0R0S0. (d) Pyramidal volume flux correction ⁎ capping the donating region of the face P0Q0R0S0.
Fig. 5. Interface reconstruction. (a) PLIC polygon in the grid cell, (b) Non-planar image of the PLIC polygon inside the cell's image by isomorphism, (c) Planar PLIC inside the cell's image by computation of the average normal vector. (Triangulation of the cell's image faces are omitted for clarity.)
Fig. 5. Interface reconstruction. (a) PLIC polygon in the grid cell, (b) Non-planar image of the PLIC polygon inside the cell’s image by isomorphism, (c) Planar PLIC inside the cell’s image by computation of the average normal vector. (Triangulation of the cell’s image faces are omitted for clarity.)
Fig. 6. Convergence of the geometric errors in the translation tests.
Fig. 6. Convergence of the geometric errors in the translation tests.
Fig. 7. Reconstructed PLIC polygons (in light blue) superimposed to the exact sphere position (in dark blue) at the end of the rotation tests for the LSF method and CFL = 1.
Fig. 7. Reconstructed PLIC polygons (in light blue) superimposed to the exact sphere position (in dark blue) at the end of the rotation tests for the LSF method and CFL = 1.
Fig. 8. Reconstructed PLIC polygons in the shear tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs' method.
Fig. 8. Reconstructed PLIC polygons in the shear tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs’ method.
Fig. 9. Reconstructed PLIC polygons in the deformation tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs' method.
Fig. 9. Reconstructed PLIC polygons in the deformation tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs’ method.

References
[1]
C.W. Hirt, B.D. Nichols, Volume of fluid (VOF) method for the dynamics of free boundaries, Journal of Computational Physics 39 (1981) 201–225. https://doi.org/10.1016/0021-9991(81)90145-5.
Google Scholar
[2]
F.H. Harlow, J.E. Welch, Numerical Calculation of Time-Dependent Viscous Incompressible Flow of Fluid with Free Surface, The Physics of Fluids 8 (1965) 2182–2189. https://doi.org/10.1063/1.1761178.
Google Scholar
[3]
S. McKee, M.F. Tomé, V.G. Ferreira, J.A. Cuminato, A. Castelo, F.S. Sousa, N. Mangiavacchi, The MAC method, Computers & Fluids 37 (2008) 907–930. https://doi.org/10.1016/j.compfluid.2007.10.006.
Google Scholar
[4]
G. Tryggvason, B. Bunner, A. Esmaeeli, D. Juric, N. Al-Rawahi, W. Tauber, J. Han, S. Nas, Y.-J. Jan, A front-tracking method for the computations of multiphase flow, Journal of Computational Physics 169 (2001) 708–759. https://doi.org/10.1006/jcph.2001.6726.
Google Scholar
[5]
S. Shin, D. Juric, Modeling three-dimensional multiphase flow using a level contour reconstruction method for front tracking without connectivity, Journal of Computational Physics 180 (2002) 427–470. https://doi.org/10.1006/jcph.2002.7086.
Google Scholar
[6]
M. Sussman, P. Smereka, S. Osher, A level set approach for computing solutions to incompressible two-phase flow, Journal of Computational Physics 114 (1994) 146–159. https://doi.org/10.1006/jcph.1994.1155.
Google Scholar
[7]
E. Olsson, G. Kreiss, A conservative level set method for two phase flow, Journal of Computational Physics 210 (2005) 225–246. https://doi.org/10.1016/j.jcp.2005.04.007.
Google Scholar
[8]
D. Jacqmin, Calculation of two-phase Navier–Stokes flows using phase-field modeling, Journal of Computational Physics 155 (1999) 96–127. https://doi.org/10.1006/jcph.1999.6332.
Google Scholar
[9]
M. Sussman, E.G. Puckett, A coupled level set and volume-of-fluid method for computing 3D and axisymmetric incompressible two-phase flows, Journal of Computational Physics 162 (2000) 301–337. https://doi.org/10.1006/jcph.2000.6537.
Google Scholar
[10]
M. Sussman, A second order coupled level set and volume-of-fluid method for computing growth and collapse of vapor bubbles, Journal of Computational Physics 187 (2003) 110–136. https://doi.org/10.1016/S0021-9991(03)00087-1.
Google Scholar
[11]
N. Balcázar, O. Lehmkuhl, L. Jofre, J. Rigola, A. Oliva, A coupled volume-of-fluid/level-set method for simulation of two-phase flows on unstructured meshes, Computers & Fluids 124 (2016) 12–29. https://doi.org/10.1016/j.compfluid.2015.10.005.
Google Scholar
[12]
Y. Liu, X. Yu, A coupled phase–field and volume-of-fluid method for accurate representation of limiting water wave deformation, Journal of Computational Physics 321 (2016) 459–475. https://doi.org/10.1016/j.jcp.2016.05.059.
Google Scholar
[13]
E. Aulisa, S. Manservisi, R. Scardovelli, A surface marker algorithm coupled to an area-preserving marker redistribution method for three-dimensional interface tracking, Journal of Computational Physics 197 (2004) 555–584. https://doi.org/10.1016/j.jcp.2003.12.009.
Google Scholar
[14]
D. Enright, R. Fedkiw, J. Ferziger, I. Mitchell, A hybrid particle level set method for improved interface capturing, Journal of Computational Physics 183 (2002) 83–116. https://doi.org/10.1006/jcph.2002.7166.
Google Scholar
[15]
T. Marić, H. Marschall, D. Bothe, lentFoam – A hybrid Level Set/Front Tracking method on unstructured meshes, Computers & Fluids 113 (2015) 20–31. https://doi.org/10.1016/j.compfluid.2014.12.019.
Google Scholar
[16]
S. Mirjalili, S.S. Jain, M. Dodd, Interface-capturing methods for two-phase flows: An overview and recent developments, In: Center for Turbulence Research Annual Research Briefs (2017) 117–135.
Google Scholar
[17]
D. Fuster, A. Bagué, T. Boeck, L. Le Moyne, A. Leboissetier, S. Popinet, P. Ray, R. Scardovelli, S. Zaleski, Simulation of primary atomization with an octree adaptive mesh refinement and VOF method, International Journal of Multiphase Flow 35 (2009) 550–565. https://doi.org/10.1016/j.ijmultiphaseflow.2009.02.014.
Google Scholar
[18]
X. Chen, D. Ma, V. Yang, S. Popinet, High-fidelity simulations of impinging jet atomization, Atomization and Sprays 23 (2013) 1079–1101. https://doi.org/10.1615/AtomizSpr.2013007619.
Google Scholar
[19]
J. Delteil, S. Vincent, A. Erriguible, P. Subra-Paternault, Numerical investigations in Rayleigh breakup of round liquid jets with VOF methods, Computers & Fluids 50 (2011) 10–23. https://doi.org/10.1016/j.compfluid.2011.05.010.
Google Scholar
[20]
Agbaglah, S. Delaux, D. Fuster, J. Hoepffner, C. Josserand, S. Popinet, P. Ray, R. Scardovelli, S. Zaleski, Parallel simulation of multiphase flows using octree adaptivity and the volume-of-fluid method, Comptes Rendus Mecanique 339 (2011) 194–207. https://doi.org/10.1016/j.crme.2010.12.006.
Google Scholar
[21]
H. Grosshans, A. Movaghar, L. Cao, M. Oevermann, R.Z. Szász, L. Fuchs, Sensitivity of VOF simulations of the liquid jet breakup to physical and numerical parameters, Computers & Fluids 136 (2016) 312–323. https://doi.org/10.1016/j.compfluid.2016.06.018.
Google Scholar
[22]
D. Lörstad, L. Fuchs, High-order surface tension VOF-model for 3D bubble flows with high density ratio, Journal of Computational Physics 200 (2004) 153–176. https://doi.org/10.1016/j.jcp.2004.04.001.
Google Scholar
[23]
D. Fuster, S. Popinet, An all-Mach method for the simulation of bubble dynamics problems in the presence of surface tension, Journal of Computational Physics 374 (2018) 752–768. https://doi.org/10.1016/j.jcp.2018.07.055.
Google Scholar
[24]
N. Nikolopoulos, K.S. Nikas, G. Bergeles, A numerical investigation of central binary collision of droplets, Computers & Fluids 38 (2009) 1191–1202. https://doi.org/10.1016/j.compfluid.2008.11.007.
Google Scholar
[25]
G. Strotos, I. Malgarinos, N. Nikolopoulos, M. Gavaises, Predicting droplet deformation and breakup for moderate Weber numbers, International Journal of Multiphase Flow 85 (2016) 96–109. https://doi.org/10.1016/j.ijmultiphaseflow.2016.06.001.
Google Scholar
[26]
D. Jiao, K. Jiao, F. Zhang, Q. Du, Direct numerical simulation of droplet deformation in turbulent flows with different velocity profiles, Fuel 247 (2019) 302–314. https://doi.org/10.1016/j.fuel.2019.03.010.
Google Scholar
[27]
F. Giussani, F. Piscaglia, G. Saez-Mischlich, J. Hèlie, A three-phase VOF solver for the simulation of in-nozzle cavitation effects on liquid atomization, Journal of Computational Physics 406 (2020) 109068. https://doi.org/10.1016/j.jcp.2019.109068.
Google Scholar
[28]
M.R. Pendar, E. Roohi, Investigation of cavitation around 3D hemispherical head-form body and conical cavitators using different turbulence and cavitation models, Ocean Engineering 112 (2016) 287–306. https://doi.org/10.1016/j.oceaneng.2015.12.010.
Google Scholar
[29]
Flow Science, Inc., Santa Fe, NM, USA. FLOW-3D® Version 12.0 (2019). https://www.flow3d.com.
Google Scholar
[30]
O. Ubbink, R.I. Issa, A method for capturing sharp fluid interfaces on arbitrary meshes, Journal of Computational Physics 153 (1999) 26–50. https://doi.org/10.1006/jcph.1999.6276.
Google Scholar
[31]
S. Muzaferija, A two-fluid Navier-Stokes solver to simulate water entry, In: Proceedings of 22nd Symposium on Naval Architecture (1999) 638–651.
Google Scholar
[32]
M. Darwish, F. Moukalled, Convective schemes for capturing interfaces of free-surface flows on unstructured grids, Numerical Heat Transfer, Part B: Fundamentals 49 (2006) 19–42. https://doi.org/10.1080/10407790500272137.
Google Scholar
[33]
S.S. Deshpande, L. Anumolu, M.F. Trujillo, Evaluating the performance of the two-phase flow solver interFoam, Computational Science & Discovery 5 (2012) 014016. https://doi.org/10.1088/1749-4699/5/1/014016.
Google Scholar
[34]
J.A. Heyns, A.G. Malan, T.M. Harms, O.F. Oxtoby, Development of a compressive surface capturing formulation for modelling free-surface flow by using the volume-of-fluid approach, International Journal for Numerical Methods in Fluids 71 (2013) 788–804. https://doi.org/10.1002/fld.3694.
Google Scholar
[35]
S. Ii, K. Sugiyama, S. Takeuchi, S. Takagi, Y. Matsumoto, F. Xiao, An interface capturing method with a continuous function: The THINC method with multi-dimensional reconstruction, Journal of Computational Physics 231 (2012) 2328–2358. https://doi.org/10.1016/j.jcp.2011.11.038.
Google Scholar
[36]
B. Xie, S. Ii, F. Xiao, An efficient and accurate algebraic interface capturing method for unstructured grids in 2 and 3 dimensions: The THINC method with quadratic surface representation, International Journal for Numerical Methods in Fluids 76 (2014) 1025–1042. https://doi.org/10.1016/j.jcp.2013.11.034.
Google Scholar
[37]
Q. Zhang, On Donating Regions: Lagrangian Flux through a Fixed Curve, SIAM Review 55 (2013) 443–461. https://doi.org/10.1137/100796406.
Google Scholar
[38]
E. Aulisa, S. Manservisi, R. Scardovelli, S. Zaleski, Interface reconstruction with least-squares fit and split advection in three-dimensional Cartesian geometry, Journal of Computational Physics 225 (2007) 2301–2319. https://doi.org/10.1016/j.jcp.2007.03.015.
Google Scholar
[39]
G.D. Weymouth, D.K.-P. Yue, Conservative Volume-of-Fluid method for free-surface simulations on Cartesian-grids, Journal of Computational Physics 229 (2010) 2853–2865. https://doi.org/10.1016/j.jcp.2009.12.018.
Google Scholar
[40]
C.S. Wu, D.L. Young, H.C. Wu, Simulations of multidimensional interfacial flows by an improved volume-of-fluid method, International Journal of Heat and Mass Transfer 60 (2013) 739–755. https://doi.org/10.1016/j.ijheatmasstransfer.2012.12.049.
Google Scholar
[41]
T. Marić, D.B. Kothe, D. Bothe, Unstructured un-split geometrical Volume-of-Fluid methods – A review, Journal of Computational Physics 420 (2020) 109695. https://doi.org/10.1016/j.jcp.2020.109695.
Google Scholar
[42]
Q. Zhang, On a Family of Unsplit Advection Algorithms for Volume-of-Fluid Methods, SIAM Journal on Numerical Analysis 51 (2013) 2822–2850. https://doi.org/10.1137/120897882.
Google Scholar
[43]
W.J. Rider, D.B. Kothe, Reconstructing Volume Tracking, Journal of Computational Physics 141 (1998) 112–152. https://doi.org/10.1006/jcph.1998.5906.
Google Scholar
[44]
J. López, J. Hernández, P. Gómez, F. Faura, A volume of fluid method based on multidimensional advection and spline interface reconstruction, Journal of Computational Physics 195 (2004) 718–742. https://doi.org/10.1016/j.jcp.2003.10.030.
Google Scholar
[45]
D.J.E. Harvie, D.F. Fletcher, A new volume of fluid advection algorithm: the defined donating region scheme, International Journal for Numerical Methods in Fluids 35 (2001) 151–172. https://doi.org/10.1002/1097-0363(20010130)35:2<151::AID-FLD87>3.0.CO;2-4.
Google Scholar
[46]
D.J.E. Harvie, D.F. Fletcher, A New Volume of Fluid Advection Algorithm: The Stream Scheme, Journal of Computational Physics 162 (2000) 1–32. https://doi.org/10.1006/jcph.2000.6510.
Google Scholar
[47]
J.E. Pilliod Jr., E.G. Puckett, Second-order accurate volume-of-fluid algorithms for tracking material interfaces, Journal of Computational Physics 199 (2004) 465–502. https://doi.org/10.1016/j.jcp.2003.12.023.
Google Scholar
[48]
A. Cervone, S. Manservisi, R. Scardovelli, S. Zaleski, A geometrical predictor–corrector advection scheme and its application to the volume fraction function, Journal of Computational Physics 228 (2009) 406–419. https://doi.org/10.1016/j.jcp.2008.09.016.
Google Scholar
[49]
R. Comminal, J. Spangenberg, J.H. Hattel, Cellwise conservative unsplit advection for the volume of fluid method, Journal of Computational Physics 283 (2015) 582–608. https://doi.org/10.1016/j.jcp.2014.12.003.
Google Scholar
[50]
J. Mencinger, I. Žun, A PLIC–VOF method suited for adaptive moving grids, Journal of Computational Physics 230 (2011) 644–663. https://doi.org/10.1016/j.jcp.2010.10.010.
Google Scholar
[51]
P. Liovic, M. Rudman, J.-L. Liow, D. Lakehal, D. Kothe, A 3D unsplit-advection volume tracking algorithm with planarity-preserving interface reconstruction, Computers & Fluids 35 (2006) 1011–1032. https://doi.org/10.1016/j.compfluid.2005.09.003.
Google Scholar
[52]
J. Hernández, J. López, P. Gómez, C. Zanzi, F. Faura, A new volume of fluid method in three dimensions—Part I: Multidimensional advection method with face-matched flux polyhedra, International Journal for Numerical Methods in Fluids 58 (2008) 897–921. https://doi.org/10.1002/fld.1776.
Google Scholar
[53]
V. Le Chenadec, H. Pitsch, A 3D Unsplit Forward/Backward Volume-of-Fluid Approach and Coupling to the Level Set Method, Journal of Computational Physics 233 (2013) 10–33. https://doi.org/10.1016/j.jcp.2012.07.019.
Google Scholar
[54]
M. Owkes, O. Desjardins, A computational framework for conservative, three-dimensional, unsplit, geometric transport with application to the volume-of-fluid (VOF) method, Journal of Computational Physics 270 (2014) 587–612. https://doi.org/10.1016/j.jcp.2014.04.022.
Google Scholar
[55]
L. Jofre, O. Lehmkuhl, J. Castro, A. Oliva, A 3-D Volume-of-Fluid advection method based on cell-vertex velocities for unstructured meshes, Computers & Fluids 94 (2014) 14–29. https://doi.org/10.1016/j.compfluid.2014.02.001.
Google Scholar
[56]
T. Marić, H. Marschall, D. Bothe, voFoam – A geometrical Volume of Fluid algorithm on arbitrary unstructured meshes with local dynamic adaptive mesh refinement using OpenFOAM, arXiv preprint (2013) arXiv:1305.3417.
Google Scholar
[57]
T. Marić, H. Marschall, D. Bothe, An enhanced un-split face-vertex flux-based VoF method, Journal of Computational Physics 371 (2018) 967–993. https://doi.org/10.1016/j.jcp.2018.03.048.
Google Scholar
[58]
C.B. Ivey, P. Moin, Conservative volume of fluid advection method on unstructured grids in three dimensions, In: Center for Turbulence Research Annual Research Briefs (2012) 179–192.
Google Scholar
[59]
C.B. Ivey, P. Moin, Conservative and bounded volume-of-fluid advection on unstructured grids, Journal of Computational Physics 350 (2017) 387–419. https://doi.org/10.1016/j.jcp.2017.08.054.
Google Scholar
[60]
J. Roenby, H. Bredmose, H. Jasak, A computational method for sharp interface advection, Royal Society Open Science 3 (2016) 160405. https://doi.org/10.1098/rsos.160405.
Google Scholar
[61]
J. López, P. Gómez, C. Zanzi, F. Faura, H. Hernández, Application of Non-Convex Analytic and Geometric Tools to a PLIC-VOF Method. In: ASME International Mechanical Engineering Congress and Exposition (2016) V007T09A005. https://doi.org/10.1115/IMECE2016-67409.
Google Scholar
[62]
J. López, J. Hernández, P. Gómez, F. Faura, Non-convex analytical and geometrical tools for volume truncation, initialization and conservation enforcement in VOF methods, Journal of Computational Physics 392 (2019) 666–693. https://doi.org/10.1016/j.jcp.2019.04.055.
Google Scholar
[63]
J. López, J. Hernández, P. Gómez, C. Zanzi, R. Zamora, VOFTools 5: An extension to non-convex geometries of calculation tools for volume of fluid methods, Computer Physics Communications (2020) 107277. https://doi.org/10.1016/j.cpc.2020.107277.
Google Scholar
[64]
D.L. Youngs, Time-dependent multi-material flow with large fluid distortion, In: Numerical Methods for Fluid Dynamics, Eds: K.W. Morton, M.J. Baines, Academic Press New York, 1982, pp. 273–285.
Google Scholar
[65]
R. Scardovelli, S. Zaleski, Interface reconstruction with least-square fit and split Eulerian–Lagrangian advection, International Journal for Numerical Methods in Fluids 41 (2003) 251–274. https://doi.org/10.1002/fld.431.
Google Scholar
[66]
R. Scardovelli, S. Zaleski, Analytical Relations Connecting Linear Interfaces and Volume Fractions in Rectangular Grids, Journal of Computational Physics 164 (2000) 228–237. https://doi.org/10.1006/jcph.2000.6567.
Google Scholar
[67]
D. Gueyffier, J. Li, A. Nadim, R. Scardovelli, S. Zaleski, Volume-of-fluid interface tracking with smoothed surface stress methods for three-dimensional flows, Journal of Computational Physics 152 (1999) 423–456. https://doi.org/10.1006/jcph.1998.6168.
Google Scholar
[68]
V. Dyadechko, M. Shashkov, Moment-of-fluid interface reconstruction, Los Alamos Report LA-UR-07-1537 (2007).
Google Scholar
[69]
F. Tampieri, Newell’s method for computing the plane equation of a polygon, In: Graphics Gems III (1992) 231–232. https://doi.org/10.1016/B978-0-08-050755-2.50052-X.
Google Scholar
[70]
J. López, J. Hernández, P. Gómez, F. Faura, A new volume conservation enforcement method for PLIC reconstruction in general convex grids, Journal of Computational Physics 316 (2016) 338–359. https://doi.org/10.1016/j.jcp.2016.04.018.
Google Scholar
[71]
C.W.S. Bruner, Geometric Properties of Arbitrary Polyhedra in Terms of Face Geometry, AIAA Journal 33 (1995) 1350–1350. https://doi.org/10.2514/3.12556.
Google Scholar
[72]
R.N. Goldman, Area of planar polygons and volume of polyhedra, In: Graphics Gems II (1991) 170–171. https://doi.org/10.1016/B978-0-08-050754-5.50043-8.
Google Scholar
[73]
B. Freireich, M. Kodam, C. Wassgren, An exact method for determining local solid fractions in discrete element method simulations, AIChE Journal 56 (2010) 3036–3048. https://doi.org/10.1002/aic.12223.
Google Scholar
[74]
J. López, C. Zanzi, P. Gómez, F. Faura, J. Hernández, A new volume of fluid method in three dimensions—Part II: Piecewise-planar interface reconstruction with cubic-Bézier fit, International Journal for Numerical Methods in Fluids 58 (2008) 923–944. https://doi.org/10.1002/fld.1775.
Google Scholar
[75]
P. Cifani, W.R. Michalek, G.J.M. Priems, J.G. Kuerten, C.W.M. van der Geld, B.J. Geurts, A comparison between the surface compression method and an interface reconstruction method for the VOF approach, Computers & Fluids 136 (2016) 421–435. https://doi.org/10.1016/j.compfluid.2016.06.026.
Google Scholar
[76]
A. Asuri Mukundan, T. Ménard, J.C. Brändle de Motta, A. Berlemont, A 3D Moment of Fluid method for simulating complex turbulent multiphase flows, Computers & Fluids 198 (2020) 104364. https://doi.org/10.1016/j.compfluid.2019.104364.
Google Scholar
[77]
C.B. Ivey, P. Moin, Accurate interface normal and curvature estimates on three-dimensional unstructured non-convex polyhedral meshes, Journal of Computational Physics 300 (2015) 365–386. https://doi.org/10.1016/j.jcp.2015.07.055.
Google Scholar
[78]
H.T. Ahn, M. Shashkov, Multi-material interface reconstruction on generalized polyhedral meshes, Journal of Computational Physics 226 (2007) 2096–2132. https://doi.org/10.1016/j.jcp.2007.06.033.
Google Scholar
[79]
G. Černe, S. Petelin, I. Tiselj, Numerical errors of the volume-of-fluid interface tracking algorithm, International Journal for Numerical Methods in Fluids 38 (2002) 329–350. https://doi.org/10.1002/fld.228.
Google Scholar
[80]
S.J. Mosso, B.K. Swartz, D.B. Kothe, R.C. Ferrell, A parallel, volume-tracking algorithm for unstructured meshes, In: Parallel Computational Fluid Dynamics 1996: Algorithms and Results Using Advanced Computers, 1997, pp. 368–375. https://doi.org/10.1016/B978-044482327-4/50113-3.
Google Scholar
1
This definition of the CFL number is different from the usual definition used in multi-dimensional algebraic advection schemes. However, the component-wise definition is more meaningful in the context of geometric VOF schemes, because it determines the number of layers of cells around the interfacial cells where the liquid volume fractions need to be updated.

Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics Figures

Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics

전산 유체 역학을 사용하여 GMAW에 대한 Marangoni 흐름에서 슬래그 이동의 수치 시뮬레이션

Dae-WonChoaYeong-DoParkbMuralimohanCheepucaBusan Machinery Research Center, Korea Institute of Machinery and Materials, 48, Mieumsandan 5-ro 41beon-gil, Gangseo-gu, Busan 46744, Republic of KoreabDepartment of Advanced Materials Engineering, Dong-Eui University, Busan, Republic of KoreacSuper-TIG Welding Co., Limited, Busan, Republic of Korea

Keywords : Marangoni flowMolten slag movementMolten pool behavorSurface tension gradient

Abstract

이 연구는 전산 유체 역학을 이용하여 스프레이 모드 가스 금속 아크 용접에서 생성되는 산화물인 용융 슬래그의 거동을 분석했습니다. 주로 규산염 (SiO2)으로 구성된 용융 슬래그는 용융 풀 표면에 있습니다. 일반적으로 용융 슬래그는 아크 플라즈마 경계 주변에서 생성된다고 가정합니다.

따라서 이 연구의 수치 시뮬레이션에서 슬래그는 특정 밀도와 크기를 가진 구형 입자로 모델링됩니다. Marangoni 유동 효과를 비교하기 위해 이 연구는 표면 장력 구배가 다른 두 가지 사례 (양수 및 음수)를 조사했습니다. 수치 시뮬레이션과 실험 결과 모두 음의 표면 장력 구배가 비드 가장자리에 갇힌 슬래그를 형성하는 반면 양의 표면 장력 구배는 상단 표면의 중앙에 갇힌 슬래그를 형성하는 것으로 나타났습니다.

This study analyzed the behavior of molten slag, which is an oxide generated during spray mode gas metal arc welding, with computational fluid dynamics. The molten slag, composed mainly of silicate (SiO2), is located on the molten pool surface. It is generally assumed that the molten slag is generated around the arc plasma boundary. Therefore, in the numerical simulation in this study the slag is modeled as a spherical particle, which has a specific density and size. To compare the Marangoni flow effect, this study investigated two different cases where the surface tension gradients were different (positive and negative). In both the numerical simulation and experimental results it was found that negative surface tension gradient formed trapped slag on the bead edge while the positive surface tension gradient formed trapped slag on the center of the top surface.

Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics Figures
Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics Figures
Weld bead surface images showing the slag formation location for (a) wire 1 and (b) wire 2.

The effect of alloying elements of gas metal arc welding (GMAW) wire on weld pool flow and slag formation location in cold metal transfer (CMT)

가스 금속 아크 용접 (GMAW) 와이어의 합금 원소가 CMT (Cold Metal Transfer)에서 용접 풀 흐름 및 슬래그 형성 위치에 미치는 영향

Md. R. U. Ahsan1,3, Muralimohan. Cheepu2, Yeong-Do Park* 2,3
1Department of Mechanical Engineering, International University of Business, Agriculture and Technology,
Dhaka 1230, Bangladesh.
r.ahsan06me@gmail.com
2Department of Advanced Materials and Industrial Management Engineering, Dong-Eui University, Busan
47340, Republic of Korea.
muralicheepu@gmail.com
3Department of Advanced Materials Engineering, Dong-Eui University, B

Abstract

용접시 표면 장력 구동 흐름 또는 마랑고니 흐름은 용접 비드 모양을 제어하는데 중요한 역할을 하므로 용접 접합 품질에 영향을 미칩니다. 용해된 금속의 표면 장력은 보통 음의 온도 계수를 가지므로 용접 풀이 중심에서 토우 방향으로 흐르게 됩니다.

표면 장력의 이 온도 계수는 황(S), 산소(O), 셀레늄(Se) 및 텔루륨(Te)과 같은 표면 활성 요소가 있는 경우 양의 계수로 변경할 수 있습니다. 소모품에 존재하는 탈산화 원소의 양이 용접 금속에 존재하는 산소량을 결정합니다. 탈산화제 양이 적으면 용접 금속에 산소 농도가 높아집니다.

적절한 양의 산소가 있으면 용융지에 표면 장력 구배의 양의 온도 계수가 발생할 수 있습니다. 이 경우 용접 풀은 토우에서 중앙 방향으로 흐릅니다. 그 결과, 아크와 용융지에 있는 화농성 반응의 경우, 합금 요소의 다양한 산화물이 슬래그(slag)라고 합니다. 슬래그는 용융지 표면에 떠서 용융지 흐름 패턴에 따라 누적됩니다.

그 결과, 슬래그는 용융지 흐름 패턴에 따라 용접 비드 중심 또는 토우 중심을 따라 형성됩니다. 슬래그는 용접 비드의 외관과 도장 접착력을 저하시키므로 제거해야 합니다. 쉽게 분리할 수 있기 때문에 용접 비드 중심 부근에서 슬래그가 형성되는 것이 좋습니다.

용접 풀의 현장 고속 비디오 촬영, 용접 금속 화학 성분 분석, 소모품 합금 요소가 용접 풀 흐름 패턴 및 슬래그 형성 위치에 미치는 영향이 공개되어 CMT-GMAW의 생산성 향상을 위해 용접 소모품 선택을 용이하게 할 수 있습니다.

The surface tension driven flow or Marangoni flow in welding plays an important role in governing weld bead shape hence affecting the weld joint quality. The surface tension of molten metal usually has a negative temperature coefficient causing the weld pool to flow from the center towards the toe.

This temperature coefficient of the surface tension can be altered to be a positive one in the presence of surface-active elements like sulfur (S), oxygen (O), selenium (Se) and tellurium (Te). The amount of deoxidizing elements present in the consumables governs the amount of oxygen present in the weld metal. The presence of a lower amount of deoxidizers results in higher concentration of oxygen in the weld metal.

The presence of adequate amount of oxygen can result in a positive temperature coefficient of surface tension gradient in the weld pool. In such situation, the weld pool flows from the toe towards the direction of the center. As a result, of pyrometallurgical reactions in the arc and the weld pool various oxides of the alloying elements are former which are referred as slag.

The slags float on the weld pool surface and accumulate following the weld pool flow pattern. As a result, slags form either along the center of the weld bead or the toe depending on the weld pool flow pattern. The slags need to be removed as they degrade the weld bead appearance and paint adhesiveness.

Due to easy detachability, slag formation near the center of the weld bead is desired. From in-situ high-speed videography of weld pool, weld metal chemical composition analysis, the effect of consumables alloying elements on weld pool flow pattern and slag formation location are disclosed, which can facilitate the selection of the welding consumables for better productivity in CMT-GMAW.

Weld bead surface images showing the slag formation location for (a) wire 1 and (b) wire 2.
Weld bead surface images showing the slag formation location for (a) wire 1 and (b) wire 2.
Fig. 2: High-speed movie frames and schematic showing the weld pool flow pattern and the slag formation location for wire 1 and wire 2.
Fig. 2: High-speed movie frames and schematic showing the weld pool flow pattern and the slag formation location for wire 1 and wire 2.
Fig. 3: Quantitative analysis data on slag formation for different wire.
Fig. 3: Quantitative analysis data on slag formation for different wire.

References

[1] S. Lu, H. Fujii, and K. Nogi: “Marangoni convection and weld shape variations in He-CO2 shielded gas
tungsten arc welding on SUS304 stainless steel,” J. Mater. Sci., Vol. 43, No. 13 (2008), pp. 4583–4591.
[2] Y. Wang and H. L. Tsai: “Effects of surface active elements on weld pool fluid flow and weld penetration in
gas metal arc welding,” Metall. Mater. Trans. B, Vol. 32, No. 3 (2001), pp. 501–515.
[3] P. Sahoo, T. Debroy, and M. J. McNallan: “Surface tension of binary metal-surface active solute systems under
conditions relevant to welding metallurgy,” Metall. Trans. B, Vol. 19, No. 2 (1988), pp. 483–491.
[4] M. J. Mcnallan and T. Debroy: “Effect of Temperature and in Fe-Ni-Cr Alloys Containing Sulfur,”Metall.
Trans. B,Vol. 22, No. 4 (1991) pp. 557-560.
[5] S. Kou, C. Limmaneevichitr, and P. S. Wei: “Oscillatory Marangoni flow: A fundamental study by conductionmode laser spot welding,” Weld. J., Vol. 90, No. 12 (2011), pp. 229–240.
[6] M. Hasegawa, M. Watabe, and W. H. Young: “Theory of the surface tension of liquid metals,” J. Phys. F Met.
Phys., Vol. 11, No. 8 (2000), pp. 173–177.
[7] C. Heiple and J. Roper: “Effect of selenium on GTAW fusion zone geometry,” Weld. J., (1981), pp. 143–145.
[8] C. R. Heiple and J. R. Roper: “Mechanism for Minor Element Effect on {GTA} Fusion Zone Geometry,”
Weld. J., Vol. 61, (1982)pp. 97–102.
[9] C. Heiple, J. Roper, R. Stagner, and R. Aden: “Surface active element effects on the shape of GTA, laser and
electron beam welds,” Weld. J., (1983) pp. 72–77.
[10] C. R. Heiple and P. Burgardt: “Effects of SO2 Shielding Gas Additions on GTA Weld Shape,” Weld. J., (1985)
pp. 159–162.
[11] P. F. Mendez, and T. W. Eagar: “Penetration and Defect Formation in High-Current Arc Welding,” Weld. J.,
(2003) pp. 296–306.
[12] B. Ribic, S. Tsukamoto, R. Rai, and T. DebRoy: “Role of surface-active elements during keyhole-mode laser
welding,” J. Phys. D. Appl. Phys., Vol. 44, No. 48 (2011), pp. 485–203.
[13] C. Limmaneevichitr and S. Kou, “Experiments to simulate effect of Marangoni convection on weld pool shape,”
Weld. J., Vol. 79, (2000)pp. 231–237.
[14] C. Limmaneevichitr and S. Kou: “Visualization of Marangoni convection in simulated weld pools containing a
surface-active agent,” Weld. J., vol. 79, No. 11 (2000), pp. 324–330.
[15] Y. Wang and H. L. Tsai: “Impingement of filler droplets and weld pool dynamics during gas metal arc welding
process,” Int. J. Heat Mass Transf., Vol. 44, No. 11 (2001), pp. 2067–2080.
[16] S. Liu: “Pyrometallurgical Studies of Molten Metal Droplets for the Characterization of Gas Metal Arc
Welding,” Proc 9thTrends in Welding Research Conf., Chicago, Illinois, June 2012, pp. 353–361.
[17] Y. Umehara, R. Suzuki and T. Nakano: “Development of the innovative GMA wire improving the flow
direction of molten pool” Quart. J. Japan Weld. Soc., Vol. 27, NO. 2 (2009), pp. 163–168.

Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.

Investigation on Laser Welding of Al Ribbon to Cu Sheet: Weldability, Microstructure, and Mechanical and Electrical Properties

알루미늄 리본과 구리 시트의 레이저 용접에 대한 조사 : 용접성, 미세 구조, 기계적 및 전기적 특성

Won‐Sang Shin 1,†, Dae‐Won Cho 2,†, Donghyuck Jung 1, Heeshin Kang 3, Jeng O Kim 3, Yoon‐Jun Kim 1,*
and Changkyoo Park 3,*

Al 리본과 Cu 시트의 펄스 레이저 용접은 전력 전자 모듈의 전기적 상호 연결에 대해 조사되었습니다. 결함 없는 Al / Cu 조인트를 얻기 위해 레이저 출력, 스캔 속도 및 열 입력이 서로 다른 다양한 실험 조건이 사용되었습니다. Al / Cu 레이저 용접 중에 금속 간 화합물이 용접 영역에 형성되었습니다. 전자 탐침 마이크로 분석기와 투과 전자 현미경으로 Al4Cu9, Al2Cu, AlCu 등으로 밝혀진 금속 간 화합물의 상을 확인했습니다. 전산 유체 역학 시뮬레이션은 Marangoni 효과가 용융 풀의 순환을 유도하여 혼합물을 생성하는 것으로 나타났습니다. Al과 Cu의 결합과 Al / Cu 조인트에서 소용돌이 모양의 구조 형성. Al / Cu 접합부의 인장 전단강도와 전기 저항을 측정하였으며 용접 면적과 강한 상관 관계를 보였다. Al / Cu 접합부의 용접 면적이 증가함에 따라 기계적 강도의 감소와 전기 저항의 증가가 측정 되었습니다. 또한 무결점 Al / Cu 접합을 위한 공정 창을 개발하고 Al / Cu 레이저 브레이즈 용접을 위한 실험 조건을 조사하여 Al / Cu 접합에서 금속 간 화합물 형성을 최소화했습니다.

Introduction

전기 상호 연결은 전력 전자 모듈을 패키징하는 데 중요합니다. 우수한 기계적 및 전기적 특성을 가진 견고한 전기적 상호 연결은 전력 전자 모듈의 전기적 고장을 방지하는 데 필수적입니다. 저항 스폿 용접, 브레이징, 납땜 및 초음파 용접 (USW)이 전기 상호 연결에 사용되었습니다.

납땜과 납땜 모두 저온 공정으로 인해 접합부에서 한계 변형과 잔류 응력이 발생합니다 [1]. 필러 합금은 두 공정 모두 견고한 전기 접촉을 달성하는 데 필수적입니다. 따라서 조인트는 서로 접촉하는 서로 다른 금속으로 구성됩니다.

결과적으로 조인트는 부식 환경에서 갈바닉 부식에 취약 할 수 있습니다 [2,3]. 더욱이, 비금속과 충전재 사이의 친화도를 고려해야 하기 때문에 제한된 충전재 만 특정 조인트에 사용할 수 있습니다 [1]. USW는 용접 온도가 낮고 용접 시간이 짧기 때문에 접합부의 변형이 비교적 적습니다.

따라서 이는 특히 연질 재료 (예 : Al, Cu, Ag, Au 및 Ni)의 경우 기존 접합 방법을 대체하고 있습니다 [4–6]. 그러나 Cu를위한 USW 공정의 경우, 표면 산화물이 강해 용접성이 저하되는 것을 방지하기 위해 Cu 표면에 Sn 또는 Ni 코팅이 필요하며, 이는 공정 속도를 늦추고 산업적 응용을위한 경제적 측면을 악화시킨다 [7 , 8].

레이저 용접은 쉬운 제어, 고정밀 및 원격 처리의 특성으로 인해 전력 전자 모듈의 전기 연결에 대한 유망한 후보입니다. 열의 영향을 받는 작은 영역과 변형은 전기 접점의 손상을 최소화 할 것으로 예상됩니다 [9-11]. 또한 레이저 용접을 위해 추가 표면 준비가 필요하지 않습니다.

이종 재료의 용접은 산업 응용 분야에서 중요했습니다. 더욱이 그림 1 [12,13]에서 볼 수 있듯이 전기 연결을위한 와이어 또는 리본 본딩에 여러 다른 조인트가 필요하기 때문에 전력 전자 모듈에서 필수적인 기술이되고 있습니다.

전기 접점의 다양한 조합 중에서 Al과 Cu는 높은 전기 전도성으로 인해 전기 연결에 중요한 재료로 종종 간주됩니다 [14]. 그러나 Al과 Cu의 서로 다른 용접은 금속 간 화합물 (IMC)의 형성을 촉진하고 동시에 Al / Cu 조인트의 기계적 및 전기적 특성에 영향을 줍니다. 일반적으로 Al / Cu 조인트 내부에 IMC가 있으면 연성 및 전기 저항에 해를 끼치므로 균열이 쉽게 발생하고 용접을 통한 전기 전도도를 방해합니다 [15,16].

따라서 견고한 Al / Cu 조인트를 얻으려면 IMC의 형성을 피해야합니다. 여러 연구에서 Al 및 Cu 시트의 레이저 빔 용접을 조사했습니다. 연속파 (CW) 레이저가 Al / Cu 조인트에 사용되었습니다 [17-23]. 큰 열 입력과 상당한 IMC 형성으로 인해 용접 영역에서 많은 균열이 관찰되었습니다 [18,19].

CW 레이저 빔의 공간 진동은 Al / Cu 조인트의 용접 품질을 향상시키는 것으로 나타났습니다. 직선 CW 레이저 빔 [18-20]과 비교하여 용접 영역에서 IMC 크기가 더 작은 기공과 균열이 더 적습니다.

Al과 Cu 시트의 겹침 접합에는 CW 단일 모드 파이버 레이저를 사용했으며, IMC 형성을 억제하여 높은 용접 속도 (즉, 50m / min)에서 견고한 Al / Cu 접합을 얻었습니다 [22]. Mai et al. [23]은 다른 Al / Cu 용접을 달성하기 위해 펄스 레이저를 사용했습니다.

그들은 Al / Cu 용접성이 레이저 공정 매개 변수에 크게 의존한다는 것을 밝혔으며 100mm / min 미만의 스캔 속도에서 균열없는 Al / Cu 접합을 달성하는 데 성공했습니다.

본문 내용 생략 : 문서 하단부의 원문보기를 참고하시기 바랍니다.

Figure 1. Schematic diagram of the insulated gate bipolar transistors (IGBT) power module. Red‐dotted box indicated the electrical connections
Figure 1. Schematic diagram of the insulated gate bipolar transistors (IGBT) power module. Red‐dotted box indicated the electrical connections
Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.
Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.
Figure 3. Schematic diagram of the numerical simulation domain and boundary conditions.
Figure 3. Schematic diagram of the numerical simulation domain and boundary conditions.
Figure 4. Experimental setup for the four‐point electrical resistance measurement.
Figure 4. Experimental setup for the four‐point electrical resistance measurement.
Figure 5. Cross‐sectional OM image of the Al/Cu joints in parallel to the laser welding direction. The laser power and scan speed were set at 2300 W and 20 mm/s, respectively.
Figure 5. Cross‐sectional OM image of the Al/Cu joints in parallel to the laser welding direction. The laser power and scan speed were set at 2300 W and 20 mm/s, respectively.
Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20.
Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20,
Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20.
Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (d) 27/20.
Figure 7. EPMA line scan analysis and identification of the IMCs for the (a) 23/20 and (b) 25/15.4.
Figure 7. EPMA line scan analysis and identification of the IMCs for the (a) 23/20 and (b) 25/15.4.
Figure 8. TEM analysis for the 25/28.6. (a) Indicating the location of TEM analysis in SEM image of the welding zone. (b) TEM bright‐field image and SAED pattern insets, examined at the location (1) in figure (a), confirmed Al‐rich phase (white globular shape) and Al2Cu eutectic phase (gray region), and (c) TEM bright‐field image and SAED pattern inset of Al4Cu9, examined at the location (2) in figure (a).
Figure 8. TEM analysis for the 25/28.6. (a) Indicating the location of TEM analysis in SEM image of the welding zone. (b) TEM bright‐field image and SAED pattern insets, examined at the location (1) in figure (a), confirmed Al‐rich phase (white globular shape) and Al2Cu eutectic phase (gray region), and (c) TEM bright‐field image and SAED pattern inset of Al4Cu9, examined at the location (2) in figure (a).
Figure 9. Temperature profiles and molten pool flow on transverse cross‐section (y–z plane at x = 1.23 cm): (a) Negative surface tension gradient for the 23/20 (Case 1), (b) negative surface tension gradient for the 25/15.4 (Case 2), (c) positive surface tension gradient for the 25/15.4 (Case 3), and (d) without surface tension for the 25/15.4 (Case 4).
Figure 9. Temperature profiles and molten pool flow on transverse cross‐section (y–z plane at x = 1.23 cm): (a) Negative surface tension gradient for the 23/20 (Case 1), (b) negative surface tension gradient for the 25/15.4 (Case 2), (c) positive surface tension gradient for the 25/15.4 (Case 3), and (d) without surface tension for the 25/15.4 (Case 4).
Figure 12. Results of the tensile shear tests for the (a) 23/20: fracture at the Al ribbon and (b) 25/15.4: fracture at the weld
Figure 12. Results of the tensile shear tests for the (a) 23/20: fracture at the Al ribbon and (b) 25/15.4: fracture at the weld
Figure 13. Stress–strain curves obtained by the tensile shear tests.
Figure 13. Stress–strain curves obtained by the tensile shear tests.

References

  1. Schwartz, M.M.; Aircraft, S. Introduction to Brazing and Soldering. ASM Int. 2018, 6, doi.org/10.31399/asm.hb.v06.a0001344.
  2. Vianco, P.T. Corrosion issues in solder joint design and service. Weld. J. 1999, 78, 39–46.
  3. Shi, Y.; Li, J.; Zhang, G.; Huang, J.; Gu, Y. Corrosion Behavior of Aluminum‐Steel Weld‐Brazing Joint. J. Mater. Eng. Perform.
    2016, 25, 1916–1923, doi:10.1007/s11665‐016‐2020‐9.
  4. Harman, G.G. Wire Bonding in Microelectronics: Materials, Processes, Reliability and Yield, 3rd ed; McGraw‐Hill Education: New
    York, NY, USA, 2010; ISBN 9780071642651.
  5. Aonuma, M.; Nakata, K. Dissimilar metal joining of ZK60 magnesium alloy and titanium by friction stir welding. Mater. Sci.
    Eng. B Solid State Mater. Adv. Technol. 2012, 177, 543–548, doi:10.1016/j.mseb.2011.12.031.
  6. Sasaki, T.; Watanabe, T.; Hosokawa, Y.; Yanagisawa, A. Analysis for relative motion in ultrasonic welding of aluminium sheet.
    Sci. Technol. Weld. Jt. 2012, 18, 19–24, doi:10.1179/1362171812Y.0000000066.
  7. Maeda, M.; Sato, T.; Inoue, N.; Yagi, D.; Takahashi, Y. Anomalous microstructure formed at the interface between copper ribbon
    and tin‐deposited copper plate by ultrasonic bonding. Microelectron. Reliab. 2011, 51, 130–136, doi:10.1016/j.microrel.2010.05.009.
  8. Maeda, M.; Yagi, D.; Takahashi, Y. Interfacial microstructure between copper ribbon and nickel‐coated copper plate formed by
    ultrasonic bonding. Q. J. Jpn. Weld. Soc. 2013, 31, 188–191, doi:10.2207/qjjws.31.188s.
  9. Sun, Z.; Ion, J.C. Laser welding of dissimilar metal combinations. J. Mater. Sci. 1995, 30, 4205–4214, doi:10.1007/BF00361499.
  10. Yan, S.; Hong, Z.; Watanabe, T.; Jingguo, T. CW/PW dual‐beam YAG laser welding of steel/aluminum alloy sheets. Opt. Lasers
    Eng. 2010, 48, 732–736, doi:10.1016/j.optlaseng.2010.03.015.
  11. Mehlmann, B.; Gehlen, E.; Olowinsky, A.; Gillner, A. Laser micro welding for ribbon bonding. Phys. Procedia 2014, 56, 776–781,
    doi:10.1016/j.phpro.2014.08.085.
  12. Nwanoro, K.C.; Lu, H.; Yin, C.; Bailey, C. An analysis of the reliability and design optimization of aluminium ribbon bonds in
    power electronics modules using computer simulation method. Microelectron. Reliab. 2018, 87, 1–14,
    doi:10.1016/j.microrel.2018.05.013.
  13. Li, H.; Cao, B.; Yang, J.W.; Liu, J. Modeling of resistance heat assisted ultrasonic welding of Cu‐Al joint. J. Mater. Process. Technol.
    2018, 256, 121–130, doi:10.1016/j.jmatprotec.2018.02.008.
  14. Davis, J.R. Copper and Copper Alloys. In ASM Speciality Handbook; ASM International: Almere, The Netherlands, 2001; ISBN
    2001022956
  1. Rabkin, D.M.; Ryabov, V.R.; Lozovskaya, A.V.; Dovzhenko, V.A. Preparation and properties of copper‐aluminum intermetallic
    compounds. Sov. Powder Metall. Met. Ceram. 1970, 9, 695–700, doi:10.1007/BF00803820.
  2. Chen, C.Y.; Chen, H.L.; Hwang, W.S. Influence of interfacial structure development on the fracture mechanism and bond
    strength of aluminum/copper bimetal plate. Mater. Trans. 2006, 47, 1232–1239, doi:10.2320/matertrans.47.1232.
  3. Schmidt, P.A.; Schweier, M.; Zaeh, M.F. Joining of lithium‐ion batteries using laser beam welding: Electrical losses of welded
    aluminum and copper joints. J. Laser Appl. 2012, 915, doi:10.2351/1.5062563.
  4. Smith, S.; Blackburn, J.; Gittos, M.; De Bono, P.; Hilton, P. Welding of dissimilar metallic materials using a scanned laser beam.
    J. Laser Appl. 2013, 493, doi:10.2351/1.5062921.
  5. Solchenbach, T.; Plapper, P. Mechanical characteristics of laser braze‐welded aluminium‐copper connections. Opt. Laser Technol.
    2013, 54, 249–256, doi:10.1016/j.optlastec.2013.06.003.
  6. Kraetzsch, M.; Standfuss, J.; Klotzbach, A.; Kaspar, J.; Brenner, B.; Beyer, E. Laser Beam Welding with High‐Frequency Beam
    Oscillation: Welding of Dissimilar Materials with Brilliant Fiber Lasers. Phys. Procedia 2011, 12, 142–149,
    doi:10.1016/j.phpro.2011.03.018.
  7. Solchenbach, T.; Plapper, P.; Cai, W. Electrical performance of laser braze‐welded aluminum‐copper interconnects. J. Manuf.
    Process. 2014, 16, 183–189, doi:10.1016/j.jmapro.2013.12.002.
  8. Lee, S.J.; Nakamura, H.; Kawahito, Y.; Katayama, S. Effect of welding speed on microstructural and mechanical properties of
    laser lap weld joints in dissimilar Al and Cu sheets. Sci. Technol. Weld. Jt. 2014, 19, 111–118, doi:10.1179/1362171813Y.0000000168.
  9. Mai, T.A.; Spowage, A.C. Characterisation of dissimilar joints in laser welding of steel‐kovar, copper‐steel and copper‐
    aluminium. Mater. Sci. Eng. A 2004, 374, 224–233, doi:10.1016/j.msea.2004.02.025.
  10. Zhang, G.; Takahashi, Y.; Heng, Z.; Takashima, K.; Misawa, K. Ultrasonic weldability of al ribbon to cu sheet and the dissimilar
    joint formation mode. Mater. Trans. 2015, 56, 1842–1851, doi:10.2320/matertrans.M2015251.
  11. Zhu, B.; Zhen, L.; Xia, H.; Su, J.; Niu, S.; Wu, L.; Tan, C.; Chen, B. Effect of the scanning path on the nanosecond pulse laser
    welded Al/Cu lapped joint. Opt. Laser Technol. 2021, 139, 106945, doi.org/10.1016/j.optlastec.2021.106945.
  12. Kumar, A.; Gupta, M.P.; Banerjee, J.; Neogy, S.; Keskar, N.; Bhatt, R.B.; Behere, P.G.; Biswas, D.J. Micro‐Welding of Stainless
    Steel and Copper Foils Using a Nano‐Second Pulsed Fiber Laser. Lasers Manuf. Mater. Process. 2019, 6, 158–172,
    doi.org/10.1007/s40516‐019‐00088‐w.
  13. Trinh, L.N.; Lee, D. The Characteristics of Laser Welding of a Thin Aluminum Tab and Steel Battery Case for Lithium‐Ion
    Battery. Metals 2020, 10, 842, doi.org/10.3390/met10060842.
  14. Cho, D.W.; Park, J.H.; Moon, H.S. A study on molten pool behavior in the one pulse one drop GMAW process using
    computational fluid dynamics. Int. J. Heat Mass Transf. 2019, 139, 848–859, doi:10.1016/j.ijheatmasstransfer.2019.05.038.
  15. Cho, W.I.; Na, S.J.; Cho, M.H.; Lee, J.S. Numerical study of alloying element distribution in CO2 laser‐GMA hybrid welding.
    Comput. Mater. Sci. 2010, 49, 792–800, doi:10.1016/j.commatsci.2010.06.025.
  16. Cho, D.W.; Kiran, D.V.; Na, S.J. Analysis of molten pool behavior by flux‐wall guided metal transfer in low‐current submerged
    arc welding process. Int. J. Heat Mass Transf. 2017, 110, 104–112, doi:10.1016/j.ijheatmasstransfer.2017.02.060.
  17. Cho, W.‐I.; Na, S.‐J. Impact of Wavelengths of CO2, Disk, and Green Lasers on Fusion Zone Shape in Laser Welding of Steel. J.
    Weld. Jt. 2020, 38, 235–240, doi:10.5781/jwj.2020.38.3.1.
  18. Sim, A.; Chun, E.J.; Cho, D.W. Numerical Simulation of Surface Softening Behavior for Laser Heat Treated Cu‐Bearing Medium
    Carbon Steel. Met. Mater. Int. 2020, 26, 1207–1217, doi:10.1007/s12540‐019‐00577‐9.
  19. Jarwitz, M.; Fetzer, F.; Weber, R.; Graf, T. Weld seam geometry and electrical resistance of laser‐welded, aluminum‐copper
    dissimilar joints produced with spatial beam oscillation. Metals 2018, 8, 510, doi:10.3390/met8070510.
  20. Weigl, M.; Albert, F.; Schmidt, M. Enhancing the ductility of laser‐welde copper‐aluminum connections by using adapted filler
    materia. Phys. Procedia 2011, 12, 335–341, doi:10.1016/j.phpro.2011.03.141.
  21. Chen, J.; Lai, Y.S.; Wang, Y.W.; Kao, C.R. Investigation of growth behavior of Al‐Cu intermetallic compounds in Cu wire
    bonding. Microelectron. Reliab. 2011, 51, 125–129, doi:10.1016/j.microrel.2010.09.034.
  22. Chen, H.; Yang, L.; Long, J. First‐principles investigation of the elastic, Vickers hardness and thermodynamic properties of Al‐
    Cu intermetallic compounds. Superlattices Microstruct. 2015, 79, 156–165, doi:10.1016/j.spmi.2014.11.005.
  23. Liu, H.J.; Shen, J.J.; Zhou, L.; Zhao, Y.Q.; Liu, C.; Kuang, L.Y. Microstructural characterisation and mechanical properties of
    friction stir welded joints of aluminium alloy to copper. Sci. Technol. Weld. Jt. 2011, 16, 92–99,
    doi:10.1179/1362171810Y.0000000007.
  24. Hug, E.; Bellido, N. Brittleness study of intermetallic (Cu, Al) layers in copper‐clad aluminium thin wires. Mater. Sci. Eng. A
    2011, 528, 7103–7106, doi:10.1016/j.msea.2011.05.077.
  25. Braunović, M.; Alexandrov, N. Intermetallic Compounds At Aluminum‐To‐Copper Electrical Interfaces: Effect of Temperature
    And Electric Current. IEEE Trans. Compon. Packag. Manuf. Technol. Part A 1994, 17, 78–85, doi:10.1109/95.296372.
  26. Lee, W.B.; Bang, K.S.; Jung, S.B. Effects of intermetallic compound on the electrical and mechanical properties of friction welded
    Cu/Al bimetallic joints during annealing. J. Alloys Compd. 2005, 390, 212–219, doi:10.1016/j.jallcom.2004.07.057.
Figure 2. Ink fraction contours for mesh 1 through 4 (left to right) at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs.

Coupled CFD-Response Surface Method (RSM) Methodology for Optimizing Jettability Operating Conditions

분사성 작동 조건을 최적화하기 위한 결합된 CFD-Response Surface Method(RSM)

Nuno Couto 1, Valter Silva 1,2,* , João Cardoso 2, Leo M. González-Gutiérrez 3 and Antonio Souto-Iglesias 41
INEGI-FEUP, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal;
nunodiniscouto@hotmail.com
2 VALORIZA, Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal; jps.cardoso@ipportalegre.pt
3 CEHINAV, DMFPA, ETSIN, Universidad Politécnica de Madrid, 28040 Madrid, Spain; leo.gonzalez@upm.es
4 CEHINAV, DACSON, ETSIN, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
antonio.souto@upm.es

  • Correspondence: valter.silva@ipportalegre.pt; Tel.: +351-245-301-592

소개

물방울 생성에 대한 이해는 여러 산업 응용 분야에서 매우 중요합니다 [ 1 ]. 잉크젯 프린팅 프로세스는 일반적으로 10 ~ 100 μm [ 1 ] 범위의 독특하고 작은 액적 크기를 특징으로 하며 연속적 또는 충동적 흐름을 사용하여 얻을 수 있습니다 (마지막 방식은 주문형 드롭 (DoD)이라고도 함). 잉크젯).

여러 장점 덕분에 DoD 방법은 산업 환경에서 상당한 수용을 얻고 있습니다 [ 2 ].DoD는 복잡한 프로세스이며 유체 속성, 노즐 형상 및 구동 파형 [ 1 , 3 ]의 세 가지 주요 범주로 분류되는 여러 매개 변수에 따라 달라집니다 .그러나 길이와 시간 척도가 모두 마이크로 오더 [ 4 ] 이기 때문에 실험을하기가 어렵습니다 .

결과적으로 실험 설정은 항상 비용이 많이 들고 복잡하며 CFD (전산 유체 역학)와 같은 고급 수치 접근 방식이 엄격한 요구 사항입니다 [ 5 , 6 ]. VOF (volume-of-fluid) 접근 방식은 액체 분해 및 액적 생성에 대한 다상 공정을 시뮬레이션하기위한 적절한 대안으로 밝혀졌으며 과거 연구에서 그대로 사용되었습니다 [ 7 , 8], 인쇄 프로세스의 맥락에서 전자는 여전히 현재 연구의 주제입니다. 

또한 VOF 체계를 사용하면 단일 운동량 방정식 세트를 해결하고 도메인 전체에 걸쳐 각 유체의 체적 분율을 추적하여 명확하게 정의된 인터페이스로 둘 이상의 혼합 불가능한 유체를 효과적으로 시뮬레이션 할 수 있습니다. Feng [ 9 ]는 VOF 접근 방식을 사용하여 일시적인 유체 인터페이스 변형 및 중단을 효과적으로 추적하는 패키지 FLOW-3D를 사용하여 낙하 배출 중 복잡한 유체 역학 프로세스를 시뮬레이션하는 선구자 작업 중 하나를 수행했습니다.

주요 목표는 볼륨 및 속도와 같은 민감한 변수를 더 잘 이해하면서 장치 개발에서 일반적인 설계 규칙을 구현하는 것이 었습니다. 이러한 종류의 공정과 관련된 주요 질문 중 하나는 안정적인 액적 형성을 위한 작동 범위의 정의입니다.

Fromm [ 10 ]은 Reynolds 수와 Weber 수의 제곱근 비율이 2보다 작으면 안정적인 방울을 생성 할 수 없다는 것을 확인했습니다. 이 무차원 값은 나중에 Z 번호로 알려졌으며 분사 가능성 범위 [ 11 ]를 정의합니다 . 문헌에서 분사 가능성을 위한 Z 간격은 1 ~ 10 [ 12 ], 4 ~ 14 [ 13 ] 또는 0.67 ~ 50 [ 14]을 찾을 수 있습니다. 

이것은 Z 값 만으로는 분사 가능성 조건을 나타낼 수 없음을 분명히 의미합니다. 실제로, 다른 속성을 가진 유체는 다른 인쇄 품질을 나타내면서 동일한 Z 값을 나타낼 수 있습니다. 액적 생성 공정과 해당 분사 성은 주로 전체 공정 품질에 큰 영향을 미치는 매개 변수 세트에 의해 결정됩니다. 

토대 메커니즘을 더 잘 이해하려면 확장 된 작동 조건 및 매개 변수 세트를 고려하여 여러 실험 또는 수치 실행을 수행해야 합니다. DoE (design-of-experiment) 접근 방식과 같은 체계적인 접근 방식이 없으면 이것은 달성하기 매우 어려운 작업이 될 수 있습니다. 최적화 문제를 해결하기 위해 반응 표면 방법을 사용하여 처음으로 체계화된 접근 방식이 개발된 Box and Wilson [ 15 ] 의 선구자 기사 이후 ,이 입증된 방법론은 많은 화학 및 산업 공정[ 16 ] 및 기타 관련 학계에 성공적으로 적용되었습니다.

예를 들어 Silva와 Rouboa [ 17 ]는 직접 메탄올 연료 전지의 출력 밀도에 영향을 미치는 관련 매개 변수를 식별하기 위해 반응 표면 방법론 (RSM)을 사용했습니다. 많은 실제 산업 응용 분야에서 실험 연구는 작동 매개 변수를 조절하기 어렵 기 때문에 제한적이지만 주로 설정을 개발하거나 실험을 실행하는 데 드는 비용이 높기 때문입니다. 

따라서 솔루션은 주요 시스템 응답을 시뮬레이션하고 예측할 수 있는 효과적인 수학적 모델의 개발에 의존합니다. DoE와 같은 최적화 방법론을 수치 모델과 결합하면 비용이 많이 들고 시간이 많이 걸리는 실험을 피하고 다양한 입력 조합을 사용하여 최적의 조건을 얻을 수 있습니다 [ 16 ]. 

실바와 루 보아 [ 18] CFD 프레임 워크 하에서 개발 된 2D Eulerian-Eulerian 바이오 매스 가스화 모델에서 얻은 결과를 RSM과 결합하여 다양한 응용 분야에서 합성 가스를 생성하기 위한 최적의 작동 조건을 찾습니다. 

저자는 입력 요인으로 인한 최상의 응답과 최소한의 변동을 모두 보장하는 작동 조건을 찾을 수 있었습니다. Frawley et al. [ 19 ] CFD 및 DoE 기술 (특히 RSM)을 결합하여 파이프의 팔꿈치에서 고체 입자 침식에 대한 다양한 주요 요인의 영향을 조사하여 침식 예측 모델을 개발할 수 있습니다.우리가 아는 한, DoD 잉크젯 프로세스의 개선 및 더 나은 이해에 적용되는 DoE 접근법 (실험적으로 또는 모든 종류의 수치 모델과 결합)을 구현하는 연구는 없습니다. 선도 기업이 이러한 접근 방식을 적용 할 가능성이 있지만 관련 결과는 민감할 수 있으므로 더 넓은 커뮤니티에서 사용할 수 없습니다. 이 사실은 DoD 잉크젯 공정에서 액적 생성에 대한 여러 매개 변수의 영향을 평가하기 위한 이러한 종류의 연구로서 현재 논문의 영향을 증가 시킬 수 있습니다.

CFD 프레임 워크 내에서 VOF 접근 방식을 사용하여 여러 컴퓨터 실험의 설계를 개발하고 RSM을 분석 도구로 사용했습니다. 충분한 수치 정확도와 수용 가능한 시간 계산 시뮬레이션의 균형을 맞추기 위해 메쉬 수렴 연구가 수행되었습니다. 설계 목적을 위해 점도, 표면 장력, 입구 속도 및 노즐 직경이 입력 요인으로 선택되었습니다. 응답은 break-up 시간과 break-up 길이였습니다.

Figure 1. Schematic of the computational domain
Figure 1. Schematic of the computational domain
Figure 2. Ink fraction contours for mesh 1 through 4 (left to right) at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs.
Figure 2. Ink fraction contours for mesh 1 through 4 (left to right) at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs.
Figure 3. Comparison between surface tensions at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 3. Comparison between surface tensions at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 4. Comparison between viscosity values at the following four time steps: (a) 6 μs, (b) 12 μs, (c) 18 μs, and (d) 24 μs.
Figure 4. Comparison between viscosity values at the following four time steps: (a) 6 μs, (b) 12 μs, (c) 18 μs, and (d) 24 μs.
Figure 5. Comparison between different nozzle diameters at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 5. Comparison between different nozzle diameters at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 6. Comparison between different inlet velocities at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 6. Comparison between different inlet velocities at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs
Figure 8. Contour response plots for break-up time as a function of (a) surface tension and viscosity, (b) nozzle diameter and viscosity, (c) inlet velocity and viscosity, (d) nozzle diameter and surface tension, (e) inlet velocity and surface tension, and (f) inlet velocity and nozzle diameter.
Figure 8. Contour response plots for break-up time as a function of (a) surface tension and viscosity, (b) nozzle diameter and viscosity, (c) inlet velocity and viscosity, (d) nozzle diameter and surface tension, (e) inlet velocity and surface tension, and (f) inlet velocity and nozzle diameter.
Figure 12. Break-up length as a function of the We–Ca space (obtained from the 25 runs).
Figure 12. Break-up length as a function of the We–Ca space (obtained from the 25 runs).

References

  1. Hutchings, I.M.; Martin, G.D. Inkjet Technology for Digital Fabrication; John Wiley & Sons Ltd.: Hoboken, NJ,
    USA, 2013.
  2. Waasdorp, R.; Heuvel, O.; Versluis, F.; Hajee, B.; GhatKesar, M. Acessing individual 75-micron diameter
    nozzles of a desktop inkjet printer to dispense picoliter droplets on demand. RSC Adv. 2018, 8, 14765.
  3. Zhang, H.; Wang, J.; Lu, G. Numerical investigation of the influence of companion drops on drop-ondemand ink jetting. Appl. Phys. Eng. 2012, 13, 584–595.
  4. Dong, H.; Carr, W. An experimental study of drop-on-demand drop formation. Phys. Fluids 2006, 18,
    072102.
  5. Patel, M.; Pericleous, K.; Cross, M. Numerical Modelling of Circulating Fluidized beds. Int. J. Comput.
  6. Fluid Dyn. 1993, 1, 161–176. [CrossRef]
  7. Zhao, X.; Glenn, C.; Xiao, Z.; Zhang, S. CFD development for macro particle simulations. Int. J. Comput.
  8. Fluid Dyn. 2014, 28, 232–249. [CrossRef]
  9. Hasan, M.N.; Chandy, A.; Choi, J.W. Numerical analysis of post-impact droplet deformation for direct-print.
  10. Eng. Appl. Comput. Fluid Mech. 2015, 9, 543–555. [CrossRef]
  11. Ghafouri-Azar, R.; Mostaghimi, J.; Chandra, S. Numerical study of impact and solidification of a droplet
  12. over a deposited frozen splat. Int. J. Comput. Fluid Dyn. 2004, 18, 133–138. [CrossRef]
  13. Feng, J. A General Fluid Dynamic Analysis of Drop Ejection in Drop-on-Demand Ink Jet Devices. J. Imaging
  14. Sci. Technol. 2002, 46, 398–408.
  15. Fromm, J. Numerical Calculation of the Fluid Dynamics of Drop-on-Demand Jets. IBM J. Res. Dev. 1984, 28,
  16. 322–333. [CrossRef]
  17. Nallan, H.; Sadie, J.; Kitsomboonloha, R.; Volkman, S.; Subramanian, V. Systematic Design of Jettable
  18. Nanoparticle-Based Inkjet Inks: Rheology, Acoustics and Jettability. Langmuir 2014, 30, 13470–13477.
  19. [CrossRef] [PubMed]
  20. Reis, N.; Derby, B. Ink Jet Deposition of Ceramic Suspensions: Modelling and Experiments of Droplet Formation;
  21. Chapter in MRS Online Proceeding Library Archive; Cambridge University Press: Cambridge, UK, 2000;
  22. Volume 624, pp. 117–122.
  23. Jang, D.; Kim, D.; Moon, J. Influence of Fluid Physical Properties on Ink-Jet Printability. Langmuir 2009, 25,
  24. 2629–2635. [CrossRef] [PubMed]
  25. Tai, J.; Gan, H.Y.; Liang, Y.N.; Lok, B.K. Control of Droplet Formation in Inkjet Printing Using Ohnesorge
  26. Number Category: Materials and Processes. In Proceedings of the 10th Electronics Packaging Technology
  27. Conference, EPTC, Singapore, 9–12 December 2008; pp. 761–766.
  28. Box, G.; Wilson, K. On the Experimental Attainment of Optimum Conditions. J. R. Stat. Soc. Ser. B 1951, 13,
  29. 1–45.
  30. Silva, V.; Rouboa, A. Optimizing the gasification operating conditions of forest residues by coupling a
  31. two-stage equilibrium model with a response surface methodology. Fuel Process. Technol. 2014, 122, 163–169.
  32. [CrossRef]
  33. Silva, V.; Rouboa, A. Optimizing the DMFC Operating Conditions using a Response Surface Method.
  34. Appl. Math. Comput. 2012, 218, 6733–6743. [CrossRef]
  35. Silva, V.; Rouboa, A. Combining a 2-D multiphase CFD model with a Response Surface Methodology to
  36. optimize the gasification of Portuguese biomasses. Energy Convers. Manag. 2015, 99, 28–40. [CrossRef]
  37. Frawley, P.; Corish, J.; Niven, A.; Geron, M. Combination of CFD and DOE to analyse solid particle erosion
  38. in elbows. Int. J. Comput. Fluid Dyn. 2009, 23, 411–426. [CrossRef]
  39. Morrison, N.F.; Harlen, O.G. Viscoelasticity in inkjet printing. Rheol. Acta 2010, 49, 619–632. [CrossRef]
  40. ANSYS Inc. ANSYS Fluent Tutorial Guide; Release 15.0; ANSYS Inc.: Canonsburg, PA, USA, November 2013.
  41. ANSYS Inc. ANSYS Fluent Theory Guide; Release 17.0; ANSYS Inc.: Canonsburg, PA, USA, January 2016.
  42. Dinsenmeyer, R.; Fourmigué, J.F.; Caney, N.; Marty, P. Volume of fluid approach of boiling flows in
  43. concentrated solar plants. Int. J. Heat Fluid Flow 2017, 65, 177–191. [CrossRef]
  44. Das, S.; Weerasiri, L.D.; Yang, W. Influence of surface tension on bubble nucleation, formation and onset of
  45. sliding. Colloids Surf. A Physicochem. Eng. Asp. 2017, 516, 23–31. [CrossRef]
  46. Du, W.; Zhang, J.; Lu, P.; Xu, J.; Wei, W.; He, G.; Zhang, L. Advanced understanding of local wetting
  47. behaviour in gas-liquid-solid packed beds using CFD with a volume of fluid (VOF) method. Chem. Eng. Sci.
  48. 2017, 170, 378–392. [CrossRef]
  49. Shrestha, S.; Chou, K. A build surface study of Powder-Bed electron beam additive manufacturing by
  50. 3D thermo-fluid simulation and white-light interferometry. Int. J. Mach. Tools Manuf. 2017, 121, 37–49.
  51. [CrossRef]
  52. Zhong, Y.; Fang, H.; Ma, Q.; Dong, X. Analysis of droplet stability after ejection from an inkjet nozzle. J. Fluid
  53. Mech. 2018, 845, 378–391. [CrossRef]
  54. Zhang, X. Dynamics of drop formation in viscous flows. Chem. Eng. Sci. 1999, 54, 1759–1774. [CrossRef]
  55. Calvert, P. Inkjet printing for materials and devices. Chem. Mater. 2001, 13, 3299–3305. [CrossRef]
  56. Kim, C.S.; Park, S.; Sim, W.; Kim, Y.; Yoo, Y. Modelling and characterization of an industrial inkjet head for
  57. micro-patterning on printed circuit boards. Comput. Fluids 2009, 38, 602–612. [CrossRef]
  58. ChemEngineering 2018, 2, 51 19 of 19
  59. Wang, P. Numerical Analysis of Droplet Formation and Transport of a Highly Viscous Liquid. Master’s Thesis,
  60. University of Kentucky, Lexington, KY, USA, 2014.
  61. Zhang, Z.; Xiong, R.; Corr, D.; Huang, Y. Study of Impingement Types and Printing Quality during Laser
  62. Printing of Viscoelastic Alginate Solutions. Langmuir 2016, 32, 3004–3014. [CrossRef] [PubMed]
  63. Derby, B. Inkjet Printing Ceramics: From Drops to Solid. J. Eur. Ceram. Soc. 2011, 31, 2543–2550. [CrossRef]
  64. Kim, E.; Baek, J. Numerical Study on the Effects of Non Dimensional Parameters on Drop-on-Demand
  65. Droplet Formation Dynamics and Printability Range in the up-Scaled Model. Phys. Fluids 2012, 24, 082103.
  66. [CrossRef]
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

Result of simulation by changing surface tension

잉크젯 프린팅에서 해상력에 관한 컴퓨터 시뮬레이션 연구

A Study on the Simulation of the Resolution for Ink-Jet Printing

  • Lee, Ji-Eun (Dept. of Graphic Arts Engineering, Graduate School, Pukyong National University) ;
  • Youn, Jong-Tae (Dept. of Graphic Arts Information, College of Engineering, Pukyong National University) ;
  • Koo, Chul-Whoi (Dept. of Graphic Arts Information, College of Engineering, Pukyong National University)
  • 이지은 (부경대학교 대학원 인쇄공학과) ;
  • 윤종태 (부경대학교 공과대학 인쇄정보공학과) ;
  • 구철회 (부경대학교 공과대학 인쇄정보공학과)

초록

Ink-jet is part of the non impact printing that shooting the ink drop from the nozzle to paper. It is very silence and express good color. There are two types of printing that continuous and drop on demand. But drop on demand process is becoming the mainstream. these days, LCD, PDP is passed more than semiconductor industry. And we expect organic EL, FED as a next display. But product equipment, main component and technology have a gap between an advanced country and us nevertheless physical development. Expecially, previous process part is depended on imports. Ink-jet printing technology that there isn’t complicated photo lithography process is attracted, so ink-jet printing resolution is more embossed. But there were not many of ink-jet resolution thesis but ink-jet head or nozzle. Because, to out of the ink from the nozzle is unseeable and hard to experiment. Therefore this thesis was experimented and simulated how can ink-jet printer improved resolution by flow-3d simulation package program.

잉크젯은 노즐에서 종이로 잉크 방울을 분사하는 비 충격 인쇄의 일부입니다. 매우 조용하고 좋은 색상을 표현합니다. 연속 및 요청시 드롭되는 두 가지 유형의 인쇄가 있습니다. 그러나 주문형 드롭 프로세스가 주류가되고 있습니다. 요즘 LCD, PDP는 반도체 산업을 넘어서고 있습니다. 그리고 우리는 유기 EL, FED를 다음 디스플레이로 기대합니다. 그러나 제품 장비, 주요 부품 및 기술은 선진국과 우리의 물리적 발달 사이에 격차가 있습니다. 특히 이전 공정 부분은 수입품에 의존합니다. 복잡한 포토 리소그래피 공정이없는 잉크젯 프린팅 기술이 매료되어 잉크젯 프린팅 해상도가 더욱 강조됩니다. 하지만 잉크젯 해상도 논문은 많지 않고 잉크젯 헤드 나 노즐이 많았습니다. 왜냐하면 노즐에서 잉크가 빠져 나가는 것은 보이지 않고 실험하기 어렵 기 때문입니다. 따라서이 논문은 flow-3d 시뮬레이션 패키지 프로그램을 통해 잉크젯 프린터가 해상도를 향상시킬 수있는 방법을 실험하고 시뮬레이션했습니다.

국내 및 해외에 다양한 인쇄 기술이 보급되어 있는 상황에서 잉크젯 기술은 1990년대 후반부터 궤도에 올랐다. 잉크젯은 비접촉성 인쇄 기술의 하나로 인쇄 표면에 잉크 방울 들을 투사해 전자적으로 조정하기 때문에 여러 가지 장점들이 있다. 원하는 양을 원하는 때 제작 가능하고 2,400dpi이상의 높은 해상도를 가지며 잉크 방울의 크기를 조절하여 보다 정확한 이미지인 그레이 스케일 이미지를 얻을 수 있다. 따라서 사진과 같은 이미 지를 만들 수 있다. 또한 기존의 붓을 이용한 디자인에 비해 높은 해상도의 이미지를 손 쉽게 만들 수 있으므로 그래픽 디자인에 대한 적용 범위를 확장할 수 있다. 그리고 카트 리지에 저장되어 있는 잉크를 이미지에 필요한 양만큼 소비하기 때문에 생산비 절감에 유리하다. 이는 코팅 기술이 가지고 있는 원료의 소모를 획기적으로 개선할 수 있다.또 한 코팅 방법과는 달리 기판에 영향을 주지 않는다. 거칠거나 민감한 모든 종류의 표면 위에 인쇄가 가능하며, 1분당 100,000라인의 인쇄 속도로 고속 처리에 적합하다. 현재 잉 크젯 프린터의 성능을 평가하는 방법 중에 가장 기본적인 것은 해상도이다. 그렇기 때문 에 인쇄물의 해상도에서는 dpi가 무척 중요하다. dpi는 dot per inch의 약자로 1인치당 찍은 점의 수이다. dpi는 인쇄물의 해상력을 결정하는 단위이다. 예를 들어 300dpi는 1인 치에 300개의 점을 찍는 밀도로 잉크 점을 찍어 인쇄를 한다는 뜻이다. 당연히 dpi는 숫 자가 클수록 인쇄물이 더 정교해진다. 그러나 제조업체에 따라 출력 dpi 수가 다르며 요 구되는 최적의 해상도도 프린터 엔진의 특성에 따라 다르다. 일반적인 인쇄물은 200dpi 면 좋은 품질이며, 300dpi를 넘으면 매우 우수한 품질이 된다. 우리가 일상생활에서 보 는 대부분의 인쇄물은 100~300dpi 정도롤 사용한다. 잉크젯 프린터에 1,440dpi라고 쓰여 있는 것은 dot의 실질적인 것을 말하는 것이 아니라, 이상적인 종이에 잉크 방울을 려 구현할 수 있는 이론상의 수치이다. 종이에 작은 잉크 입자돌을 뿌려 번지게 하는 방법 으로 인해, 표시된 해상력만큼 재현하지 못하는 경우가 많다. 따라서 실제로는 600dpi 잉크젯 프린터라고 해도 인쇄소에서 300dpi로 출력한 것보다 품질이 떨어지기도 한다. 그러므로 좋은 품질을 얻기 위해서는 목표로 한 해상력 보다 높게 인쇄해야 하는데 그 러기 위해서는 잉크젯의 해상력에 관한 연구가 필수적이다. 잉크에서는 주로 헤드와 노즐에 관한 연구들이 많이 있지만,~9 본 논문에서는 잉크젯의 해상력에 관한 연구를 하고자 한다. 본 연구의 목적은 FLOW-3D 시뮬레이션 프로그램을 이용하여 액적의 비산 모양을 시뮬레이션 함으로서 해상력에 대한 예측을 하기 위한 것이다. 잉크 방울의 크기가 해상 력에 미친다는 것을 알고, 잉크의 물성을 변화시켜가며 액적을 줄이기 위한 시뮬레이션 을 하였다.

Simulation of the bubble jet printing by FLOW-3D
ZSimulation of the bubble jet printing by FLOW-3D
Result of simulation by changing surface tension
Result of simulation by changing surface tension

자유 표면 모델링 방법

본 자료는 국내 사용자들의 편의를 위해 원문 번역을 해서 제공하기 때문에 일부 오역이 있을 수 있어서 원문과 함께 수록합니다. 자료를 이용하실 때 참고하시기 바랍니다.

Free Surface Modeling Methods

An interface between a gas and liquid is often referred to as a free surface. The reason for the “free” designation arises from the large difference in the densities of the gas and liquid (e.g., the ratio of density for water to air is 1000). A low gas density means that its inertia can generally be ignored compared to that of the liquid. In this sense the liquid moves independently, or freely, with respect to the gas. The only influence of the gas is the pressure it exerts on the liquid surface. In other words, the gas-liquid surface is not constrained, but free.

자유 표면 모델링 방법

기체와 액체 사이의 계면은 종종 자유 표면이라고합니다.  ‘자유’라는 호칭이 된 것은 기체와 액체의 밀도가 크게 다르기 때문입니다 (예를 들어, 물 공기에 대한 밀도 비는 1000입니다).  기체의 밀도가 낮다는 것은 액체의 관성에 비해 기체의 관성은 일반적으로 무시할 수 있다는 것을 의미합니다.  이러한 의미에서, 액체는 기체에 대해 독립적으로, 즉 자유롭게 움직입니다.  기체의 유일한 효과는 액체의 표면에 대한 압력입니다.  즉, 기체와 액체의 표면은 제약되어있는 것이 아니라 자유롭다는 것입니다.

In heat-transfer texts the term ‘Stephen Problem’ is often used to describe free boundary problems. In this case, however, the boundaries are phase boundaries, e.g., the boundary between ice and water that changes in response to the heat supplied from convective fluid currents.

열전달에 관한 문서는 자유 경계 문제를 묘사할 때 “Stephen Problem’”라는 용어가 자주 사용됩니다.  그러나 여기에서 경계는 상(phase) 경계, 즉 대류적인 유체의 흐름에 의해 공급된 열에 반응하여 변화하는 얼음과 물 사이의 경계 등을 말합니다.

Whatever the name, it should be obvious that the presence of a free or moving boundary introduces serious complications for any type of analysis. For all but the simplest of problems, it is necessary to resort to numerical solutions. Even then, free surfaces require the introduction of special methods to define their location, their movement, and their influence on a flow.

이름이 무엇이든, 자유 또는 이동 경계가 존재한다는 것은 어떤 유형의 분석에도 복잡한 문제를 야기한다는 것은 분명합니다. 가장 간단한 문제를 제외한 모든 문제에 대해서는 수치 해석에 의존할 필요가 있습니다. 그 경우에도 자유 표면은 위치, 이동 및 흐름에 미치는 영향을 정의하기 위한 특별한 방법이 필요합니다.

In the following discussion we will briefly review the types of numerical approaches that have been used to model free surfaces, indicating the advantages and disadvantages of each method. Regardless of the method employed, there are three essential features needed to properly model free surfaces:

  1. A scheme is needed to describe the shape and location of a surface,
  2. An algorithm is required to evolve the shape and location with time, and
  3. Free-surface boundary conditions must be applied at the surface.

다음 설명에서는 자유 표면 모델링에 사용되어 온 다양한 유형의 수치적 접근에 대해 간략하게 검토하고 각 방법의 장단점을 설명합니다. 어떤 방법을 사용하는지에 관계없이 자유롭게 표면을 적절히 모델화하는 다음의 3 가지 기능이 필요합니다.

  1. 표면의 형상과 위치를 설명하는 방식
  2. 시간에 따라 모양과 위치를 업데이트 하는 알고리즘
  3. 표면에 적용할 자유 표면 경계 조건

Lagrangian Grid Methods

Conceptually, the simplest means of defining and tracking a free surface is to construct a Lagrangian grid that is imbedded in and moves with the fluid. Many finite-element methods use this approach. Because the grid and fluid move together, the grid automatically tracks free surfaces.

라그랑주 격자 법

개념적으로 자유 표면을 정의하고 추적하는 가장 간단한 방법은 유체와 함께 이동하는 라그랑주 격자를 구성하는 것입니다. 많은 유한 요소 방법이 이 접근 방식을 사용합니다. 격자와 유체가 함께 움직이기 때문에 격자는 자동으로 자유 표면을 추적합니다.

At a surface it is necessary to modify the approximating equations to include the proper boundary conditions and to account for the fact that fluid exists only on one side of the boundary. If this is not done, asymmetries develop that eventually destroy the accuracy of a simulation.

표면에서 적절한 경계 조건을 포함하고 유체가 경계의 한면에만 존재한다는 사실을 설명하기 위해 근사 방정식을 수정해야합니다. 이것이 수행되지 않으면 결국 시뮬레이션의 정확도를 훼손하는 비대칭이 발생합니다.

The principal limitation of Lagrangian methods is that they cannot track surfaces that break apart or intersect. Even large amplitude surface motions can be difficult to track without introducing regridding techniques such as the Arbitrary-Lagrangian-Eulerian (ALE) method. References 1970 and 1974 may be consulted for early examples of these approaches.

라그랑지안 방법의 주요 제한은 분리되거나 교차하는 표면을 추적 할 수 없다는 것입니다. ALE (Arbitrary-Lagrangian-Eulerian) 방법과 같은 격자 재생성 기법을 도입하지 않으면 진폭이 큰 표면 움직임도 추적하기 어려울 수 있습니다. 이러한 접근법의 초기 예를 보려면 참고 문헌 1970 및 1974를 참조하십시오.

The remaining free-surface methods discussed here use a fixed, Eulerian grid as the basis for computations so that more complicated surface motions may be treated.

여기에서 논의된 나머지 자유 표면 방법은 보다 복잡한 표면 움직임을 처리할 수 있도록 고정된 오일러 그리드를 계산의 기준으로 사용합니다.

Surface Height Method

Low amplitude sloshing, shallow water waves, and other free-surface motions in which the surface does not deviate too far from horizontal, can be described by the height, H, of the surface relative to some reference elevation. Time evolution of the height is governed by the kinematic equation, where (u,v,w) are fluid velocities in the (x,y,z) directions. This equation is a mathematical expression of the fact that the surface must move with the fluid:

표면 높이 법

낮은 진폭의 슬로 싱, 얕은 물결 및 표면이 수평에서 너무 멀리 벗어나지 않는 기타 자유 표면 운동은 일부 기준 고도에 대한 표면의 높이 H로 설명 할 수 있습니다. 높이의 시간 진화는 운동학 방정식에 의해 제어되며, 여기서 (u, v, w)는 (x, y, z) 방향의 유체 속도입니다. 이 방정식은 표면이 유체와 함께 움직여야한다는 사실을 수학적으로 표현한 것입니다.

Finite-difference approximations to this equation are easy to implement. Further, only the height values at a set of horizontal locations must be recorded so the memory requirements for a three-dimensional numerical solution are extremely small. Finally, the application of free-surface boundary conditions is also simplified by the condition on the surface that it remains nearly horizontal. Examples of this technique can be found in References 1971 and 1975.

이 방정식의 유한 차분 근사를 쉽게 실행할 수 있습니다.  또한 3 차원 수치 해법의 메모리 요구 사항이 극도로 작아지도록 같은 높이의 위치 값만을 기록해야합니다.  마지막으로 자유 표면 경계 조건의 적용도 거의 수평을 유지하는 표면의 조건에 의해 간소화됩니다.  이 방법의 예는 참고 문헌의 1971 및 1975을 참조하십시오.

Marker-and-Cell (MAC) Method

The earliest numerical method devised for time-dependent, free-surface, flow problems was the Marker-and-Cell (MAC) method (see Ref. 1965). This scheme is based on a fixed, Eulerian grid of control volumes. The location of fluid within the grid is determined by a set of marker particles that move with the fluid, but otherwise have no volume, mass or other properties.

MAC 방법

시간 의존성을 가지는 자유 표면 흐름의 문제에 대해 처음 고안된 수치 법이 MAC (Marker-and-Cell) 법입니다 (참고 문헌 1965 참조).  이 구조는 컨트롤 볼륨 고정 오일러 격자를 기반으로합니다.  격자 내의 유체의 위치는 유체와 함께 움직이고, 그 이외는 부피, 질량, 기타 특성을 갖지 않는 일련의 마커 입자에 의해 결정됩니다.

Grid cells containing markers are considered occupied by fluid, while those without markers are empty (or void). A free surface is defined to exist in any grid cell that contains particles and that also has at least one neighboring grid cell that is void. The location and orientation of the surface within the cell was not part of the original MAC method.

마커를 포함한 격자 셀은 유체로 채워져있는 것으로 간주되며 마커가 없는 격자 셀은 빈(무효)것입니다.  입자를 포함하고, 적어도 하나의 인접 격자 셀이 무효인 격자의 자유 표면은 존재하는 것으로 정의됩니다.  셀 표면의 위치와 방향은 원래의 MAC 법에 포함되지 않았습니다.

Evolution of surfaces was computed by moving the markers with locally interpolated fluid velocities. Some special treatments were required to define the fluid properties in newly filled grid cells and to cancel values in cells that are emptied.

표면의 발전(개선)은 국소적으로 보간된 유체 속도로 마커를 이동하여 계산되었습니다.  새롭게 충전된 격자 셀의 유체 특성을 정의하거나 비어있는 셀의 값을 취소하거나 하려면 특별한 처리가 필요했습니다.

The application of free-surface boundary conditions consisted of assigning the gas pressure to all surface cells. Also, velocity components were assigned to all locations on or immediately outside the surface in such a way as to approximate conditions of incompressibility and zero-surface shear stress.

자유 표면 경계 조건의 적용은 모든 표면 셀에 가스 압력을 할당하는 것으로 구성되었습니다. 또한 속도 성분은 비압축성 및 제로 표면 전단 응력의 조건을 근사화하는 방식으로 표면 위 또는 외부의 모든 위치에 할당되었습니다.

The extraordinary success of the MAC method in solving a wide range of complicated free-surface flow problems is well documented in numerous publications. One reason for this success is that the markers do not track surfaces directly, but instead track fluid volumes. Surfaces are simply the boundaries of the volumes, and in this sense surfaces may appear, merge or disappear as volumes break apart or coalesce.

폭넓게 복잡한 자유 표면 흐름 문제 해결에 MAC 법이 놀라운 성공을 거두고 있는 것은 수많은 문헌에서 충분히 입증되고 있습니다.  이 성공 이유 중 하나는 마커가 표면을 직접 추적하는 것이 아니라 유체의 체적을 추적하는 것입니다.  표면은 체적의 경계에 불과하며, 그러한 의미에서 표면은 분할 또는 합체된 부피로 출현(appear), 병합, 소멸 할 가능성이 있습니다.

A variety of improvements have contributed to an increase in the accuracy and applicability of the original MAC method. For example, applying gas pressures at interpolated surface locations within cells improves the accuracy in problems driven by hydrostatic forces, while the inclusion of surface tension forces extends the method to a wider class of problems (see Refs. 1969, 1975).

다양한 개선으로 인해 원래 MAC 방법의 정확성과 적용 가능성이 증가했습니다. 예를 들어, 셀 내 보간 된 표면 위치에 가스 압력을 적용하면 정 수력으로 인한 문제의 정확도가 향상되는 반면 표면 장력의 포함은 방법을 더 광범위한 문제로 확장합니다 (참조 문헌. 1969, 1975).

In spite of its successes, the MAC method has been used primarily for two-dimensional simulations because it requires considerable memory and CPU time to accommodate the necessary number of marker particles. Typically, an average of about 16 markers in each grid cell is needed to ensure an accurate tracking of surfaces undergoing large deformations.

수많은 성공에도 불구하고 MAC 방법은 필요한 수의 마커 입자를 수용하기 위해 상당한 메모리와 CPU 시간이 필요하기 때문에 주로 2 차원 시뮬레이션에 사용되었습니다. 일반적으로 큰 변형을 겪는 표면의 정확한 추적을 보장하려면 각 그리드 셀에 평균 약 16 개의 마커가 필요합니다.

Another limitation of marker particles is that they don’t do a very good job of following flow processes in regions involving converging/diverging flows. Markers are usually interpreted as tracking the centroids of small fluid elements. However, when those fluid elements get pulled into long convoluted strands, the markers may no longer be good indicators of the fluid configuration. This can be seen, for example, at flow stagnation points where markers pile up in one direction, but are drawn apart in a perpendicular direction. If they are pulled apart enough (i.e., further than one grid cell width) unphysical voids may develop in the flow.

마커 입자의 또 다른 한계는 수렴 / 발산 흐름이 포함된 영역에서 흐름 프로세스를 따라가는 작업을 잘 수행하지 못한다는 것입니다. 마커는 일반적으로 작은 유체 요소의 중심을 추적하는 것으로 해석됩니다. 그러나 이러한 유체 요소가 길고 복잡한 가닥으로 당겨지면 마커가 더 이상 유체 구성의 좋은 지표가 될 수 없습니다. 예를 들어 마커가 한 방향으로 쌓여 있지만 수직 방향으로 떨어져 있는 흐름 정체 지점에서 볼 수 있습니다. 충분히 분리되면 (즉, 하나의 그리드 셀 너비 이상) 비 물리적 공극이 흐름에서 발생할 수 있습니다.

Surface Marker Method

One way to limit the memory and CPU time consumption of markers is to keep marker particles only on surfaces and not in the interior of fluid regions. Of course, this removes the volume tracking property of the MAC method and requires additional logic to determine when and how surfaces break apart or coalesce.

표면 마커 법

마커의 메모리 및 CPU 시간의 소비를 제한하는 방법 중 하나는 마커 입자를 유체 영역의 내부가 아니라 표면에만 보존하는 것입니다.  물론 이는 MAC 법의 체적 추적 특성이 배제되기 때문에 표면이 분할 또는 합체하는 방식과 시기를 특정하기위한 논리를 추가해야합니다.

In two dimensions the marker particles on a surface can be arranged in a linear order along the surface. This arrangement introduces several advantages, such as being able to maintain a uniform particle spacing and simplifying the computation of intersections between different surfaces. Surface markers also provide a convenient way to locate the surface within a grid cell for the application of boundary conditions.

2 차원의 경우 표면 마커 입자는 표면을 따라 선형으로 배치 할 수 있습니다.  이 배열은 입자의 간격을 균일하게 유지할 수있는 별도의 표면이 교차하는 부분의 계산이 쉽다는 등 몇 가지 장점이 있습니다.  또한 표면 마커를 사용하여 경계 조건을 적용하면 격자 셀의 표면을 간단한 방법으로 찾을 수 있습니다.

Unfortunately, in three-dimensions there is no simple way to order particles on surfaces, and this leads to a major failing of the surface marker technique. Regions may exist where surfaces are expanding and no markers fill the space. Without markers the configuration of the surface is unknown, consequently there is no way to add markers. Reference 1975 contains examples that show the advantages and limitations of this method.

불행히도 3 차원에서는 표면에 입자를 정렬하는 간단한 방법이 없으며 이로 인해 표면 마커 기술이 크게 실패합니다. 표면이 확장되고 마커가 공간을 채우지 않는 영역이 존재할 수 있습니다. 마커가 없으면 표면의 구성을 알 수 없으므로 마커를 추가 할 방법이 없습니다.
참고 문헌 1975이 방법의 장점과 한계를 보여주는 예제가 포함되어 있습니다.

Volume-of-Fluid (VOF) Method

The last method to be discussed is based on the concept of a fluid volume fraction. The idea for this approach originated as a way to have the powerful volume-tracking feature of the MAC method without its large memory and CPU costs.

VOF (Volume-of-Fluid) 법

마지막으로 설명하는 방법은 유체 부피 분율의 개념을 기반으로합니다. 이 접근 방식에 대한 아이디어는 대용량 메모리 및 CPU 비용없이 MAC 방식의 강력한 볼륨 추적 기능을 갖는 방법에서 시작되었습니다.

Within each grid cell (control volume) it is customary to retain only one value for each flow quantity (e.g., pressure, velocity, temperature, etc.) For this reason it makes little sense to retain more information for locating a free surface. Following this reasoning, the use of a single quantity, the fluid volume fraction in each grid cell, is consistent with the resolution of the other flow quantities.

각 격자 셀 (제어 체적) 내에서 각 유량 (예 : 압력, 속도, 온도 등)에 대해 하나의 값만 유지하는 것이 일반적입니다. 이러한 이유로 자유 표면을 찾기 위해 더 많은 정보를 유지하는 것은 거의 의미가 없습니다. 이러한 추론에 따라 각 격자 셀의 유체 부피 분율인 단일 수량의 사용은 다른 유량의 해상도와 일치합니다.

If we know the amount of fluid in each cell it is possible to locate surfaces, as well as determine surface slopes and surface curvatures. Surfaces are easy to locate because they lie in cells partially filled with fluid or between cells full of fluid and cells that have no fluid.

각 셀 내의 유체의 양을 알고 있는 경우, 표면의 위치 뿐만 아니라  표면 경사와 표면 곡률을 결정하는 것이 가능합니다.  표면은 유체 가 부분 충전 된 셀 또는 유체가 전체에 충전 된 셀과 유체가 전혀없는 셀 사이에 존재하기 때문에 쉽게 찾을 수 있습니다.

Slopes and curvatures are computed by using the fluid volume fractions in neighboring cells. It is essential to remember that the volume fraction should be a step function, i.e., having a value of either one or zero. Knowing this, the volume fractions in neighboring cells can then be used to locate the position of fluid (and its slope and curvature) within a particular cell.

경사와 곡률은 인접 셀의 유체 체적 점유율을 사용하여 계산됩니다.  체적 점유율은 계단 함수(step function)이어야 합니다, 즉, 값이 1 또는 0 인 것을 기억하는 것이 중요합니다.  이 것을 안다면, 인접 셀의 부피 점유율을 사용하여 특정 셀 내의 유체의 위치 (및 그 경사와 곡률)을 찾을 수 있습니다.

Free-surface boundary conditions must be applied as in the MAC method, i.e., assigning the proper gas pressure (plus equivalent surface tension pressure) as well as determining what velocity components outside the surface should be used to satisfy a zero shear-stress condition at the surface. In practice, it is sometimes simpler to assign velocity gradients instead of velocity components at surfaces.

자유 표면 경계 조건을 MAC 법과 동일하게 적용해야 합니다.  즉, 적절한 기체 압력 (및 대응하는 표면 장력)을 할당하고, 또한 표면에서 제로 전단 응력을 충족 시키려면 표면 외부의 어떤 속도 성분을 사용할 필요가 있는지를 확인합니다.  사실, 표면에서의 속도 성분 대신 속도 구배를 지정하는 것이보다 쉬울 수 있습니다.

Finally, to compute the time evolution of surfaces, a technique is needed to move volume fractions through a grid in such a way that the step-function nature of the distribution is retained. The basic kinematic equation for fluid fractions is similar to that for the height-function method, where F is the fraction of fluid function:

마지막으로, 표면의 시간 변화를 계산하려면 분포의 계단 함수의 성질이 유지되는 방법으로 격자를 통과하고 부피 점유율을 이동하는 방법이 필요합니다.  유체 점유율의 기본적인 운동학방정식은 높이 함수(height-function) 법과 유사합니다.  F는 유체 점유율 함수입니다.

A straightforward numerical approximation cannot be used to model this equation because numerical diffusion and dispersion errors destroy the sharp, step-function nature of the F distribution.

이 방정식을 모델링 할 때 간단한 수치 근사는 사용할 수 없습니다.  수치의 확산과 분산 오류는 F 분포의 명확한 계단 함수(step-function)의 성질이 손상되기 때문입니다.

It is easy to accurately model the solution to this equation in one dimension such that the F distribution retains its zero or one values. Imagine fluid is filling a column of cells from bottom to top. At some instant the fluid interface is in the middle region of a cell whose neighbor below is filled and whose neighbor above is empty. The fluid orientation in the neighboring cells means the interface must be located above the bottom of the cell by an amount equal to the fluid fraction in the cell. Then the computation of how much fluid to move into the empty cell above can be modified to first allow the empty region of the surface-containing cell to fill before transmitting fluid on to the next cell.

F 분포가 0 또는 1의 값을 유지하는 같은 1 차원에서이 방정식의 해를 정확하게 모델링하는 것은 간단합니다.  1 열의 셀에 위에서 아래까지 유체가 충전되는 경우를 상상해보십시오.  어느 순간에 액체 계면은 셀의 중간 영역에 있고, 그 아래쪽의 인접 셀은 충전되어 있고, 상단 인접 셀은 비어 있습니다.  인접 셀 내의 유체의 방향은 계면과 셀의 하단과의 거리가 셀 내의 유체 점유율과 같아야 한다는 것을 의미합니다.  그 다음 먼저 표면을 포함하는 셀의 빈 공간을 충전 한 후 다음 셀로 유체를 보내도록 위쪽의 빈 셀에 이동하는 유체의 양의 계산을 변경할 수 있습니다.

In two or three dimensions a similar procedure of using information from neighboring cells can be used, but it is not possible to be as accurate as in the one-dimensional case. The problem with more than one dimension is that an exact determination of the shape and location of the surface cannot be made. Nevertheless, this technique can be made to work well as evidenced by the large number of successful applications that have been completed using the VOF method. References 1975, 1980, and 1981 should be consulted for the original work on this technique.

2 차원과 3 차원에서 인접 셀의 정보를 사용하는 유사한 절차를 사용할 수 있지만, 1 차원의 경우만큼 정확하게 하는 것은 불가능합니다.  2 차원 이상의 경우의 문제는 표면의 모양과 위치를 정확히 알 수없는 것입니다.  그래도 VOF 법을 사용하여 달성 된 다수의 성공 사례에서 알 수 있듯이 이 방법을 잘 작동시킬 수 있습니다.  이 기법에 관한 초기의 연구 내용은 참고 문헌 1975,1980,1981를 참조하십시오.

The VOF method has lived up to its goal of providing a method that is as powerful as the MAC method without the overhead of that method. Its use of volume tracking as opposed to surface-tracking function means that it is robust enough to handle the breakup and coalescence of fluid masses. Further, because it uses a continuous function it does not suffer from the lack of divisibility that discrete particles exhibit.

VOF 법은 MAC 법만큼 강력한 기술을 오버 헤드없이 제공한다는 목표를 달성 해 왔습니다.  표면 추적이 아닌 부피 추적 기능을 사용하는 것은 유체 질량의 분할과 합체를 처리하는 데 충분한 내구성을 가지고 있다는 것을 의미합니다.  또한 연속 함수를 사용하기 때문에 이산된 입자에서 발생하는 숫자를 나눌 수 없는 문제를 겪지 않게 됩니다.

Variable-Density Approximation to the VOF Method

One feature of the VOF method that requires special treatment is the application of boundary conditions. As a surface moves through a grid, the cells containing fluid continually change, which means that the solution region is also changing. At the free boundaries of this changing region the proper free surface stress conditions must also be applied.

VOF 법의 가변 밀도 근사

VOF 법의 특수 처리가 필요한 기능 중 하나는 경계 조건의 적용입니다.  표면이 격자를 통과하여 이동할 때 유체를 포함하는 셀은 끊임없이 변화합니다.  즉, 계산 영역도 변화하고 있다는 것입니다.  이 변화하고있는 영역의 자유 경계에는 적절한 자유 표면 응력 조건도 적용해야합니다.

Updating the flow region and applying boundary conditions is not a trivial task. For this reason some approximations to the VOF method have been used in which flow is computed in both liquid and gas regions. Typically, this is done by treating the flow as a single fluid having a variable density. The F function is used to define the density. An argument is then made that because the flow equations are solved in both liquid and gas regions there is no need to set interfacial boundary conditions.

유체 영역의 업데이트 및 경계 조건의 적용은 중요한 작업입니다.  따라서 액체와 기체의 두 영역에서 흐름이 계산되는 VOF 법에 약간의 근사가 사용되어 왔습니다.  일반적으로 가변 밀도를 가진 단일 유체로 흐름을 처리함으로써 이루어집니다.  밀도를 정의하려면 F 함수를 사용합니다.  그리고, 흐름 방정식은 액체와 기체의 두 영역에서 계산되기 때문에 계면의 경계 조건을 설정할 필요가 없다는 논증이 이루어집니다.

Unfortunately, this approach does not work very well in practice for two reasons. First, the sensitivity of a gas region to pressure changes is generally much greater than that in liquid regions. This makes it difficult to achieve convergence in the coupled pressure-velocity solution. Sometimes very large CPU times are required with this technique.

공교롭게도 이 방법은 두 가지 이유로 인해 실제로는 그다지 잘 작동하지 않습니다.  하나는 압력의 변화에 대한 기체 영역의 감도가 일반적으로 액체 영역보다 훨씬 큰 것입니다.  따라서 압력 – 속도 결합 해법 수렴을 달성하는 것은 어렵습니다.  이 기술은 필요한 CPU 시간이 매우 커질 수 있습니다.

The second, and more significant, reason is associated with the possibility of a tangential velocity discontinuity at interfaces. Because of their different responses to pressure, gas and liquid velocities at an interface are usually quite different. In the Variable-Density model interfaces are moved with an average velocity, but this often leads to unrealistic movement of the interfaces.

두 번째 더 중요한 이유는 계면에서 접선 속도가 불연속이되는 가능성에 관련이 있습니다.  압력에 대한 반응이 다르기 때문에 계면에서 기체와 액체의 속도는 일반적으로 크게 다릅니다.  가변 밀도 모델은 계면은 평균 속도로 동작하지만, 이는 계면의 움직임이 비현실적으로 되는 경우가 많습니다.

Even though the Variable-Density method is sometimes referred to as a VOF method, because is uses a fraction-of-fluid function, this designation is incorrect. For accurately tracking sharp liquid-gas interfaces it is necessary to actually treat the interface as a discontinuity. This means it is necessary to have a technique to define an interface discontinuity, as well as a way to impose the proper boundary conditions at that interface. It is also necessary to use a special numerical method to track interface motions though a grid without destroying its character as a discontinuity.

가변 밀도 방법은 유체 분율 함수를 사용하기 때문에 VOF 방법이라고도하지만 이것은 올바르지 않습니다. 날카로운 액체-가스 인터페이스를 정확하게 추적하려면 인터페이스를 실제로 불연속으로 처리해야합니다. 즉, 인터페이스 불연속성을 정의하는 기술과 해당 인터페이스에서 적절한 경계 조건을 적용하는 방법이 필요합니다. 또한 불연속성으로 특성을 훼손하지 않고 격자를 통해 인터페이스 동작을 추적하기 위해 특수한 수치 방법을 사용해야합니다.

Summary

A brief discussion of the various techniques used to numerically model free surfaces has been given here with some comments about their relative advantages and disadvantages. Readers should not be surprised to learn that there have been numerous variations of these basic techniques proposed over the years. Probably the most successful of the methods is the VOF technique because of its simplicity and robustness. It is this method, with some refinement, that is used in the FLOW-3D program.

여기에서는 자유 표면을 수치적으로 모델링 할 때 사용하는 다양한 방법에 대해 상대적인 장점과 단점에 대한 설명을 포함하여 쉽게 설명하였습니다.  오랜 세월에 걸쳐 이러한 기본적인 방법이 많이 제안되어 온 것을 알고도 독자 여러분은 놀라지 않을 것입니다.  아마도 가장 성과를 거둔 방법은 간결하고 강력한 VOF 법 입니다.  이 방법에 일부 개량을 더한 것이 현재 FLOW-3D 프로그램에서 사용되고 있습니다.

Attempts to improve the VOF method have centered on better, more accurate, ways to move fluid fractions through a grid. Other developments have attempted to apply the method in connection with body-fitted grids and to employ more than one fluid fraction function in order to model more than one fluid component. A discussion of these developments is beyond the scope of this introduction.

VOF 법의 개선은 더 나은, 더 정확한 방법으로 유체 점유율을 격자를 통과하여 이동하는 것에 중점을 두어 왔습니다.  기타 개발은 물체 적합 격자(body-fitted grids) 관련 기법을 적용하거나 여러 유체 성분을 모델링하기 위해 여러 유체 점유율 함수를 채용하기도 했습니다.  이러한 개발에 대한 논의는 여기에서의 설명 범위를 벗어납니다.

References

1965 Harlow, F.H. and Welch, J.E., Numerical Calculation of Time-Dependent Viscous Incompressible Flow, Phys. Fluids 8, 2182.

1969 Daly, B.J., Numerical Study of the Effect of Surface Tension on Interface Instability, Phys. Fluids 12, 1340.

1970 Hirt, C.W., Cook, J.L. and Butler, T.D., A Lagrangian Method for Calculating the Dynamics of an Incompressible Fluid with Free Surface, J. Comp. Phys. 5, 103.

1971 Nichols, B.D. and Hirt, C.W.,Calculating Three-Dimensional Free Surface Flows in the Vicinity of Submerged and Exposed Structures, J. Comp. Phys. 12, 234.

1974 Hirt, C.W., Amsden, A.A., and Cook, J.L.,An Arbitrary Lagrangian-Eulerian Computing Method for all Flow Speeds, J. Comp. Phys., 14, 227.

1975 Nichols, B.D. and Hirt, C.W., Methods for Calculating Multidimensional, Transient Free Surface Flows Past Bodies, Proc. of the First International Conf. On Num. Ship Hydrodynamics, Gaithersburg, ML, Oct. 20-23.

1980 Nichols, B.D. and Hirt, C.W., Numerical Simulation of BWR Vent-Clearing Hydrodynamics, Nucl. Sci. Eng. 73, 196.

1981 Hirt, C.W. and Nichols, B.D., Volume of Fluid (VOF) Method for the Dynamics of Free Boundaries, J. Comp. Phys. 39, 201.

Thermocapillary Actuation

Thermocapillary Actuation

표면 장력의 온도 의존성은 유체 방울을 패턴 있는 표면 위로 전달하는 데 사용될 수 있습니다. 표면은 유체 방울이 친수성-수소성 인터페이스에 의해 형성된 채널을 따르도록 제한되도록 친수성 또는 친수성 접촉 각도로 패턴화됩니다. 또한 프로그램 가능한 방식으로 가열된 마이크로 히터의 배열은 열전압 작동을 유발하여 유체 방울을 뜨거운 영역에서 차가운 지역으로 유도합니다. 아래 이미지는 문제 설정의 상단 및 단면 뷰(Anton A)를 보여줍니다. Darhuber 외.) 다음에 Flow-3D를 설정합니다.

Liquid droplet moving along hydrophilic microstripe
Top-view of a liquid droplet moving along a hydrophilic microstripe. The array of Ti-resistors (shown in light gray) beneath the hydrophilic stripes locally heat the droplet thereby modifying the surface tension and propelling the liquid toward the colder regions of the device surface. The dark gray stripes represent the leads and contacts (Au) for the heating resistors.
Cross sectional view of device
Cross-sectional view of a portion of the device containing two micro-heaters and an overlying droplet.

더 차가운 표면 온도 영역은 인접한 따뜻한 지점보다 더 높은 표면 장력을 유지하여 액체를 당기는 접선 표면 힘을가합니다. 부분적 습윤 (접촉각> 0) 표면은 전체 습윤 표면 (접촉각 = 0)에 비해 부피 손실이 적은 유체 수송을 허용하기 때문에 바람직한 옵션입니다.

FLOW-3D setup of three microheaters

Top view of the setup in FLOW-3D showing three microheaters in pink, yellow and blue respectively. The central hydrophilic strip is shown in black with a fluid (water) droplet in sky blue.

아래 애니메이션은 완전히 젖은 표면과 부분적으로 젖은 표면의 비교를 보여줍니다. 예상대로 완전히 젖은 표면은 부분적으로 젖은 표면보다 액적을 더 평평하게 (그리고 더 많이 퍼지게) 만듭니다. 히터가 한 번에 하나씩 활성화되면 물방울이 더 차가운 영역으로 이동됩니다. 더 많은 유체가 남겨질수록 시뮬레이션이 끝날 때까지 완전히 젖은 표면은 더 많은 유체 볼륨을 잃는 것을 볼 수 있습니다. 따라서 부분적으로 젖은 표면은 유체 손실을 줄이기위한 더 바람직한 옵션입니다. 두 경우 모두 소수성 표면으로 둘러싸인 중앙 친수성 스트립으로 인해 물방울이 중앙에 머물러야합니다.

Animation of the results post-processed in FlowSight.

References

Anton A. Darhuber, Joseph P. Valentino, Sandra M. Trian and Sigurd Wagner, Thermocapillary Actuation of Droplets on Chemically Patterned Surfaces by Programmable Microheater Arrays, Journal of Microelectrochemical Systems, Vol. 12, No. 6, December 2003

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

Air Entrainment(공기혼입) Analysis

일부 자유 표면 유동에서 난류 또는 특정 유동조건으로 인해 자유 표면에 가스(Air)가 혼입될 수 있습니다. 그러므로 유동 해석시 가스(Air) 혼입에 대한 고려를 해야합니다.

공기혼입의 예시

  • 댐 수문게이트
  • 정화장치 부문
  • Dam aerated flow region(댐 공기 유동 영역) etc.

Air entrainment physical processes(공기 혼입 물리 프로세스)

  • Entrained air transprot(혼입 공기 수송 모델)
    : 혼입계수(The Entrainment rate coefficient)는 0.5가 적합
    : 표면장력(The surface tension) 고려
  • Bulking : Variable density(가변 밀도 모델)
    : 유입 유체의 밀도 조정은 유체 및 공기 밀도의 조합을 설명하기 위해 자동으로 계산됩니다. 결과적으로, 체적 유량은 유체 및 혼입 된 공기 혼합물의 총 체적 유량이며, 경계(Boundary)에서 공기의 농도를 정의 할 때 사용자가 고려해야합니다.
  • Turbulence model(RANS, RNG etc.)
    : 공기 혼입(Air entrainment) 모델을 사용할 때 적절한 난류모델을 고려해야 합니다. 난류 모델에 대한 설명은 아래 링크를 참조하시길 바랍니다.

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  • Buoyancy : Variable density + Drift-Flux(부력의 효과)
    : 부력(Buoyancy force) 효과를 고려하면 드리프트 플럭스 모델(Drift-Flux model)과의 상호 작용을 설명 할 수 있습니다. 이 경우, 기포는 밀도의 차이로 인해 유체 내에서 이동할 수 있으며 유체 운동에 영향을 줄 수 있습니다.

공기 혼입 모델(Air entrainement model) 해석 사례

Laser Welding and Additive Manufacturing

Application

  • Shallow penetration weld (Shallow 침투 용접)
  • Deep penetration weld (Deep 침투 용접)
  • Laser-arc hybrid welding(레이저-아크 하이브리드 용접)
  • Laser repair technology
  • Laser cladding(레이저 클레딩)
  • Laser powder bed fusion process

관련 물리 모델

  • Viscous Flows and Turbulence(점성 유체 및 난류 모델)
  • Surface Tension(표면장력)
  • General Moving Objects(GMO)
  • Heat Transfer(열전달)
  • Visco-elasto-plasticity(점탄성)
  • Solidification(응고)
  • Thermal Stresses(열응력)

Laser/Heat source(레이저/열원)

  • 레이저 출력 및 용접 속도 향상
    – 더 큰 키홀(Keyhole) 개방 및 깊이 변동이 적음
    – 후면 용융 풀 (Moltan Pool)의 난기류가 최소화된 키홀(Keyhole) 앞부분 벽(Wall)에 레이저 빔(Laser beam)이 노출
    – 다공성 형성(Porosity formation) 최소화

Laser beam motion(레이저 빔 모션)

  • 레이저 빔(Laser beam) 기울기 증가
    – 큰 각도에서 유사한 방향을 따라 작용하는 중력 및 반동 압력으로 인해 후면 용융 풀(Moltan pool)에서 층류(Laminar flow)가 관찰
    – 다공성 발생(Porosity occurrence) 최소화

해석 사례

  • Laser metal deposition(레이저 금속 증착) -Single layer
  • 40마이크론 유체 입자 주입 (500,000/sec)
  • 레이저 출력 : 100W
  • 스캔속도 : 1cm/sec
  • 레이저 빔 직경 : 2mm
  • 재질 : IN-718 meterail alloy
  • Laser metal deposition(레이저 금속 증착) – Multilayer
  • Laser powder bed fusion process
  • FLOW-3D DEM 및 FLOW-3D WELD 고려
    – 용융 영역(Melt region)
    – 용융 풀(Melt pool)의 속도 및 온도
    – 고체 영역(Solid fraction)
  • 레이저 방사(Laser irradiation) 조건
    – 출력 : 200W
    – 스캔속도 : 3m/s
    – Spot radius : 0.1mm

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 5월 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

Aluminum Integral Foam Molding Process

Aluminum Integral Foam Molding Process

This application note was contributed by Johannes Hartmann and Vera Jüchter, Department of Materials Science, Chair of Metals Science and Technology, University of Erlangen-Nuremberg

 

알루미늄 폼은 우수한 댐핑 및 높은 에너지 흡수율 및 굴곡 강성과 같은 예외적인 특성을 보여줍니다[1]. 강성은 특히 하중 지지 및 경량 구조에 사용하기에 특히 매력적입니다. 중량별 강성을 높이고 보다 우수한 하중 전달을 위해 알 Aluminum Foam Sandwiches (AFS)와 같은 컴팩트한 특성이 필요합니다 [2].

Erlangen-Nuremberg 대학의 금속 공학과 기술 위원장은 알루미늄 발포 특성을 점차적으로 생산하기 위해 다이캐스팅 공정인 Integral Foam Molding 개발하였습니다(그림 1 참조). 이 공정은 폴리머의 사출 성형으로 개발되었으며 따라서 컴팩트한 층을 가진 복잡한 폼을 비용 효율적으로 대량 생산에 적합합니다. 이 노트에 설명 된 시뮬레이션 기법은 프로세스 매개 변수를 선택하는데 도움을 주기 위한 모델링프로세스를 확인할 수 있습니다.

Figure 1. Cross section of an aluminum integral foam with a compact skin, a transition region with decreasing relative density and smaller pores, as well as a foamed core.

Aluminum Integral Foam Molding Technology

일정량의 발포제 (수소화 마그네슘, MgH2)가 러너 시스템에 배치되고 샷 챔버는 알루미늄 용융물로 채워진다 (공정은 그림 2에 묘사되어 있으며, 공정은 [3]에 자세히 설명되어있다). 피스톤이 진행됨에 따라, 분말은 난류 방식으로 주형에 이송된다. 기술 변형 “고압 일체형 폼 몰딩 (HP-IFM)”의 경우 표준 다이캐스팅 공정에서 알 수 있듯이 이 부품은 주변의 높은 압력에서 완전히 채워져 우수한 표면 품질을 보장합니다. 템퍼링된 금형 표면에서 시작하여 용융물은 일체형으로 고형화되기 시작합니다. 몇 밀리 초가 지나면 금형은 코어 풀러 시스템 위에 열리고 부피는 국부적으로 증가하고 압력은 감소하여 열분해 및 수소화 마그네슘 입자의 수소 방출로 인해 여전히 반고체 내부 영역에서 기공 성장을 시작합니다. 모든 발포제 입자는 이웃하는 공극의 역압에 의해 멈추어 질 때까지 공극의 성장을 지속합니다. 발포된 입자의 벽은 알루미늄 합금의 응고된 입자에 의해 안정화가 되며 이를 endogenous stabilization이라고 합니다[4].

Figure 2. Schematic process cycle of “High Pressure Integral Foam Molding (HP-IFM)” of aluminum.

주조 부품의 전체 부피에서 균일한 형태에 대한 전제조건은 분해 순간의 양호한 입자분포입니다. 또한, 발포제 유입시의 용융물의 온도는 수소화 마그네슘의 분해를 결정하며 (그림 3 참조), 게다가 발포시 solid phase의 양을 결정한다. 그러나 고상의 양이 너무 많으면 기공의 강성이 증가하고 현상 기공의 구형화를 방해하여 구조가 파괴된다 [2].

Microcellular Aluminum Integral Foams – Approaching the Process Limits

일체형 발포 성형 공정시뮬레이션은 새로운 부품 설계의 몰드 충진 특성을 조사하는 데 도움이 될 뿐만 아니라 입자 침투도 예측하고 비용을 절약할 수 있게 발포 공정 조건을 결정할 수 있는 강력한 도구입니다. 현재 연구의 목표는 다공성 수준을 일정하게 유지하면서 기공 크기를 줄이는 것입니다. 전산 유체 역학 (CFD) 시뮬레이션은 가능한 한 현재의 프로세스 한계에 가깝게 접근할 수 있습니다. 발포 형태의 개선은 기계적 물성에서 균질 한 구조를 유도 할뿐만 아니라 기계적 성질에 의해 더 얇은 부품의 생산이 가능할 것입니다. 이 목적은 용융물 내에서의 높은 입자 분포 밀도와 동시에 응집 현상의 감소와 함께 완전히 안정된 기공 성장에 의해서만 달성 될 수 있다.

Figure 3. Schematic curves of decomposition of magnesium hydride as a function of the melt temperature, calculated by the Johnson-Mehl-Avrami approach [2]

Figure 4. Adjustment of heat transfer by comparisons of a real solidification curve (black) to the growth rate of the solidified skin in simulation (red).

Adapting the Simulation Parameters to Practical Integral Foam Molding Experiments

입자 거동이나 온도장에 대한 신뢰성 있는 예측을 위한 CFD 시뮬레이션을 사용할 수 있으려면 실제 실험과 일치하도록 매개 변수를 결정해야 합니다. 이를 위해, 30-130 ms의 지연 시간을 갖는 일체형 발포 부품을 제작하였으며 성형 팽창 및 기공 성장 개시 순간에 고상분율 때문에 발포 형성이 불가능한 다른 밀도의 형상을 만들었습니다. 열 전달 계수 (완전한 액체 용융물과 완전 응고된 용융물)를 변화시켜 합금 AlSi9Cu3 (Fe)의 주조 사이클을 시뮬레이션하면 응고 곡선을 적용할 수 있습니다. 이러한 목표를 달성하기 위해 시뮬레이션을 피스톤 이동이 시작되기 전에 실제 온도분포를 묘사해야 합니다. 온도는 배치된 열에 의해 숏 챔버에서 국부적으로 측정되었으며 시뮬레이션 내 실제 데이터와 잘 일치하여 성공적으로 묘사 될 수 있었습니다. 금형 충진 중에 금형 표면에서 온도 측정을 참조 할 수도 있습니다. 시간 경과에 따른 그 변화는 시뮬레이션 결과와 잘 일치합니다.

표면장력이나 응고 항력계수와 같은 용융의 유동을 정의하는 추가 매개 변수 단계에서는 다른 설정과 시뮬레이션을 비교하여 조정됩니다. 시뮬레이션 내에서 용융물의 흐름이 실제 시험과 일치하는 즉시 매개 변수가 설정됩니다

Figure 5. Adjustment of melt flow defining parameters such as the surface tension by comparisons of real experiments (left) to simulations (right)

냉각 및 용해 흐름 특성을 정의한 후 입자의 유입을 시뮬레이션 합니다. 입자 / 유체 의 상호 작용에 대한 시뮬레이션을 조정하기 위해 매개 변수계수의 X 선 샘플과 비교가 되며 구리선 입자에서는 수산화 마그네슘보다 높은 함량 입자가 적용됩니다. (그림 6 참조). 시뮬레이션 결과는 실험과 매우 잘 어울리므로 프로세스 매개 변수의 함수로서 입자 분포의 신뢰할 수 있습니다.

Figure 6. Adjustment of parameters influencing particle/melt-interactions by comparisons of x-rayed samples left); produced by the entrainment of copper particles) to simulations (right)

Conclusion

전체적으로 FLOW-3D는 실제 생산 전에 새로운 부품 제조의 잠재적 결함을 조사하는 중요한 수단이 될 수 있다는 것을 증명할 수 있었습니다. 이러한 방식으로, 차가운 흐름 또는 데드 존이 없는 성공적인 충전 및 발포제 분포가 보장 될 수 있다. 또한, 예상되는 온도 필드의 정확한 묘사로, 수소화 마그네슘의 분해 특성 및 기공형성을 예측할 수 있습니다. 이는 일체형 폼 구조와 관련하여 고객의 요구를 충족시키기 위한 공정 변수를 정의 할 수 있는 가능성을 제공합니다

1 Criterion is the solid phase fraction where the shear strength and therefore the resistance to pore evolution increases drastically.

References

[1] C. Körner, R. F. Singer, Adv. Eng. Mater. 20002 (4), pp. 159-165.
[2] C. Körner, in Integral Foam Molding of Light Metals – Technology, Foam Physics and Foam Simulation, Springer, Berlin, Heidelberg, Germany 2008.
[3] H. Wiehler, C. Körner, R. F. Singer, Adv. Eng. Mater. 200810 (3), pp. 171-178.
[4] J. Hartmann, A. Trepper, C. Körner, Adv. Eng. Mater. 201113 (11), pp. 1050-1055.

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D Cast>

 

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.

FLOW-3D CAST Suites

FLOW-3D CAST Suites

FLOW-3D CAST v5 comes in Suites of relevant casting processes: 

HIGH PRESSURE DIE CASTING SUITE

Process Workspace

High Pressure Die Casting

Features

Thermal Die Cycling
– Cooling/heating channels
– Spray cooling
Filling
– Shot sleeve with Plunger
– Shot motion
– Ladles, stoppers
– Venting efficiency
– PQ^2 analysis
– HPDC machine database
Solidification
– Squeeze pins
Cooling


PERMANENT MOLD CASTING SUITE

Process Workspaces

Permanent Mold Casting
Low Pressure Die Casting
Tilt Pour Casting

Features

Thermal Die Cycling
– Cooling/heating channels
Filling
– Tilt pouring
Solidification
– Squeeze pins
Cooling


SAND CASTING SUITE

Process Workspaces

Sand Casting
Low Pressure Sand Casting

Features

Filling
– Permeable molds
– Moisture evaporation in molds
– Gas generation in cores
– Ladle model
Solidification
– Exothermic sleeves
– Chills
– Cast iron solidification
Cooling


LOST FOAM CASTING SUITE

Process Workspaces

Lost Foam
Sand Casting
Low Pressure Sand Casting

Features

Filling
– Permeable molds
– Moisture evaporation in molds
– Gas generation in cores
– Ladle model
– Lost foam pattern evaporation models (Fast model and Full model)
– Lost foam defect prediction
Solidification
– Exothermic sleeves
– Chills
– Cast iron solidification
Cooling

 


ALL SUITES INCLUDE THESE CORE FEATURES:

Solver Engine

  • TruVOF – The most accurate filling simulation tool in the industry
  • Heat transfer and solidification
  • Shrinkage – Rapid Shrinkage model and Shrinkage with flow model
  • Temperature dependent properties
  • Multi-block meshing including conforming meshes
  • Turbulence models
  • Non-Newtonian viscosity (shear thinning/thickening, thixotropic)
  • Flow tracers
  • Active Simulation Control with Global Conditions
  • Surface tension model
  • Thermal stress analysis with warpage
  • General moving geometry w/6 DOF

FlowSight

  • Multi-case analysis
  • Porosity analysis tool

Defect Prediction Tools

  • Gas entrainment model
  • Thermal Modulus output
  • Hot Spot identification
  • Micro and macro porosity prediction
  • Surface defect prediction
  • Shrinkage
  • Cavitation and Cavitation Potential
  • Particle models (Inclusion modeling, collapsed bubble tracking)

User Conveniences

  • Process-oriented workspaces
  • Configurable Simulation Monitor
  • Metal and solid material databases
  • Heat transfer database
  • Filter database
  • Remote solving queues
  • Quick Analyze/Display tool

Computational Analysis of Drop Formation and Detachment

Computational Analysis of Drop Formation and Detachment

Introduction and Problem Statement

신속, 반복, 작은 물방울의 생성 및 증착, 작은 형상의 프린팅 또는 패터닝 (예 : l = 10-3-1 mm), 스프레이로  균일한 두께의 박막 형성은 다양한 산업에 매우 중요합니다(1-5). 액체 이동과 액적 형성 / 증착 공정은 복잡한 자유 표면 흐름, 자연적인 모세관운동 형성, thinning, pinch-off를 수반한다 (1-5). 단순한 뉴턴 및 비탄성 유체에 대해 액적 생성 및 액적 이동을 분석하기위한 실험적, 이론적 및 1 차원 시뮬레이션 연구가 진행되었지만 프린팅 또는 패터닝에 대한 기계론적인 이해는 여전히 과제로 남아 있습니다. 현재의 계산에 대한 주된 목표는 뉴턴 유체의 pinch-off에 대한 기계론적 이해를 얻기 위해 FLOW-3D에 내장된 VOF(volume-of-fluid) 접근법으로 시험하는 것입니다. 전산해석은 모세관, 관성, 점성 응력의 복잡한 상호 작용을 포착하여 자기유사 모세관의 thinning and pinch-off를 결정합니다. 뉴턴 유체의 물방울 형성 ​​및 분리현상은  전산해석으로부터 얻어진 자기유사 모세관현상 이론, 보편적인 축소화 기법인 1D 시뮬레이션 (1-7)과 실험 (1, 2, 8-12)을 이용하여 설명될 수 있음을 보여준다. 이러한 우리가 진행한 원형흐름 시뮬레이션은 유한한 시간의 비선형 역학, 위성 낙하현상, 복잡한 형상의 프린팅과 같이 어려운 전산해석의 기반이 될 것 입니다.

방울 형성의 전산 분석
그림 1 : FLOW-3D를 사용하여 시뮬레이션 한 저점도 유체의 드롭 형성 및 분리에 대한 전산해석 : (a) 5개의 저점도 유체에 대한 물방울의 necking에 대한 반경이 시간변화에 따라 표시됩니다. 물방울 necking의 반지름이 오른쪽에서 왼쪽으로 시간에 따른 전개를 보여줍니다. 마찬가지로 스냅 샷은 necking의 반경이 오른쪽에서 왼쪽으로 줄어듭니다. 속도의 크기 (단위 : cm/s) 와 화살표의 방향에 대한 컬러 맵을 사용하면 변형장을 결정할 수 있으며 Fluid 5 (표 1 참조)의 경우에는 순식간에 신장이됩니다. 이미지 II에 캡처 된 pinch-off 하기 전에 형성된 원추형 necking은 실험을 통해 얻은 necking 모양과 유사합니다.

Modeling Approach and Parameter Space

표면 장력 및 중력 모델을 적용한 FLOW-3D 에서 균일한 메쉬 크기를 사용하여 노즐에서 드롭 형성 및 분리에 대한 시뮬레이션을 수행하였습니다. 유한 체적의 유체를 떨어뜨리거나 분리하는 일은 물방울의 성장과 드롭, 노즐에 연결되는 모세관 현상, 관성, 점도 및 중력에 대한 상호 작용을 수반합니다. 시뮬레이션에서 스테인레스 강 노즐 ( {{D} _ {0}} = 2 {{R} _ {0}} = 1.7 \, \ text {mm}) 에서 유한 체적의 뉴턴 유체가 발생합니다. 표면 장력이 중력을 겪으면 새로 형성된 액적 분리가 발생합니다 (mg> 2 \ pi \ sigma {{R} _ {0}}). 시뮬레이션은 유체점도의 영향을 설명하기 위해 두 그룹으로 나누어져 있습니다: 저점도 유체 (글리세롤 함량이 40 % 미만인 물과 글리세롤/물 혼합물) 및 점도가 높은 유체 (예 : 글리세롤과 글리세롤/물 혼합물 점도 > 100x 물 점도). 두 그룹의 유체 특성은 각각 표 1과 2에 나와 있습니다.

계산 분석 드롭 형성 저점도

그림 2 : FLOW-3D를 사용하여 시뮬레이션 한 저점도 유체의 드롭형성 및 분리에 대한 전산 해석 : 반경 플롯에서 4개의 고점도 뉴톤유체에 대해 necking 반경을 시간변화에 따라 표시합니다. 낙하 분리 중 모세관 현상이 스냅 샷으로 표시됩니다. 컬러 맵은 Fluid 8의 속도 크기 (단위 : cm/s)의 변화를 포착합니다 (표2 참조). 화살표는 성장하는 물방울과 얇아지는 물방울내에서 흐름방향을 나타냅니다. FLOW-3D 시뮬레이션으로 얻은 necking 모양은 고점도의 뉴턴유체에 대한 특징인 원통형 유체요소로 이어집니다.

 

<표 1 : FLOW-3D를 사용하여 시뮬레이션 된 저점도 유체의 특성>
Fluid Property Fluid 1 Fluid 2 Fluid 3 Fluid 4 Fluid 5
Viscosity [Pa · s] 0.05 0.02 0.01 0.0075 0.005
Surface Tension  [mN / m] 68 68 68 68 68
Density [g / cm 3 ] 1 1 1 1 1
Ohnesorge Number 0.21 0.08 0.04 0.03 0.021
 저점도 유체 (표 1의 유체 2) 가 노즐에서 떨어지는 것을 시뮬레이션 합니다. 색상변수는 속도크기 (단위 : cm / s)이며 속도벡터가 표시됩니다.

 

<표 2 : FLOW-3D를 사용하여 시뮬레이션 된 고점도 유체의 특성>
Fluid Property Fluid 6 Fluid 7 Fluid 8 Fluid 9
Viscosity [Pa · s] 1.5 0.8 0.5 0.25
Surface Tension  [mN / m ] 68 68 68 68
Density [g / cm 3 ] 1 1 1 1
Ohnesorge Number 6.24 3.33 2.08 1.04

고점도 유체 (표 2의 유체 8) 가 노즐에서 떨어지는 것을 시뮬레이션 합니다. 색상변수는 속도크기 (단위 : cm / s) 이며 속도 벡터가 표시됩니다.

Discussion of the Simulation Results

드롭 형성 및 분리는 표1과 표2에 열거 된 유체에 대해 FLOW-3D 를 사용하여 시뮬레이션 하였고, 시간 경과에 따른 necking 모양, 반경을 분석하였습니다. 물방울의 necking 모양과 저점도에서의 necking에 대한 역학(그림 1 참조)은 실험, 흐름 이론, 1D 시뮬레이션, 자기유사 관성에 대한 모세현상의 특성을 나타냅니다 (1, 2, 6, 7, 13) :

(1)  \ displaystyle \ frac {{R (t)}} {{{{R} _ {0}}}} \ approx 0.8 R {{{{왼쪽} {R} {0} 3}}} 오른쪽}) ^ {{{{frac {1} {3}}} {{왼쪽 {{{{왼쪽}}} {2} {3}}}}

여기서 R (t)가  necking의 순간 반경이고, R0는 노즐의 외부반경이며,  \ displaystyle \ sigma 는 표면 장력,  \ displaystyle \ rho 는 유체의 밀도 tC 는 pinch-off 시간이다. 마찬가지로, 이러한 더 높은 점도의 뉴턴유체에 대한 반경 변화데이터는 시간에 따른 반경의 감소를 나타내는 것이며,  Papageorgiou’s visco-capillary scaling (8, 9)은 아래의 식으로 표현된다.

(2)  \ {0 \} {} {} {} {} {} {} {} {} {} {} {} {} { } ({{t} _ {p}} - t)

모세관 속도(표면 장력과 점도의 비)의 측정 값은 McKinley와 Tripathi (8)에 의해 Capillary Break-Up Extensional Rheometer (CaBER)라고 불리는 상업적으로 이용 가능한 장비를 사용하여 얻은 값과 모세관 속도는 공칭 표면 장력과 점도를 사용하여 계산됩니다.

FLOW-3D 는 물방울의 necking부분을 속도 벡터로 시각화하여 유체의 흐름을 나타낼 수 있습니다. 또한, 이는 그림 1과 같이 전단, 확장을 겪은 후 얇아지는 물방울이 흐르는 과정의 순간을 결정할 수 있는 가능성을 줍니다. 추가로, 낮은 점도의 뉴턴유체는 높은 점도의 뉴턴 유체에 비해 질적으로 다른 거동을 보여준다(그림 2참조). 낮은 점도의 뉴턴 유체에 대한 necking 프로파일은 이론(6,13)에 따라 자기 유사성이 됩니다.

Conclusions, Outlook and Ongoing work

우리의 예비결과는 FLOW-3D 기반의 전산해석이 액적 형성과 탈착의 기초가 되는 프로토타입의 자유 표면흐름을 시뮬레이션하는데 사용될 수 있음을 보여줍니다 . 시뮬레이션된 반경변화 프로파일이 실험적으로 관찰된 높은 유체 및 이론적으로 예측된 유체인 스케일링 법칙 및 pinch-off dynamics과 일치하는 것을 발견하였습니다.

자주 사용되는 1D 또는 2D 모델과 달리 FLOW-3D 는 기본 응력 및 확장 유동장 (균일도 및 크기)의 강도와 얇은 액체 필라멘트 내 흐름에 대한 시각화를 나타낼 수 있습니다(그림1과 2 참조). 확장 유동장과 연관된 흐름 방향 속도 구배는 모세관현상이 나타나는 물방울의 얇은 부분 내에서 발생합니다. 유동학적으로 복잡한 유체에서 non Newtonian shear 및 신장, 점도뿐만 아니라 그외의 탄성 응력이 nonlinear pinch-off dynamics을 급격하게 변화시킵니다(2, 10-12). 우리는 현재 점탄성과 non-Newtonian 유동학을 사용하여 FLow-3D에 복합 유체의 처리 성능평가를 위한 강력한 연산 프로토콜을 개발하고 있습니다.

References

  1. J. Eggers, Nonlinear dynamics and breakup of free-surface flows. Rev. Mod. Phys. 69, 865-929 (1997).
  2. G. H. McKinley, Visco-elasto-capillary thinning and break-up of complex fluids. Rheology Reviews, 1-48 (2005).
  3. B. Derby, Inkjet Printing of Functional and Structural Materials: Fluid Property Requirements, Feature Stability, and Resolution. Annual Review of Materials Research 40, 395-414 (2010).
  4. O. A. Basaran, H. Gao, P. P. Bhat, Nonstandard Inkjets. Annual Review of Fluid Mechanics 45, 85-113 (2013).
  5. S. Kumar, Liquid Transfer in Printing Processes: Liquid Bridges with Moving Contact Lines. Annual Review of Fluid Mechanics 47, 67-94 (2014).
  6. R. F. Day, E. J. Hinch, J. R. Lister, Self-similar capillary pinchoff of an inviscid fluid. Phys. Rev. Lett. 80, 704-707 (1998).
  7. J. Eggers, M. A. Fontelos, Singularities: Formation, Structure, and Propagation. (Cambridge University Press, Cambridge, UK, 2015), vol. 53.
  8. G. H. McKinley, A. Tripathi, How to extract the Newtonian viscosity from capillary breakup measurements in a filament rheometer. J. Rheol. 44, 653-670 (2000).
  9. D. T. Papageorgiou, On the breakup of viscous liquid threads. Phys. Fluids 7, 1529-1544 (1995).
  10. J. Dinic, L. N. Jimenez, V. Sharma, Pinch-off dynamics and dripping-onto-substrate (DoS) rheometry of complex fluids. Lab on a Chip 17, 460-473 (2017).
  11. J. Dinic, Y. Zhang, L. N. Jimenez, V. Sharma, Extensional Relaxation Times of Dilute, Aqueous Polymer Solutions. ACS Macro Letters 4, 804-808 (2015).
  12. V. Sharma et al., The rheology of aqueous solutions of Ethyl Hydroxy-Ethyl Cellulose (EHEC) and its hydrophobically modified Analogue (hmEHEC): Extensional flow response in capillary break-up, jetting (ROJER) and in a cross-slot extensional rheometer. Soft Matter 11, 3251-3270 (2015).
  13. J. R. Castrejón-Pita et al., Plethora of transitions during breakup of liquid filaments. Proc. Natl. Acad. Sci. U.S.A. 112, 4582-4587 (2015).

[FLOW-3D 물리모델] Surface Tension / 표면 장력

Surface Tension / 표면 장력

표면장력은 기체와 액체 사이 또는 두 섞이지 않는 액체 사이에서 뚜렷한 경계면에 접한 평면에 작용하는 힘이다. 이 힘은 두 물질 사이 분자간 힘의 차이에 의해 발생한다. FLOW-3D 에서 표면장력은 1 또는 2유체의 유동에서 모델링 될 수 있으며 항상 General Interface tracking 에서 활성화된 Free surface or sharp interface 모델과 함께 사용 되어야 한다.

이 모델은 Physics Surface tensionActivate surface tension model 를 체크함으로써 활성화된다. 표면장력 계수는(Surface tension coefficient, SIGMA) 또는 물성치 트리에서 지정된다: Fluids Surface Tension Surface Tension Coefficient. 여기서 정의되는 전반적 Contact angle은 경계면이 고체벽 경계와 고체요소를 만날 때 습윤거동을 조절한다. 접촉각 변수는 0.0(완전습윤)과 1.0(완전 비습윤) 사이의 값을 취한다. 추가로 각 요소의 개별적 접촉각이 Meshing and Geometry Component Properties Surface properties Component Contact Angle에서 지정될 수 있다. 그렇지 않은 경우 전반적인 Contact angle로 기본값이 지정된다.

기본설정은 90도의 접촉각이다. 표면장력이 활성화되면 벽 접착이 활성화된다; 어떤 요소에서는 벽 접착을 해제시킬 수 없다. 모든 유체는 고체표면에서 접착 거동을 보여주며 습윤이거나 비습윤에 상관 없이 그 거동을 나타내기 위해 거동이 접촉각을 지정하는 것이 필요하다.

표면장력 계수는 온도의 함수이다. 온도와 표면장력간의 단순한 선형 관계식을 위해 다음에 따르는  Temperature Dependence (Fluids 내 Temperature sensitivity)에 대한 값을 준다.

where: 여기서

  • σ 는 계산된 표면 장력계수
  • σ0 는 사용자 정의된 Surface tension coefficient
  • Temperature Dependence
  • T 는 지역온도이며
  • T 는 사용자 지정 Reference Temperature (Fluids Properties에서 지정)

이 값들은 Physics Surface tension 대화상자나 Fluids Surface Tension에서 입력된다. 온도 의존 모델을 사용하기 위해서 유체 내 열 전달을 활성화 시키는 것이 필요하다.(Physics Heat transferFluid internal energy advection).

표면장력이 온도의 선형함수가 아닌 경우, Surface tension coefficient in Fluids Surface TensionTabular 버튼을 선택함으로써 표면장력 및 온도와 관련된 표 형식의 데이터를 입력할 수 있다.

자유표면이 부서지고 변형되는 유동에서 작은 유체방울이 간혹 표면장력 압력 계산의 수치적 잡음 때문에 발생될 수 있다. 이 방울들은 작은 표면 곡률 때문에 표면 압력 분포에 바람직하지 않은 변화를 일으킬 수 있다. 이런 방울 들은 이러한 수치적 부 정확성을 감소시키기 위해 제거될 수 있다. 이 옵션은 Fluid fraction cleanup in Numerics Volume-of-fluid advection Advanced options의 값을 입력함으로써 조절된다.

이 변수에 양수 값을 지정하면 유체분율 제거옵션이 활성화된다. 한 셀과 그 주변 셀의 유체분율 값이 이보다 작으면 유체는 그 셀에서 완전히 제거될 것이다.

제거된 체적은 누적 체적 에러로 기록되고 시간의 함수로 모사 후에 그려진다.; 이는 Fluid fraction cleanup 선택이 유체 체적이 크게 변경된 경우 보여진다. 2유체 표면장력 문제의 Fluid fraction cleanup 기본값은 0.05이다. 다른 모사의 경우 이의 기본값은0이다.

표면장력 모델은 곡률, 즉, 2차 미분 값에 의존하기 때문에 코드내의 다른 모델보다 불규칙한 격자에 더 민감하다. 가능하면 정육면체(2차원의 정사각형)에 가까운 제어체적을 사용하는 것을 추천한다.

 

표면 장력 / Surface Tension

표면 장력 / Surface Tension

FLOW-3D에 추가 된 최초의 물리 모델 중 하나는 표면 장력이었습니다.

이 모델은 잉크젯, 무중력 환경에서의 액체 연료 거동 및 다양한 MEMS (마이크로 전자 기계 시스템) 장치와 같이 다양한 종류의 응용 분야에서 수년 동안 널리 사용되어 왔습니다. 이 후에 모델의 개선 및 확장에 대한 많은 사용자 요청이 처리되었습니다.
표면 장력에 대해 보다 나은 성능개선을 위해 FLOW-3D 버전 11에 대한 새로운 모델이 개발되었습니다. 이 모델은 계산된 모든 표면 장력의 정확성과 임의 형상의 솔리드 표면을 잡아 당기는 접착력의 정확성을 향상시킵니다. 또한 이 새로운 모델은 다공성 물질의 모세관 압력과 비 균일한 표면 장력으로 인한 접선 표면 장력을 가지고 있습니다.

새로운 모델의 예는 무중력에 포함된 원형 벽을 적시는 단순한 문제입니다.

그림 1은 실린더와 접촉각이 0 도인 물로 채워진 0.25m 직경의 실린더 75 %의 경우를 보여줍니다. 버블은 10 초 전에 벽에서 깨끗하게 분리되어 탱크를 가로 질러 움직입니다. 비 구형은 기포 표면에서 모세관 파가 전파되기 때문입니다.

그림 1. 0.0, 2.5, 5.0 및 10.0 초에 무중력에서 접촉 각이 0 인 실린더 표면의 유체 (적색) 습윤 표면.

다른 예가 그림5에 도시되어 있습니다. 2에서 서로 다른 밀도의 2 개의 초기 구형 방울이 (플롯의 색으로 표시됨) 단단한 벽을 향해 아래로 이동합니다. 플롯의 시간은 0.0, 0.01, 0.02 및 0.03 초입니다. 방울은 직경이 0.0017m, 밀도가 다르지만 표면 장력 계수는 1.872 뉴턴 / m입니다.

그림 2. 접시쪽으로 움직이는 구형의 물방울. 새로운 표면 장력 모델로 시뮬레이션. 색상은 밀도를 나타냅니다.

표면 장력 모델에 대해 자세히 알아보십시오.

Download the Flow Science Report on Surface Tension

Download Surface Tension Validation – Simple Test Problems

FSR_01-12_Air-Entrainment-Report [공기 혼입 모델 분석]

Overview
In free-surface flows the turbulence in the liquid may be sufficient to disturb the surface to the point of entraining air into the flow. This process is important, for example, in water treatment where air is needed to sustain microorganisms for water purification and in rivers and streams for sustaining a healthy fish population. Air entrainment is typically engineered into spillways downstream of hydropower plants to reduce the possibility of cavitation damage at the base of the spillway. Situations where air entrainment is undesirable are in the sprue and runner systems used by metal casters, and in the filling of liquid containers used for consumer products.
The importance of being able to predict the amount and distribution of entrained air at a free liquid surface has led to the development of a unique model in FLOW-3D®. The model has two options. One option, to be used when the volume fraction of entrained air is relatively low, uses a passive scalar variable to record and transport the air volume fraction. This model is passive in that it does not alter the dynamics of the flow.
The second air-entrainment model option is based on a variable density formulation. This model includes the “bulking” of fluid volume by the addition of air and the buoyancy effects associated with entrained air. This dynamically coupled model cannot, however, be used in conjunction with heat transport and natural (thermal) convection.
In addition, when using the variable density formulation, the model can include a relative drifting of air in water, the possible escape of air if it rises to the surface of the water and the removal or addition of air to trapped bubble regions represented as adiabatic bubbles.
The same basic entrainment process is used in both options. It is based on a competition between the stabilizing forces of gravity and surface tension and the destabilizing effects of surface turbulence.
Because turbulence is the main cause of entrainment, a turbulence-transport model must be used in connection with the air-entrainment model. It is recommended that the RNG version of the more traditional k-epsilon turbulence model be employed. All the validation tests reported in this Technical Note were performed using the RNG model.

 

[다운로드]

FSR_01-12_Air-Entrainment-Report

Salt dissolution model [소금 용해 모델]

Introduction
Dissolution of salt in liquid is of interest in several applications – from solution mining to food processing to medical applications. This article describes a new model in FLOW-3D1 version 10.0 for dissolving salt in fluids and tracking the solute in the brine.
The dissolution of salt increases the density of the fluid and thus may affect the flow. In addition, as salt is dissolved, the flow domain increases. It is of interest, therefore, to predict these changes in the flow as well as the transport of the dissolved salt in the fluid.
The model accounts for the basic physical phenomena, such as mass transfer at the interface between salt and fluid, the change of volume and shape of the solid salt, diffusion and convection of dissolved salt in fluid and, finally, the change in fluid density, viscosity and surface tension coefficient.

Simulating the Residue left by Evaporating Drops

Background
The “coffee ring” effect is the name given to a well known observation where the evaporative drying of a drop of coffee leaves behind a ring of dark material at the edge of the original drop. On first thought one would expect that the coffee particles, which are uniformly distributed in the drop, would simply be deposited uniformly over the area wetted by the drop. It has only been in recent years that researchers have uncovered the mechanisms that produce the ring effect (Deegan, R.D., et al).
As currently understood, the edges of drops can become pinned because of roughness or chemical elements on the surface on which they lie. Heat transfer to the drops from the substrate or the air induces evaporation, which is usually greater near the drop edge. Surface tension forces then adjust the curvature of the remaining liquid consistent with the pinned edge, which results in a net flow of liquid toward the edge. This flow replenishes the evaporative loss but also moves solute to the edge where it is concentrated by evaporation. Eventually, this mechanism builds up a ring deposit of solute at the original edge of the drop.
The residue from dried drops has implications for many useful applications, including general coating processes, formation of pixel arrays of organic materials for video displays and for a variety of micro-electro-mechanical (MEMS) devices.
Because many factors control the distribution of dried residue it is desirable to have some means to model the fluid dynamics of the process to aid engineers in making the best choices for each specific application. Such a capability has been incorporated into FLOW-3D1 making it possible to computationally investigate the influence of such parameters as the initial solute concentration, fluid viscosity, volatility of the solvent, evaporation rate, surface tension and initial shape of the drop.
This technical note presents a brief description of the residue formation model and illustrates it with several computations of an evaporating drop subject to different physical conditions.

Modeling Turbulent Entrainment of Air at a Free Surface

Overview
In free-surface flows the turbulence in the liquid may be sufficient to disturb the surface to the point of entraining air into the flow. This process is important, for example, in water treatment where air is needed to sustain microorganisms for water purification and in rivers and streams for sustaining a healthy fish population. Air entrainment is typically engineered into spillways downstream of hydropower plants to reduce the possibility of cavitation damage at the base of the spillway. Other situations where air entrainment is undesirable are in the sprue and runner systems used by metal casters, and in the filling of liquid containers used for consumer products.
The importance of being able to predict the amount and distribution of entrained air at a free liquid surface has led to the development of a unique model that can be easily inserted into FLOW-3D® as a user customization. The model has two options. One option, to be used when the volume fraction of entrained air is relatively low, uses a scalar variable to record the air volume fraction. This model is passive in that it does not alter the dynamics of the flow.
A second air-entrainment model, option two, is based on a variable density formulation. This model includes the “bulking” of fluid volume by the addition of air and the buoyancy effects associated with entrained air. However, this dynamically coupled model cannot be used in connection with heat transport and natural (thermal) convection.
In both model options the same basic entrainment process is used that is based on a competition between the stabilizing forces of gravity and surface tension and the destabilizing effects of surface turbulence. The model is described in the next section. Because turbulence is the main cause of entrainment, a turbulence-transport model must be used in connection with the air-entrainment model (i.e., ifvis=3 or 4). It is recommended that the RNG version of the more traditional k-epsilon turbulence model be employed. All the validation tests reported in this Technical Note were performed using the RNG turbulence model.

Surface Tension Validation Tests

Modeling surface tension phenomena is computationally difficult because it requires the evaluation of second derivatives.
This is particulary true in the FLOW-3D program where the capability to represent highly complicated and multiple free surfaces difficulties are further compounded in three-dimensional calculations because one is often forced, for reasons of economy, to use marginal numerical resolution.

Simulating the Wetting and Drying of Shallow Flows [얕은 흐름의 습윤 및 건조 시뮬레이션]

Introduction
Shallow flows, characterized by having a thickness much smaller than their lateral extent, can often be modeled by a depth-averaged (shallow-water or 2.5 dimensional) approximation.
Average fluid velocities are computed in the layer and the top fluid surface is free to move, which leads to a changing fluid-layer thickness. The advantages of this approach are its speed
and simplicity over full three-dimensional simulations.
One complication, however, is how to efficiently account for dynamic contact-line effects at lateral boundaries of the fluid. These boundaries are free to move over the underlying solid
surface. Furthermore, the fluid contact angle at these boundaries depends on the local dynamic flow conditions.
In this paper we present a new shallow-flow computational method based on the Volume-of-Fluid (VOF) technique, which conserves fluid mass, while allowing for general wetting and
drying behavior. Non-uniform surface tension and fluid-substrate interactions, defined by a static contact angle, are included in the model. No special prescriptions are needed to locate
contact line locations or define dynamic contact angles.

A Surface Tension Model Update [표면장력 모델 업데이트]

PURPOSE AND BACKGROUND
The modeling of surface tension forces is computationally difficult because it requires the evaluation of surface curvatures, i.e., second derivatives of the surface location. This is
particularly true in FLOW-3D® since it uses a regular rectangular grid that does not conform to surface shapes. Although this simple grid structure makes it more difficult to evaluate surface
slopes and curvatures, it is this feature that also gives the strength needed to simulate coalescence and breakup of fluid blobs.
Evaluation of surface slope and curvature in FLOW-3D® is done by determining which coordinate direction is closest to the outward normal vector to the surface. Then fluid in a 3 by 3
by 3 set of grid cells surrounding a given cell is summed up in the cell columns parallel to the normal. This, in effect, gives a discrete representation of the surface height in nine (3×3)
columns, which can be used to compute slopes and curvatures.
In most cases this procedure works quite well, but when normal directions in the grid are near 45° the surface may be too steep for this procedure to work accurately. A consequence of this
loss of accuracy is the introduction of spurious pressures or perturbations that sometimes generate undesirable capillary waves (i.e., kinetic energy noise). Occasionally, these
perturbations can even destroy a computation.
A summary of the original surface tension model was given in Technical Note TN6, “Surface Tension Validation Tests,” (1987). Since that Note there have been a number of major improvements:

1. Wall adhesion sensitive to slope of wall,
2. Static contact angle as an obstacle property,
3. Two-fluid interfacial surface tension,
4. Thermocapillary (i.e., tangential) surface forces (see TN47).

In this Technical Note we document another improvement that has been made. In particular, we have improved the accuracy of the column summation technique for the computation of surface
curvatures. As the following examples will show, this improvement is quite dramatic in many cases where the earlier model experienced substantial difficulties.

물리 모델 소개

FLOW-3D 는 고도의 정확성이 필요한 항공, 자동차,  수자원 및 환경, 금속 산업분야의 세계적인 선진 기업에서 사용됩니다.

FLOW-3D의 광범위한 다중 물리 기능(multiphysics )은 자유 표면 흐름, 표면 장력, 열전달, 난류, 움직이는 물체, 단순 변형 고체, 전기 기계, 캐비테이션, 탄/소성, 점성, 가소성, 입자, 고체 연료, 연소 및 위상 변화를 포함합니다.
이러한 모델은 FLOW-3D를 사용하는 사용자들이 기술 및 과학의 광범위한 문제를 해결하도록 설계를 최적화하고 복잡한 프로세스 흐름에 대한 통찰력을 얻을 수 있도록 합니다.

flow-3d-multiphysics-model
Physics Models
Flow/Fluid Modes

Materials Databases

  • Fluids Database
  • Solids Database

매우 정확한
시뮬레이션 결과

FAVOR, 으로 알려진 특별한 메쉬 프로세스는 데카르트 구조의 단순함을 유지하면서 복잡한 형상을 효율적으로 구현합니다.

Optimized Setup
and Workflow

TruVOF 표면 추적 방법은 유동시뮬레이션을 위해 알려진 유체 체적을 사용하는 동안 가장 높은 정확도를 제공합니다.

FlowSight
Postprocessing

산업계에서 최고의 시각화 postprocessor인 FlowSight 는 사용자에게 2차원 및 3차원에 대한 심층 분석 기능을 제공합니다.

 

Microfluidics Bibliography

Microfluidics Bibliography

다음은 Microfluidics Bibliography의 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D  결과를 특징으로  합니다. 미세 유체 공정 및 장치 를 성공적으로 시뮬레이션하기 위해 FLOW-3D 를 사용 하는 방법에 대해 자세히 알아보십시오  .

2021년 5월 Update

Below is a collection of technical papers in our Microfluidics Bibliography. All of these papers feature FLOW-3D results. Learn more about how FLOW-3D can be used to successfully simulate microfluidic processes and devices.

14-21   Jian-Chiun Liou, Chih-Wei Peng, Philippe Basset, Zhen-Xi Chen, DNA printing integrated multiplexer driver microelectronic mechanical system head (IDMH) and microfluidic flow estimation, Micromachines, 12.1; 25, 2021. doi.org/10.3390/mi12010025

08-20   Li Yong-Qiang, Dong Jun-Yan and Rui Wei, Numerical simulation for capillary driven flow in capsule-type vane tank with clearances under microgravity, Microgravity Science and Technology, 2020. doi.org/10.1007/s12217-019-09773-z

89-19   Tim Dreckmann, Julien Boeuf, Imke-Sonja Ludwig, Jorg Lumkemann, and Jorg Huwyler, Low volume aseptic filling: impact of pump systems on shear stress, European Journal of Pharmeceutics and Biopharmeceutics, in press, 2019. doi:10.1016/j.ejpb.2019.12.006

88-19   V. Amiri Roodan, J. Gomez-Pastora, C. Gonzalez-Fernandez, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, CFD analysis of the generation and manipulation of ferrofluid droplets, TechConnect Briefs, pp. 182-185, 2019. TechConnect World Innovation Conference & Expo, Boston, Massachussetts, USA, June 17-19, 2019.

55-19     Julio Aleman, Sunil K. George, Samuel Herberg, Mahesh Devarasetty, Christopher D. Porada, Aleksander Skardal, and Graça Almeida‐Porada, Deconstructed microfluidic bone marrow on‐a‐chip to study normal and malignant hemopoietic cell–niche interactions, Small, 2019. doi: 10.1002/smll.201902971

37-19     Feng Lin Ng, Miniaturized 3D fibrous scaffold on stereolithography-printed microfluidic perfusion culture, Doctoral Thesis, Nanyang Technological University, Singapore, 2019.

32-19     Jenifer Gómez-Pastora, Ioannis H. Karampelas, Eugenio Bringas, Edward P. Furlani, and Inmaculada Ortiz, Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions, Nature: Scientific Reports, Vol. 9, No. 7265, 2019. doi: 10.1038/s41598-019-43827-x

01-19  Jelena Dinic and Vivek Sharma, Computational analysis of self-similar capillary-driven thinning and pinch-off dynamics during dripping using the volume-of-fluid method, Physics of Fluids, Vol. 31, 2019. doi: 10.1063/1.5061715

75-18   Tobias Ladner, Sebastian Odenwald, Kevin Kerls, Gerald Zieres, Adeline Boillon and Julien Bœuf, CFD supported investigation of shear induced by bottom-mounted magnetic stirrer in monoclonal antibody formulation, Pharmaceutical Research, Vol. 35, 2018. doi: 10.1007/s11095-018-2492-4

53-18   Venoos Amiri Roodan, Jenifer Gómez-Pastora, Aditi Verma, Eugenio Bringas, Inmaculada Ortiz and Edward P. Furlani, Computational analysis of magnetic droplet generation and manipulation in microfluidic devices, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 154, 2018.  doi: 10.11159/ffhmt18.154

35-18   Jenifer Gómez-Pastora, Cristina González Fernández, Marcos Fallanza, Eugenio Bringas and Inmaculada Ortiz, Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies, Chemical Engineering Journal, vol. 344, pp. 487-497, 2018. doi: 10.1016/j.cej.2018.03.110

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.

15-18   J. Gómez-Pastora, I.H. Karampelas, A.Q. Alorabi, M.D. Tarn, E. Bringas, A. Iles, V.N. Paunov, N. Pamme, E.P. Furlani, I. Ortiz, CFD analysis and experimental validation of magnetic droplet generation and deflection across multilaminar flow streams, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 182-185, 2018.

14-18   J. Gómez-Pastora, C. González-Fernández, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, Design of Magnetic Blood Cleansing Microdevices through Experimentally Validated CFD Modeling, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 170-173, 2018.

10-18   A. Gupta, I.H. Karampelas, J. Kitting, Numerical modeling of the formation of dynamically configurable L2 lens in a microchannel, Biotech, Biomaterials and Biomedical TechConnect Briefs, Vol. 3, pp. 186 – 189, 2018.

17-17   I.H. Karampelas, J. Gómez-Pastora, M.J. Cowan, E. Bringas, I. Ortiz and E.P. Furlani, Numerical Analysis of Acoustophoretic Discrete Particle Focusing in Microchannels, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

16-17   J. Gómez-Pastora, I.H. Karampelas, E. Bringas, E.P. Furlani and I. Ortiz, CFD analysis of particle magnetophoresis in multiphase continuous-flow bioseparators, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

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

102-16   J. Brindha, RA.G. Privita Edwina, P.K. Rajesh and P.Rani, “Influence of rheological properties of protein bio-inks on printability: A simulation and validation study,” Materials Today: Proceedings, vol. 3, no.10, pp. 3285-3295, 2016. doi: 10.1016/j.matpr.2016.10.010

99-16   Ioannis H. Karampelas, Kai Liu, Fatema Alali, and Edward P. Furlani, Plasmonic Nanoframes for Photothermal Energy Conversion, J. Phys. Chem. C, 2016, 120 (13), pp 7256–7264

98-16   Jelena Dinic and Vivek Sharma, Drop formation, pinch-off dynamics and liquid transfer of simple and complex fluidshttp://meetings.aps.org/link/BAPS.2016.MAR.B53.12, APS March Meeting 2016, Volume 61, Number 2, March 14–18, 2016, Baltimore, Maryland

67-16  Vahid Bazargan and Boris Stoeber, Effect of substrate conductivity on the evaporation of small sessile droplets, PHYSICAL REVIEW E 94, 033103 (2016), doi: 10.1103/PhysRevE.94.033103

57-16   Ioannis Karampelas, Computational analysis of pulsed-laser plasmon-enhanced photothermal energy conversion and nanobubble generation in the nanoscale, PhD Dissertation: Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, July 2016

44-16   Takeshi Sawada et al., Prognostic impact of circulating tumor cell detected using a novel fluidic cell microarray chip system in patients with breast cancer, EBioMedicine, Available online 27 July 2016, doi: 10.1016/j.ebiom.2016.07.027.

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

30-16   Ioannis H. Karampelas, Kai Liu and Edward P. Furlani, Plasmonic Nanocages as Photothermal Transducers for Nanobubble Cancer Therapy, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

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.

02-16  Stephen D. Hoath (Editor), Fundamentals of Inkjet Printing: The Science of Inkjet and Droplets, ISBN: 978-3-527-33785-9, 472 pages, February 2016 (see chapters 2 and 3 for FLOW-3D results)

125-15   J. Berthier, K.A. Brakke, E.P. Furlani, I.H. Karampelas, V. Poher, D. Gosselin, M. Cubinzolles and P. Pouteau, Whole blood spontaneous capillary flow in narrow V-groove microchannels, Sensors and Actuators B: Chemical, 206, pp. 258-267, 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.

77-15   Ho-Lin Tsai, Weng-Sing Hwang, Jhih-Kai Wang, Wen-Chih Peng and Shin-Hau Chen, Fabrication of Microdots Using Piezoelectric Dispensing Technique for Viscous Fluids, Materials 2015, 8(10), 7006-7016. doi: 10.3390/ma8105355

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

28-15   Yongqiang Li, Mingzhu Hu, Ling Liu, Yin-Yin Su, Li Duan, and Qi Kang, Study of Capillary Driven Flow in an Interior Corner of Rounded Wall Under MicrogravityMicrogravity Science and Technology, June 2015

20-15   Pamela J. Waterman, Diversity in Medical Simulation Applications, Desktop Engineering, May 2015, pp 22-26,

16-15   Saurabh Singh, Ann Junghans, Erik Watkins, Yash Kapoor, Ryan Toomey, and Jaroslaw Majewski, Effects of Fluid Shear Stress on Polyelectrolyte Multilayers by Neutron Scattering Studies, © 2015 American Chemical Society, DOI: 10.1021/acs.langmuir.5b00037, Langmuir 2015, 31, 2870−2878, February 17, 2015

11-15   Cheng-Han Wu and Weng-Sing Hwang, The effect of process condition of the ink-jet printing process on the molten metallic droplet formation through the analysis of fluid propagation direction, Canadian Journal of Physics, 2015. doi: 10.1139/cjp-2014-0259

03-15 Hanchul Cho, Sivasubramanian Somu, Jin Young Lee, Hobin Jeong and Ahmed Busnaina, High-Rate Nanoscale Offset Printing Process Using Directed Assembly and Transfer of Nanomaterials, Adv. Materials, doi: 10.1002/adma.201404769, February 2015

122-14  Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastián D’hers and Noel M Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Research Gate, doi: 10.1007/s13346-014-0198-7, July 2014

113-14 Cihan Yilmaz, Arif E. Cetin, Georgia Goutzamanidis, Jun Huang, Sivasubramanian Somu, Hatice Altug, Dongguang Wei and Ahmed Busnaina, Three-Dimensional Crystalline and Homogeneous Metallic Nanostructures Using Directed Assembly of Nanoparticles, 10.1021/nn500084g, © 2014 American Chemical Society, April 2014

110-14 Koushik Ponnuru, Jincheng Wu, Preeti Ashok, Emmanuel S. Tzanakakis and Edward P. Furlani, Analysis of Stem Cell Culture Performance in a Microcarrier Bioreactor System, Nanotech, Washington, D.C., June 15-18, 2014

109-14   Ioannis H. Karampelas, Young Hwa Kim and Edward P. Furlani, Numerical Analysis of Laser Induced Photothermal Effects using Colloidal Plasmonic Nanostructures, Nanotech, Washington, D.C., June 15-18, 2014

108-14   Chenxu Liu, Xiaozheng Xue and Edward P. Furlani, Numerical Analysis of Fully-Coupled Particle-Fluid Transport and Free-Flow Magnetophoretic Sorting in Microfluidic Systems, Nanotech, Washington, D.C., June 15-18, 2014

95-14   Cheng-Han Wu, Weng-Sing Hwang, The effect of the echo-time of a bipolar pulse waveform on molten metallic droplet formation by squeeze mode piezoelectric inkjet printing, Accepted November 2014, Microelectronics Reliability (2014) , © 2014 Elsevier Ltd. All rights reserved.

85-14   Sudhir Srivastava, Lattice Boltzmann method for contact line dynamics, ISBN: 978-90-386-3608-5, Copyright © 2014 S. Srivastava

61-14   Chenxu Liu, A Computational Model for Predicting Fully-Coupled Particle-Fluid Dynamics and Self-Assembly for Magnetic Particle Applications, Master’s Thesis: State University of New York at Buffalo, 2014, 75 pages; 1561583, http://gradworks.umi.com/15/61/1561583.html

41-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastian D’hers, and Noel M. Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Drug Deliv. and Transl. Res., DOI 10.1007/s13346-014-0198-7, # Controlled Release Society 2014. Available for purchase online at SpringerLink.

21-14  Suk-Hee Park, Ung Hyun Koh, Mina Kim, Dong-Yol Yang, Kahp-Yang Suh and Jennifer Hyunjong Shin, Hierarchical multilayer assembly of an ordered nanofibrous scaffold via thermal fusion bonding, Biofabrication 6 (2014) 024107 (10pp), doi:10.1088/1758-5082/6/2/024107, IOP Publishing, 2014. Available for purchase online at IOP.

17-14   Vahid Bazargan, Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles, Ph.D. Thesis: Department of Mechanical Engineering, The University of British Columbia, March 2014, © Vahid Bazargan, 2014

73-13  Oliver G. Harlen, J. Rafael Castrejón-Pita, and Arturo Castrejon-Pita, Asymmetric Detachment from Angled Nozzles Plates in Drop-on Demand Inkjet Printing, NIP & Digital Fabrication Conference, 2013 International Conference on Digital Printing Technologies. Pages 253-549, pp. 277-280(4)

63-13  Fatema Alali, Ioannis H. Karampelas, Young Hwa Kim, and Edward P. Furlani, Photonic and Thermofluidic Analysis of Colloidal Plasmonic Nanorings and Nanotori for Pulsed-Laser Photothermal ApplicationsJ. Phys. Chem. C, Article ASAP, DOI: 10.1021/jp406986y, Copyright © 2013 American Chemical Society, September 2013.

25-13  Sudhir Srivastava, Theo Driessen, Roger Jeurissen, Herma Wijshoff, and Federico Toschi, Lattice Boltzmann Method to Study the Contraction of a Viscous Ligament, International Journal of Modern Physics © World Scientific Publishing Company, May 2013.

11-13  Li-Chieh Hsu, Yong-Jhih Chen, Jia-Huang Liou, Numerical Investigation in the Factors on the Pool Boiling, Applied Mechanics and Materials Vol. 311 (2013) pp 456-461, © (2013) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.311.456. Available for purchase online at Scientific.Net.

10-13 Pamela J. Waterman, CFD: Shaping the Medical World, Desktop Engineering, April 2013. Full article available online at Desktop Engineering.

90-12 Charles R. Ortloff and Martin Vogel, Spray Cooling Heat Transfer- Test and CFD Analysis, Electronics Cooling, June 2012. Available online at Electronics Cooling.

79-12    Daniel Parsaoran Siregar, Numerical simulation of evaporation and absorption of inkjet printed droplets, Ph.D. Thesis: Technische Universiteit Eindhoven, September 18, 2012, Copyright 2012 by D.P. Siregar, ISBN: 978-90-386-3190-5.

71-12   Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, Seungwan Lee, and Woonbae Kim, Varifocal liquid lens based on microelectrofluidic technology, Optics Letters, Vol. 37, Issue 21, pp. 4377-4379 (2012) http://dx.doi.org/10.1364/OL.37.004377

70-12   Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, and Seunwan Lee, Microelectrofluidic Iris for Variable ApertureProc. SPIE 8252, MOEMS and Miniaturized Systems XI, 82520O (February 9, 2012); doi:10.1117/12.906587

69-12   Jong-hyeon Chang, Eunsung Lee, Kyu-Dong Jung, Seungwan Lee, Minseog Choi, and  Woonbae Kim, Microelectrofluidic Lens for Variable CurvatureProc. SPIE 8486, Current Developments in Lens Design and Optical Engineering XIII, 84860X (October 11, 2012); doi:10.1117/12.925852.

61-12  Biddut Bhattacharjee, Study of Droplet Splitting in an Electrowetting Based Digital Microfluidic System, Thesis: Doctor of Philosophy in the College of Graduate Studies (Applied Sciences), The University of British Columbia, September 2012, © Biddut Bhattacharjee.

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. Available for purchase online at SciVerse.

54-12   Edward P. Furlani, Anthony Nunez, Gianmarco Vizzeri, Modeling Fluid Structure-Interactions for Biomechanical Analysis of the Human Eye, Nanotech Conference & Expo, June 18-21, 2012, Santa Clara, CA.

53-12   Xinyun Wu, Richard D. Oleschuk and Natalie M. Cann, Characterization of microstructured fibre emitters in pursuit of improved nano electrospray ionization performance, The Royal Society of Chemistry 2012, http://pubs.rsc.org, DOI: 10.1039/c2an35249d, May 2012

25-12    Edward P. Furlani, Ioannis H. Karampelas and Qian Xie, Analysis of Pulsed Laser Plasmon-assisted Photothermal Heating and Bubble Generation at the Nanoscale, Lab on a Chip, 10.1039/C2LC40495H, Received 01 May 2012, Accepted 07 Jun 2012. First published on the web 13 Jun 2012.

22-12  R.A. Sultanov, D. Guster, Numerical Modeling and Simulations of Pulsatile Human Blood Flow in Different 3D-Geometries, Book chapter #21 in Fluid Dynamics, Computational Modeling and Applications (2012), ISBN: 978-953-51-0052-2, p. 475 [18 pages]. Available online at INTECH.

21-12  Guo-Wei Huang, Tzu-Yi Hung, and Chin-Tai Chen, Design, Simulation, and Verification of Fluidic Light-Guide Chips with Various Geometries of Micro Polymer Channels, NEMS 2012, Kyoto, Japan, March 5-8, 2012. Available for purchase online at IEEE.

103-11   Suk-Hee Park, Development of Three-Dimensional Scaffolds containing Electrospun Nanofibers and their Applications to Tissue Regeneration, Ph.D. Thesis: School of Mechanical, Aersospace and Systems Engineering, Division of Mechanical Engineering, KAIST, 2011.

81-11   Xinyun Wu, Modeling and Characterization of Microfabricated Emitters-In Pursuit of Improved ESI-MS Performance, thesis: Department of Chemistry, Queen’s University, December 2011, Copyright © Xinyun Wu, 2011

79-11  Cong Lu, A Cell Preparation Stage for Automatic Cell Injection, thesis: Graduate Department of Mechanical and Industrial Engineering, University of Toronto, Copyright © Cong Lu, 2011

77-11 Ge Bai, W. Thomas Leach, Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development, International Journal of Pharmaceutics, Available online 8 December 2011, ISSN 0378-5173, 10.1016/j.ijpharm.2011.11.044. Available online at SciVerse.

72-11  M.R. Barkhudarov, C.W. Hirt, D. Milano, and G. Wei, Comments on a Comparison of CFD Software for Microfluidic Applications, Flow Science Technical Note #93, FSI-11-TN93, December 2011

45-11  Chang-Wei Kang, Jiak Kwang Tan, Lunsheng Pan, Cheng Yee Low and Ahmed Jaffar, Numerical and experimental investigations of splat geometric characteristics during oblique impact of plasma spraying, Applied Surface Science, In Press, Corrected Proof, Available online 20 July 2011, ISSN 0169-4332, DOI: 10.1016/j.apsusc.2011.06.081. Available to purchase online at SciVers

33-11  Edward P. Furlani, Mark T. Swihart, Natalia Litchinitser, Christopher N. Delametter and Melissa Carter, Modeling Nanoscale Plasmon-assisted Bubble Nucleation and Applications, Nanotech Conference and Expo 2011, Boston, MA, June 13-16, 2011

32-11  Lu, Cong and Mills, James K., Three cell separation design for realizing automatic cell injection, Complex Medical Engineering (CME), 2011 IEEE/ICME, pp: 599 – 603, Harbin, China, 10.1109/ICCME.2011.5876811, June 2011. Available online at IEEEXplore.

25-11 Issam M. Bahadur, James K. Mills, Fluidic vacuum-based biological cell holding device with piezoelectrically induced vibration, Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on, 22-25 May 2011, pp: 85 – 90, Harbin, China. Available online at: IEEE Xplore.

14-11  Edward P. Furlani, Roshni Biswas, Alexander N. Cartwright and Natalia M. Litchinitser, Antiresonant guiding optofluidic biosensor, doi:10.1016/j.optcom.2011.04.014, Optics Communication, April 2011

05-11 Hyeju Eom and Keun Park, Integrated numerical analysis to evaluate replication characteristics of micro channels in a locally heated mold by selective induction, International Journal of Precision Engineering and Manufacturing, Volume 12, Number 1, 53-60, DOI: 10.1007/s12541-011-0007-x, 2011. Available online at: SpringerLink.

70-10  I.N. Volnov, V.S. Nagornyi, Modeling Processes for Generation of Streams of Monodispersed Fluid Droplets in Electro-inkjet Applications, Science and Technology News, St. Petersburg State Polytechnic University, 4, pp 294-300, 2010. In Russian.

62-10  F. Mobadersani, M. Eskandarzade, S. Azizi and S. Abbasnezhad, Effect of Ambient Pressure on Bubble Growth in Micro-Channel and Its Pumping Effect, ESDA2010-24436, pp. 577-584, doi:10.1115/ESDA2010-24436, ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis (ESDA2010), Istanbul, Turkey, July 12–14, 2010. Available online at the ASME Digital Library.

58-10 Tsung-Yi Ho, Jun Zeng, and Chakrabarty, K, Digital microfluidic biochips: A vision for functional diversity and more than moore, Computer-Aided Design (ICCAD), 2010 IEEE/ACM International Conference on, DOI: 10.1109/ICCAD.2010.5654199, © IEEE, November 2010. Available online at IEEE Explore.

51-10  Regina Bleul, Marion Ritzi-Lehnert, Julian Höth, Nico Scharpfenecker, Ines Frese, Dominik Düchs, Sabine Brunklaus, Thomas E. Hansen-Hagge, Franz-Josef Meyer-Almes, Klaus S. Drese, Compact, cost-efficient microfluidics-based stopped-flow device, Anal Bioanal Chem, DOI 10.1007/s00216-010-4446-5, Available online at Springer, November 2010

22-10    Krishendu Chakrabarty, Richard B. Fair and Jun Zeng, Design Tools for Digital Microfluidic Biochips Toward Functional Diversification and More than Moore, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 29, No. 7, July 2010

14-10 E. P. Furlani and M. S. Hanchak, Nonlinear analysis of the deformation and breakup of viscous microjets using the method of lines, International Journal for Numerical Methods in Fluids (2010), © 2010 John Wiley & Sons, Ltd., Published online in Wiley InterScience. DOI: 10.1002/fld.2205

55-09 R.A. Sultanov, and D. Guster, Computer simulations of  pulsatile human blood flow through 3D models of the human aortic arch, vessels of simple geometry and a bifurcated artery, Proceedings of the 31st Annual International Conference of the IEEE EMBS (Engineering in Medicine and Biology Society), Minneapolis, September 2-6, 2009, p.p. 4704-4710.

30-09 Anurag Chandorkar and Shayan Palit, Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method, Sensors & Transducers journal, ISSN 1726-5479 © 2009 by IFSA, Vol.7, Special Issue “MEMS: From Micro Devices to Wireless Systems,” October 2009, pp. 136-149.

13-09 E.P. Furlani, M.C. Carter, Analysis of an Electrostatically Actuated MEMS Drop Ejector, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

12-09 A. Chandorkar, S. Palit, Simulation of Droplet-Based Microfluidics Devices Using a Volume-of-Fluid Approach, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

3-09 Christopher N. Delametter, FLOW-3D Speeds MEMS Inkjet Development, Desktop Engineering, January 2009

42-08  Tien-Li Chang, Jung-Chang Wang, Chun-Chi Chen, Ya-Wei Lee, Ta-Hsin Chou, A non-fluorine mold release agent for Ni stamp in nanoimprint process, Microelectronic Engineering 85 (2008) 1608–1612

26-08 Pamela J. Waterman, First-Pass CFD Analyses – Part 2, Desktop Engineering, November 2008

09-08 M. Ren and H. Wijshoff, Thermal effect on the penetration of an ink droplet onto a porous medium, Proc. Eurotherm2008 MNH, 1 (2008)

04-08 Delametter, Christopher N., MEMS development in less than half the time, Small Times, Online Edition, May 2008

02-08 Renat A. Sultanov, Dennis Guster, Brent Engelbrekt and Richard Blankenbecler, 3D Computer Simulations of Pulsatile Human Blood Flows in Vessels and in the Aortic Arch – Investigation of Non-Newtonian Characteristics of Human Blood, The Journal of Computational Physics, arXiv:0802.2362v1 [physics.comp-ph], February 2008

01-08 Herman Wijshoff, thesis: University of Twente, Structure- and fluid dynamics in piezo inkjet printheads, ISBN 978-90-365-2582-4, Venlo, The Netherlands January 2008.

30-07 A. K. Sen, J. Darabi, and D. R. Knapp, Simulation and parametric study of a novel multi-spray emitter for ESI–MS applications, Microfluidics and Nanofluidics, Volume 3, Number 3, June 2007, pp. 283-298(16)

28-07 Dan Soltman and Vivek Subramanian, Inkjet-Printed Line Morphologies and Temperature Control of the Coffee Ring Effect, Langmuir; 2008; ASAP Web Release Date: 16-Jan-2008; (Research Article) DOI: 10.1021/la7026847

23-07 A K Sen and J Darabi, Droplet ejection performance of a monolithic thermal inkjet print head, Journal of Micromechanical and Microengineering,vol.17, pp.1420-1427 (2007) doi:10.1088/0960-1317/17/8/002; Abstract only.

18-07 Herman Wisjhoff, Better Printheads Via Simulation, Desktop Engineering, October 2007, Vol. 13, Issue 2

17-07 Jos de Jong, Ph.D. Thesis: University of Twente, Air entrapment in piezo inkjet printing, ISBN 978-90-365-2483-4, April 2007

15-07 Krishnendu Chakrabarty and Jun Zeng, (Ed.), Design Automation Methods and Tools for Microfluidics-Based Biochips, Springer, September 2006.

14-07 Fei Su and Jun Zeng, Computer-aided design and test for digital microfluidics, IEEE Design & Test of Computers, 24(1), 2007, 60-70.

13-07 Jun Zeng, Modeling and simulation of electrified droplets and its application to computer-aided design of digital microfluidics, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(2), 2006, 224-233.

12-07 Krishnendu Chakrabarty and Jun Zeng, (2005), Automated top-down design for microfluidic biochips, ACM Journal on Emerging Technologies in Computing Systems, 1(3), 2005, 186–223.

01-07 Wijshoff, Herman, Drop formation mechanisms in piezo-acoustic inkjet, NSTI-Nanotech 2007, ISBN 1420061844 Vol. 3, 2007)

23-06 John J. Uebbing, Stephan Hengstler, Dale Schroeder, Shalini Venkatesh, and Rick Haven, Heat and Fluid Flow in an Optical Switch Bubble, Journal of Microelectromechanical Systems, Vol. 15, No. 6, December 2006

21-06 Wijshoff, Herman, Manipulating Drop Formation in Piezo Acoustic Inkjet, Proc. IS&T’s NIP22, 79 (2006)

20-06 J. de Jong, H. Reinten, M. van den Berg, H. Wijshoff, M. Versluis, G. de Bruin, A. Prosperetti and D. Lohse, Air entrapment in piezo-driven inkjet printheads, J. Acoust. Soc. Am. 120(3), 1257 (2006)

11-06 A. K. Sen, J. Darabi, D. R. Knapp and J. Liu, Modeling and Characterization of a Carbon Fiber Emitter for Electrospray Ionization, 1 MEMS and Microsystems Laboratory, Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA, 2 Department of Pharmacology, Medical University of South Carolina, Charleston, SC

5-06 E. P. Furlani, B. G. Price, G. Hawkins, and A. G. Lopez, Thermally Induced Marangoni Instability of Liquid Microjets with Application to Continuous Inkjet Printing, Proceedings of NSTI Nanotech Conference 2006, Vol. 2, pp 534-537.

28-05 O B Fawehinmi, P H Gaskell, P K Jimack, N Kapur, and H M Thompson, A combined experimental and computational fluid dynamics analysis of the dynamics of drop formation, May 2005. DOI: 10.1243/095440605X31788

5-05 E. P. Furlani, Thermal Modulation and Instability of Newtonian Liquid Microjets, presented at Nanotech 2005, Anaheim, CA, May 8-12, 2005.

1-05 C.W. Hirt, Electro-Hydrodynamics of Semi-Conductive Fluids: With Application to Electro-Spraying, Flow Science Technical Note #70, FSI-05-TN70

19-04 G. F. Yao, Modeling of Electroosmosis Without Resolving Physics Inside a Electric Double Layer, Flow Science Technical Note (FSI-04-TN69)

12-04 Jun Zeng and Tom Korsmeyer, Principles of Droplet Electrohydrodynamics for Lab-on-a-Chip, Lab. Chip. Journal, 2004, 4(4), 265-277

9-04 Constantine N. Anagnostopoulos, James M. Chwalek, Christopher N. Delametter, Gilbert A. Hawkins, David L. Jeanmaire, John A. Lebens, Ali Lopez, and David P. Trauernicht, Micro-Jet Nozzle Array for Precise Droplet Metering and Steering Having Increased Droplet Deflection, Proceedings of the 12th International Conference on Solid State Sensors, Actuators and Microsystems, sponsored by IEEE, Boston, June 8-12, 2003, pp. 368-71

8-04 Christopher N. Delametter, David P. Trauernicht, James M. Chwalek, Novel Microfluidic Jet Deflection – Significant Modeling Challenge with Great Application Potential, Technical Proceedings of the 2002 International Conference on Modeling and Simulation of Microsystems sponsored by NSTI, San Juan, Puerto Rico, April 21-25, 2002, pp. 44-47

6-04 D. Vadillo*, G. Desie**, A Soucemarianadin*, Spreading Behavior of Single and Multiple Drops, *Laboratoire des Ecoulements Geophysiques et Industriels (LEGI), and **AGFA-Gevaert Group N.V., XXI ICTAM, 15-21 August 2004, Warsaw, Poland

2-04 Herman Wijshoff, Free Surface Flow and Acousto-Elastic Interaction in Piezo Inkjet, Nanotech 2004, sponsored by the Nano Science & Technology Institute, Boston, MA, March 2004

30-03 D Souders, I Khan and GF Yao, Alessandro Incognito, and Matteo Corrado, A Numerical Model for Simulation of Combined Electroosmotic and Pressure Driven Flow in Microdevices, 7th International Symposium on Fluid Control, Measurement and Visualization

27-03 Jun Zeng, Daniel Sobek and Tom Korsmeyer, Electro-Hydrodynamic Modeling of Electrospray Ionization – CAD for a µFluidic Device-Mass Spectrometer Interface, Agilent Technologies Inc, paper presented at Transducers 2003, June 03 Boston (note: Reference #10 is to FLOW-3D)

17-03 John Uebbing, Switching Fiber-optic Circuits with Microscopic Bubbles, Sensors Magazine, May 2003, Vol 20, No 5, p 36-42

16-03 CFD Speeds Development of MEMS-based Printing Technology, MicroNano Magazine, June 2003, Vol 8, No 6, p 16

3-03 Simulation Speeds Design of Microfluidic Medical Devices, R&D Magazine, March 2003, pp 18-19

1-03 Simulations Help Microscopic Bubbles Switch Fiber-Optic Circuits, Agilent Technologies, Fiberoptic Product News, January 2003, pp 22-23

27-02 Feng, James Q., A General Fluid Dynamic Analysis of Drop Ejection in Drop-on-Demand Ink Jet Devices, Journal of Imaging Science and Technology®, Volume 46, Number 5, September/October 2002

1-02 Feixia Pan, Joel Kubby, and Jingkuang Chen, Numerical Simulation of Fluid Structure Interaction in a MEMS Diaphragm Drop Ejector, Xerox Wilson Research Center, Institute of Physics Publishing, Journal of Micromechanics and Microengineering, 12 (2002), PII: SO960-1317(02)27439-2, pp. 70-76

48-01   Rainer Gruber, Radial Mass Transfer Enhancement in Bubble-Train Flow, PhD thesis in Engineering Sciences, Rheinisch- Westf alischen Technische Hochschule Aachen, December 2001.

34-01 Furlani, E.P., Delametter, C.N., Chwalek, J.M., and Trauernicht, D., Surface Tension Induced Instability of Viscous Liquid Jets, Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

12-01 C. N. Delametter, Eastman Kodak Company, Micro Resolution, Mechanical Engineering, Col 123/No 7, July 2001, pp 70-72

11-01 C. N. Delametter, Eastman Kodak Company, Surface Tension Induced Instability of Viscous Liquid Jets, Technical Proceeding of the Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

9-01 Aman Khan, Unipath Limited Research and Development, Effects of Reynolds Number on Surface Rolling in Small Drops, PVP-Col 431, Emerging Technologies for Fluids, Structures and Fluids, Structures and Fluid Structure Interaction — 2001

2-00 Narayan V. Deshpande, Significance of Inertance and Resistance in Fluidics of Thermal Ink-Jet Transducers, Journal of Imaging Science and Technology, Volume 40, Number 5, Sept./Oct. 1996, pp.457-461

4-98 D. Deitz, Connecting the Dots with CFD, Mechanical Engineering Magazine, pp. 90-91, March 1998

14-94 M. P. O’Hare, N. V. Deshpande, and D. J. Drake, Drop Generation Processes in TIJ Printheads, Xerox Corporation, Adv. Imaging Business Unit, IS&T’s Tenth International Congress on Advances in Non-Impact Printing, Tech. 1994

14-92 Asai, A.,Three-Dimensional Calculation of Bubble Growth and Drop Ejection in a Bubble Jet Printer, Journal of Fluids Engineering Vol. 114 December 1992:638-641

FLOW-3D/MP Features List

FLOW-3D/MP Features

FLOW-3D/MP v6.1 은 FLOW-3D v11.1 솔버에 기초하여 물리 모델, 특징 및 그래픽 사용자 인터페이스가 동일합니다. FLOW-3D v11.1의 새로운 기능은 아래 파란색으로 표시되어 있으며 FLOW-3D/MP v6.1 에서 사용할 수 있습니다. 새로운 개발 기능에 대한 자세한 설명은 FLOW-3D v11.1에서 새로운 기능을 참조하십시오.

Meshing & Geometry

  • Structured finite difference/control volume meshes for fluid and thermal solutions
  • Finite element meshes in Cartesian and cylindrical coordinates for structural analysis
  • Multi-Block gridding with nested, linked, partially overlapping and conforming mesh blocks
  • Fractional areas/volumes (FAVOR™) for efficient & accurate geometry definition
  • Mesh quality checking
  • Basic Solids Modeler
  • Import CAD data
  • Import/export finite element meshes via Exodus-II file format
  • Grid & geometry independence
  • Cartesian or cylindrical coordinates
Flow Type Options
  • Internal, external & free-surface flows
  • 3D, 2D & 1D problems
  • Transient flows
  • Inviscid, viscous laminar & turbulent flows
  • Hybrid shallow water/3D flows
  • Non-inertial reference frame motion
  • Multiple scalar species
  • Two-phase flows
  • Heat transfer with phase change
  • Saturated & unsaturated porous media
Physical Modeling Options
  • Fluid structure interaction
  • Thermally-induced stresses
  • Plastic deformation of solids
  • Granular flow
  • Moisture drying
  • Solid solute dissolution
  • Sediment transport and scour
  • Cavitation (potential, passive tracking, active tracking)
  • Phase change (liquid-vapor, liquid-solid)
  • Surface tension
  • Thermocapillary effects
  • Wall adhesion
  • Wall roughness
  • Vapor & gas bubbles
  • Solidification & melting
  • Mass/momentum/energy sources
  • Shear, density & temperature-dependent viscosity
  • Thixotropic viscosity
  • Visco-elastic-plastic fluids
  • Elastic membranes & walls
  • Evaporation residue
  • Electro-mechanical effects
  • Dielectric phenomena
  • Electro-osmosis
  • Electrostatic particles
  • Joule heating
  • Air entrainment
  • Molecular & turbulent diffusion
  • Temperature-dependent material properties
  • Spray cooling
Flow Definition Options
  • General boundary conditions
    • Symmetry
    • Rigid and flexible walls
    • Continuative
    • Periodic
    • Specified pressure
    • Specified velocity
    • Outflow
    • Grid overlay
    • Hydrostatic pressure
    • Volume flow rate
    • Non-linear periodic and solitary surface waves
    • Rating curve and natural hydraulics
    • Wave absorbing layer
  • Restart from previous simulation
  • Continuation of a simulation
  • Overlay boundary conditions
  • Change mesh and modeling options
  • Change model parameters
Thermal Modeling Options
  • Natural convection
  • Forced convection
  • Conduction in fluid & solid
  • Fluid-solid heat transfer
  • Distributed energy sources/sinks in fluids and solids
  • Radiation
  • Viscous heating
  • Orthotropic thermal conductivity
  • Thermally-induced stresses
Turbulence Models
  • RNG model
  • Two-equation k-epsilon model
  • Two-equation k-omega model
  • Large eddy simulation
Metal Casting Models
  • Thermal stress & deformations
  • Iron solidification
  • Sand core blowing
  • Sand core drying
  • Permeable molds
  • Solidification & melting
  • Solidification shrinkage with interdendritic feeding
  • Micro & macro porosity
  • Binary alloy segregation
  • Thermal die cycling
  • Surface oxide defects
  • Cavitation potential
  • Lost-foam casting
  • Semi-solid material
  • Core gas generation
  • Back pressure & vents
  • Shot sleeves
  • PQ2 diagram
  • Squeeze pins
  • Filters
  • Air entrainment
  • Temperature-dependent material properties
  • Cooling channels
  • Fluid/wall contact time
Numerical Modeling Options
  • TruVOF Volume-of-Fluid (VOF) method for fluid interfaces
  • First and second order advection
  • Sharp and diffuse interface tracking
  • Implicit & explicit numerical methods
  • GMRES, point and line relaxation pressure solvers
  • User-defined variables, subroutines & output
  • Utilities for runtime interaction during execution
Fluid Modeling Options
  • One incompressible fluid – confined or with free surfaces
  • Two incompressible fluids – miscible or with sharp interfaces
  • Compressible fluid – subsonic, transonic, supersonic
  • Stratified fluid
  • Acoustic phenomena
  • Mass particles with variable density or diameter
Shallow Flow Models
  • General topography
  • Raster data interface
  • Subcomponent-specific surface roughness
  • Wind shear
  • Ground roughness effects
  • Laminar & turbulent flow
  • Sediment transport and scour
  • Surface tension
  • Heat transfer
  • Wetting & drying
Advanced Physical Models
  • General Moving Object model with 6 DOF–prescribed and fully-coupled motion
  • Rotating/spinning objects
  • Collision model
  • Tethered moving objects (springs, ropes, mooring lines)
  • Flexing membranes and walls
  • Porosity
  • Finite element based elastic-plastic deformation
  • Finite element based thermal stress evolution due to thermal changes in a solidifying fluid
  • Combusting solid components
Chemistry Models
  • Stiff equation solver for chemical rate equations
  • Stationary or advected species
Porous Media Models
  • Saturated and unsaturated flow
  • Variable porosity
  • Directional porosity
  • General flow losses (linear & quadratic)
  • Capillary pressure
  • Heat transfer in porous media
  • Van Genunchten model for unsaturated flow
Discrete Particle Models
  • Massless marker particles
  • Mass particles of variable size/mass
  • Linear & quadratic fluid-dynamic drag
  • Monte-Carlo diffusion
  • Particle-Fluid momentum coupling
  • Coefficient of restitution or sticky particles
  • Point or volumetric particle sources
  • Charged particles
  • Probe particles
Two-Phase & Two-Component Models
  • Liquid/liquid & gas/liquid interfaces
  • Variable density mixtures
  • Compressible fluid with a dispersed incompressible component
  • Drift flux
  • Two-component, vapor/non-condensable gases
  • Phase transformations for gas-liquid & liquid-solid
  • Adiabatic bubbles
  • Bubbles with phase change
  • Continuum fluid with discrete particles
  • Scalar transport
  • Homogeneous bubbles
  • Super-cooling
Coupling with Other Programs
  • Geometry input from Stereolithography (STL) files – binary or ASCII
  • Direct interfaces with EnSight®, FieldView® & Tecplot® visualization software
  • Finite element solution import/export via Exodus-II file format
  • PLOT3D output
  • Neutral file output
  • Extensive customization possibilities
  • Solid Properties Materials Database
Data Processing Options
  • State-of-the-art post-processing tool, FlowSight™
  • Batch post-processing
  • Report generation
  • Automatic or custom results analysis
  • High-quality OpenGL-based graphics
  • Color or B/W vector, contour, 3D surface & particle plots
  • Moving and stationary probes
  • Measurement baffles
  • Arbitrary sampling volumes
  • Force & moment output
  • Animation output
  • PostScript, JPEG & Bitmap output
  • Streamlines
  • Flow tracers
User Conveniences
  • Active simulation control (based on measurement of probes)
  • Mesh generators
  • Mesh quality checking
  • Tabular time-dependent input using external files
  • Automatic time-step control for accuracy & stability
  • Automatic convergence control
  • Mentor help to optimize efficiency
  • Change simulation parameters while solver runs
  • Launch and manage multiple simulations
  • Automatic simulation termination based on user-defined criteria
  • Run simulation on remote servers using remote solving
Multi-Processor Computing

FLOW-3D Features

The features in blue are newly-released in FLOW-3D v12.0.

Meshing & Geometry

  • Structured finite difference/control volume meshes for fluid and thermal solutions
  • Finite element meshes in Cartesian and cylindrical coordinates for structural analysis
  • Multi-Block gridding with nested, linked, partially overlapping and conforming mesh blocks
  • Conforming meshes extended to arbitrary shapes
  • Fractional areas/volumes (FAVOR™) for efficient & accurate geometry definition
  • Closing gaps in geometry
  • Mesh quality checking
  • Basic Solids Modeler
  • Import CAD data
  • Import/export finite element meshes via Exodus-II file format
  • Grid & geometry independence
  • Cartesian or cylindrical coordinates

Flow Type Options

  • Internal, external & free-surface flows
  • 3D, 2D & 1D problems
  • Transient flows
  • Inviscid, viscous laminar & turbulent flows
  • Hybrid shallow water/3D flows
  • Non-inertial reference frame motion
  • Multiple scalar species
  • Two-phase flows
  • Heat transfer with phase change
  • Saturated & unsaturated porous media

Physical Modeling Options

  • Fluid structure interaction
  • Thermally-induced stresses
  • Plastic deformation of solids
  • Granular flow
  • Moisture drying
  • Solid solute dissolution
  • Sediment transport and scour
  • Sludge settling
  • Cavitation (potential, passive tracking, active tracking)
  • Phase change (liquid-vapor, liquid-solid)
  • Surface tension
  • Thermocapillary effects
  • Wall adhesion
  • Wall roughness
  • Vapor & gas bubbles
  • Solidification & melting
  • Mass/momentum/energy sources
  • Shear, density & temperature-dependent viscosity
  • Thixotropic viscosity
  • Visco-elastic-plastic fluids
  • Elastic membranes & walls
  • Evaporation residue
  • Electro-mechanical effects
  • Dielectric phenomena
  • Electro-osmosis
  • Electrostatic particles
  • Joule heating
  • Air entrainment
  • Molecular & turbulent diffusion
  • Temperature-dependent material properties
  • Spray cooling

Flow Definition Options

  • General boundary conditions
    • Symmetry
    • Rigid and flexible walls
    • Continuative
    • Periodic
    • Specified pressure
    • Specified velocity
    • Outflow
    • Outflow pressure
    • Outflow boundaries with wave absorbing layers
    • Grid overlay
    • Hydrostatic pressure
    • Volume flow rate
    • Non-linear periodic and solitary surface waves
    • Rating curve and natural hydraulics
    • Wave absorbing layer
  • Restart from previous simulation
  • Continuation of a simulation
  • Overlay boundary conditions
  • Change mesh and modeling options
  • Change model parameters

Thermal Modeling Options

  • Natural convection
  • Forced convection
  • Conduction in fluid & solid
  • Fluid-solid heat transfer
  • Distributed energy sources/sinks in fluids and solids
  • Radiation
  • Viscous heating
  • Orthotropic thermal conductivity
  • Thermally-induced stresses

Numerical Modeling Options

  • TruVOF Volume-of-Fluid (VOF) method for fluid interfaces
  • Steady state accelerator for free-surface flows
  • First and second order advection
  • Sharp and diffuse interface tracking
  • Implicit & explicit numerical methods
  • Immersed boundary method
  • GMRES, point and line relaxation pressure solvers
  • User-defined variables, subroutines & output
  • Utilities for runtime interaction during execution

Fluid Modeling Options

  • One incompressible fluid – confined or with free surfaces
  • Two incompressible fluids – miscible or with sharp interfaces
  • Compressible fluid – subsonic, transonic, supersonic
  • Stratified fluid
  • Acoustic phenomena
  • Mass particles with variable density or diameter

Shallow Flow Models

  • General topography
  • Raster data interface
  • Subcomponent-specific surface roughness
  • Wind shear
  • Ground roughness effects
  • Manning’s roughness
  • Laminar & turbulent flow
  • Sediment transport and scour
  • Surface tension
  • Heat transfer
  • Wetting & drying

Turbulence Models

  • RNG model
  • Two-equation k-epsilon model
  • Two-equation k-omega model
  • Large eddy simulation

Advanced Physical Models

  • General Moving Object model with 6 DOF–prescribed and fully-coupled motion
  • Rotating/spinning objects
  • Collision model
  • Tethered moving objects (springs, ropes, breaking mooring lines)
  • Flexing membranes and walls
  • Porosity
  • Finite element based elastic-plastic deformation
  • Finite element based thermal stress evolution due to thermal changes in a solidifying fluid
  • Combusting solid components

Chemistry Models

  • Stiff equation solver for chemical rate equations
  • Stationary or advected species

Porous Media Models

  • Saturated and unsaturated flow
  • Variable porosity
  • Directional porosity
  • General flow losses (linear & quadratic)
  • Capillary pressure
  • Heat transfer in porous media
  • Van Genunchten model for unsaturated flow

Discrete Particle Models

  • Massless marker particles
  • Multi-species material particles of variable size and mass
  • Solid, fluid, gas particles
  • Void particles tracking collapsed void regions
  • Non-linear fluid-dynamic drag
  • Added mass effects
  • Monte-Carlo diffusion
  • Particle-fluid momentum coupling
  • Coefficient of restitution or sticky particles
  • Point or volumetric particle sources
  • Initial particle blocks
  • Heat transfer with fluid
  • Evaporation and condensation
  • Solidification and melting
  • Coulomb and dielectric forces
  • Probe particles

Two-Phase & Two-Component Models

  • Liquid/liquid & gas/liquid interfaces
  • Variable density mixtures
  • Compressible fluid with a dispersed incompressible component
  • Drift flux with dynamic droplet size
  • Two-component, vapor/non-condensable gases
  • Phase transformations for gas-liquid & liquid-solid
  • Adiabatic bubbles
  • Bubbles with phase change
  • Continuum fluid with discrete particles
  • Scalar transport
  • Homogeneous bubbles
  • Super-cooling
  • Two-field temperature

Coupling with Other Programs

  • Geometry input from Stereolithography (STL) files – binary or ASCII
  • Direct interfaces with EnSight®, FieldView® & Tecplot® visualization software
  • Finite element solution import/export via Exodus-II file format
  • PLOT3D output
  • Neutral file output
  • Extensive customization possibilities
  • Solid Properties Materials Database

Data Processing Options

  • State-of-the-art post-processing tool, FlowSight™
  • Batch post-processing
  • Report generation
  • Automatic or custom results analysis
  • High-quality OpenGL-based graphics
  • Color or B/W vector, contour, 3D surface & particle plots
  • Moving and stationary probes
  • Visualization of non-inertial reference frame motion
  • Measurement baffles
  • Arbitrary sampling volumes
  • Force & moment output
  • Animation output
  • PostScript, JPEG & Bitmap output
  • Streamlines
  • Flow tracers

User Conveniences

  • Active simulation control (based on measurement of probes)
  • Mesh generators
  • Mesh quality checking
  • Tabular time-dependent input using external files
  • Automatic time-step control for accuracy & stability
  • Automatic convergence control
  • Mentor help to optimize efficiency
  • Units on all variables
  • Custom units
  • Component transformations
  • Moving particle sources
  • Change simulation parameters while solver runs
  • Launch and manage multiple simulations
  • Automatic simulation termination based on user-defined criteria
  • Run simulation on remote servers using remote solving
  • Copy boundary conditions to other mesh blocks

Multi-Processor Computing

  • Shared memory computers
  • Distributed memory clusters

FlowSight

  • Particle visualization
  • Velocity vector fields
  • Streamlines & pathlines
  • Iso-surfaces
  • 2D, 3D and arbitrary clips
  • Volume render
  • Probe data
  • History data
  • Vortex cores
  • Link multiple results
  • Multiple data views
  • Non-inertial reference frame
  • Spline clip

MEMS/WELD 분야

Microfluidics

Microfluidics는 집적 회로 산업에서 사용되는 것과 유사한 공정을 사용하여 소형 기기의 제조에 급격하게 성장하는 기술입니다. Microfluidics 기술은 0.1 미크론에서 1mm에 이르기까지 매우 작은 장치로 기계, 유체, 광학, 전자 기능을 통합 할 수있는 방법을 제공합니다. Microfluidics는 기존의 방법과 비교하면 두 가지 중요한 장점이 있습니다. 첫째, 대량으로 제조 될 수 있으므로, 생산의 비용이 실질적으로 감소 될 수 있습니다. 둘째, 집적 회로에 통합 될 수 있어서 다른 기술보다 훨씬 더 복잡한 시스템으로 제조 될 수 있습니다.

Chip packaging simulation. Results generated by FLOW-3D/MP, FLOW-3D‘s HPC solution.

엔지니어 및 과학자가 설계, 시험 제작하고 그 성능을 최적화하기 위해 장치를 재 설계하는 등, 다른 제조 방법에서와 같이 microfluidics 설계 프로세스는 매우 고가 일 수 있습니다. 그러나, 수치 시뮬레이션은 전자, 기계, 화학, 열 과학 및 유체 과학 등의 분야에 걸쳐 정량 분석과 중요한 통찰력을 제공 할 수 있습니다.

laser-sintering

 

자동차 분야

Automotive

Nozzle filling simulation. Courtesy Reutter Group

FLOW-3D는 자동차 산업에서 직면할 수 있는 많은 문제에 대한 해법을 제공하는 포괄적인 CFD 소프트웨어입니다. FLOW-3D는 과도적인 흐름 동역학(자유 표면과 한정된 유체 모두), 유체와 고체 간의 열전달, 상 변화, 고체의 6자유도 운동, 기계적 및 열로 유도된 응력에 대한 결합된 유한 요소 해석 등을 할 수 있습니다. 자세한 내용은 FLOW-3D의 모델링 기능의 전체 목록을 살펴보십시오.

자동차 분야의 시뮬레이션 대상 분야로는 연료 탱크 슬로 싱, 언더 후드 열 관리, 분사 제어, 조기 연료 차단, 자동차 부품의 도장, 용기의 가스 제거, 파워 트레인 부품의 유체 저항, 자동차 부품 주조 등의 주조품 및 주조 공정의 더 나은 설계를 위해 도움을 줄 수 있는 몇 가지 영역들이 있습니다.

자동차분야 해석 사례


관련 기술자료

Figure 1. Steady-state shear stress a as a function of shear rate y in Sn-Pb alloy [10).

Numerical Modelling of Semi-Solid Flow under Processing Conditions

처리조건에서의 반고체유동의 수치모델링 David H. Kirkwood and Philip J. WardDepartment of Engineering Materials, University of Sheffield, Sheffield I UK Keywords: ...
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Fig. 2 Temperature distributions of oil pans (Cycling)

내열마그네슘 합금을 이용한 자동차용 오일팬의 다이캐스팅 공정 연구

A Study on Die Casting Process of the Automobile Oil Pan Using the Heat Resistant Magnesium Alloy 한국자동차공학회논문집 = Transactions ...
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Fig. 1.Schematic of wire feeding in a melting line.

Evaluation on the Efficiency of Cored Wire Feeding in Addition of Alloying Elements into Cu Melt

Bok-Hyun Kang*, Ki-Young KimKorea University of Technology and Education 코어드 와이어 피딩에 의한 Cu 용탕에의 합금 첨가 시 효율 평가 ...
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Fig. 6: Proposed Pattern Layout

Casting Defect Analysis on Caliper Bracket using Mold flow Simulation

금형 흐름 시뮬레이션을 사용한 캘리퍼 브래킷의 주조 결함 분석 Abstract 이 작업에서는 컴퓨터 보조 주조 시뮬레이션 기술을 사용하여 Green sand ...
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Figure 4.9 Flow analysis results using FLOW3D of the metal flow and solidification in the main cavity. (The velocity is in m/s.)

Numerical Analysis of Die-Casting Process in Thin Cavities Using Lubrication Approximation

Alexandre ReikherA Dissertation Submitted inPartial Fulfillment of theRequirements for the Degree ofDoctor of PhilosophyIn EngineeringatThe University of Wisconsin MilwaukeeDecember 2012 ...
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Figure 2.12: (Top) The sequence in the DISAMATIC process (1)-(5). (Middle) The performed experiments placed on the Mohr circle (I)-(V). (Bottom) The five names of the mechanical behaviours.

Numerical simulation of flow and compression of green sand

Abstract 산업 박사 프로젝트의 초점은 주조 부품에 최종 기하학적 모양을 제공하는 모래 주형 (녹색 모래)의 생산에 집중되었습니다. 주조 부품의 고품질을 ...
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Figure 9: Predicted three-dimensional spreading splats for a 90 µm diameter Nylon-11 droplet.

Effect of Substrate Roughness on Splatting Behavior of HVOF Sprayed Polymer Particles: Modeling and Experiments

International Thermal Spray Conference – ITSC-2006Seattle, Washington, U.S.A., May 2006 M. Ivosevic, V. Gupta, R. A. Cairncross, T. E. Twardowski, ...
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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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연료 탱크 슬로싱

시뮬레이션 사례 설명 이 예는 제트 전투기 연료 탱크 내 연료 슬로싱을 나타냅니다. 이 시뮬레이션을 통해 엔지니어는 탱크 내 연료 ...
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코어 가스(Core Gas)

코어 가스(Core Gas)   코어로 주조 모델링 (Modeling Castings with Cores) 모래 속의 화학 결합제는 용융 된 금속에 의해 가열 ...
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항공/우주 분야

Aerospace

항공 우주 분야에서 연구하는 엔지니어를 위해 FLOW-3D는 정확한 액체/가스 인터페이스(자유 표면) 모델링, 열 솔루션을 사용하여 연료 안정성 확보, 극저온 온도 조절, PMD(Propellent management devices), 캐비테이션 및 전하 분포에 대한 귀중한 통찰력을 제공합니다. 위상 및 정전기 물리 모델을 사용합니다.

항공 우주 분야에서 FLOW-3D의 성공적인 사용을 보여주는 기술 문서로 이동하기

Aerospace Simulations

FLOW-3D sloshing, 무중력 유체역학(zero gravity fluid dynamics), 다상유동(multi-phase fluids), 탄성 멤브레인(elastic membranes), 음속 및 초음속 상태에서 노즐(nozzles in subsonic and supersonic conditions), 유체구조의 상호 작용(fluid structure interactions) 등 항공분야에서 볼 수 있는 자연현상을 정확하게 표현하기 위해 자유표면 알고리즘을 고려하고 있습니다.

Bibliography

Models

Conference Proceedings


관련 기술자료

planar representation (cross-section at tank centre).

Analysis of cryogenic propellant behaviour in microgravity and low thrust environments*

미세 중력 및 저 추력 환경에서 극저온 추진체 거동 분석 M.F. Fisher, G.R. Schmidt and J.J. MartinNASA Marshall Space Flight ...
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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 ...
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Capsule-type Vane Tank

Numerical Simulation Analysis of Liquid Transportation in Capsule-type Vane Tank under Microgravity

Microgravity 하에서 캡슐형 베인 탱크의 액체 수송에 대한 수치 시뮬레이션 분석 Abstract Li Yong-Qiang1,2 & Dong Jun-Yan1 & Rui Wei1Received: ...
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FIGURE 1. - FLOW-3D MODEL OF K-SITE TANK PRESSUR-IZATION.

Prediction of the Ullage Gas Thermal Stratification in a NASP Vehicle Propellant Tank Experimental Simulation Using FLOW-3D

FLOW-3D를 사용한 NASP 차량 추진 탱크 실험 시뮬레이션에서 Ullage 가스 열 층화 예측 Personal AuthorHardy, T. L.; Tomsik, T. M ...
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Comparison of FLOW-3D calculations with very large amplitude slosh data

매우 큰 진폭 슬로시 데이터와 FLOW-3D 계산 비교 소속 표시 : Sicilian, J. M.;Tegart, J. R. Abstract 액체 모션 및 ...
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Figure 3-3 E200U+ PMD sponge draining

CORRELATIONS BETWEEN NEUTRAL BUOYANCY TESTS AND CED

Abstract NEUTRAL BUOYANCY 테스트는 표면 장력 장치의 기능 검증을 위해 Matra Marconi Space에서 잘 알려진 테스트 기능입니다. 새로운 Eurostar 3000 ...
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FIG. 4: The RF of a lopsided neckpinch geometry through the Type-1 singularity using surgery and yielding the geometry as a direct product of two 3-spheres. We use axial symmetry of our model to suppress one dimension and the resulting two-lobed geometry can be visualized in Euclidean 3-space (our evolution was fortunately isometrically embeddable in R 3 ). The middle 3’rd and 4’th figure occur at the same time (t = 183.0) in the evolution. They illustrate the explicit manifold surgery, where the spherical caps (two icosahedrons )are placed on the ends of the left and right lobes. This is the first numerical illustration of Thurston’s geometrization procedure that we are aware of. This surface has 3438 edges, 1580 triangle-based frustum blocks and 960 vertices, although symmetry reduces the number of edges to 80 icosahedral {si} edges and 79 axial {ai} edges.

A Realization of Thurstons Geometrization: Discrete Ricci Flow with Surgery∗

Paul M. Alsing1, Warner A. Miller2† & Shing-Tung Yau31 Air Force Research Laboratory, Information Directorate, Rome, NY 134412 Department of ...
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Figure 10.—Temperature contour time sequence for an EDS scale propellant tank at a jet mixing velocity of 0.06 m/s.

Computational Fluid Dynamics (CFD) Simulations of Jet Mixing in Tanks of Different Scales

NASA/TM—2010-216749 Kevin Breisacher and Jeffrey ModerGlenn Research Center, Cleveland, Ohio Prepared for the57th Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Meetingsponsored by ...
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aerospace-sloshing-simulation

Aerospace Sloshing Dynamics

Sloshing Dynamics 우주선의 연료 탱크에서 추진체의 움직임에 대한 지식은 작동 및 성능의 다양한 측면을 이해하는 데 필수적입니다. 추진체 운동은 액체 ...
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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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코팅분야

Coating

FLOW-3D는 산업계 및 학계의 코팅 연구원들이 기계 설계 연구, Display 공정개발 및 최적화를 위해 사용했습니다. 미크론 규모의 코팅 물리학을 이해하는 것은 코팅 유체 유변학의 복잡한 특성과 기판 및 Die와의 상호 작용으로 인해 어려울 수 있습니다.

FLOW-3D 는 비용이 많이 드는 실제 실험에 의존하지 않고, 코팅 프로세스를 분석할 수 있는 편리한 방법을 제공합니다. FLOW-3D는 표면 장력, Wall 접착, 용액 운반, 밀도 기반 흐름 및 상 변화의 영향을 이해하기위한 고밀도 모델링을 제공합니다.

Forward roll coating 공정에 대한 FLOW-3D의 시뮬레이션은 high capillary number수로 인한ribbing 결함을 포착합니다. 이 모델은 backing rollers가 400 micron nip을 통해 유체를 끌어 당길 때 표면 장력과 점도의 효과를 통합합니다. 시뮬레이션은 Lee, et al [1]의 연구를 기반으로합니다.

ribbing 시작에 대한 정확한 예측을 통해 엔지니어는 결함을 방지하기 위한 공정 매개 변수를 식별하고 수정할 수 있습니다.

Reference

[1] Lee, J. H., Han, S. K., Lee, J. S., Jung, H. W., & Hyun, J. C. (2010). Ribbing instability in rigid and deformable forward roll coating flows. Korea Australia Rheology Journal, 22(1), 75-80.

Bibliography

Models

Conference Proceedings


관련 기술자료

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 ZhenyanXiaaYangZhaoaZhenYangabcChengjuanYangabLinanLiaShibinWangaMengWangabaSchool of Mechanical Engineering, ...
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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), ...
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Figure 4. Calculate and simulate the injection of water in a single-channel injection chamber with a nozzle diameter of 60 μm and a thickness of 50 μm, at an operating frequency of 5 KHz, in the X-Y two-dimensional cross-sectional view, at 10, 20, 30, 40 and 200 μs.

DNA Printing Integrated Multiplexer Driver Microelectronic Mechanical System Head (IDMH) and Microfluidic Flow Estimation

DNA 프린팅 통합 멀티플렉서 드라이버 Microelectronic Mechanical System Head (IDMH) 및 Microfluidic Flow Estimation by Jian-Chiun Liou 1,*,Chih-Wei Peng 1,Philippe ...
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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 ...
더 보기
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 ...
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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 ...
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(a) Moving Reference Frame

Study on Swirl and Cross Flow of 3D-Printed Rotational Mixing Vane in 2×3 Subchannel

A thesis/dissertationsubmitted to the Graduate School of UNISTin partial fulfillment of therequirements for the degree ofMaster of ScienceHaneol Park07/09/2019Approved by_________________________AdvisorIn ...
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Figure 2.6 ESI apparatus for offline analysis with microscope imaging.

MODELING AND CHARACTERIZATION OF MICROFABRICATED EMITTERS: IN PURSUIT OF IMPROVED ESI-MS PERFORMANCE

미세 가공 방사체의 모델링 및 특성화 : 개선된 ESI-MS 성능 추구 by XINYUN WU A thesis submitted to the Department ...
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Figure 9: Predicted three-dimensional spreading splats for a 90 µm diameter Nylon-11 droplet.

Effect of Substrate Roughness on Splatting Behavior of HVOF Sprayed Polymer Particles: Modeling and Experiments

International Thermal Spray Conference – ITSC-2006Seattle, Washington, U.S.A., May 2006 M. Ivosevic, V. Gupta, R. A. Cairncross, T. E. Twardowski, ...
더 보기
Figure 20. Top: image of electrospray, bottom: cone-jet profile using the CF emitter. Distance between the carbon fiber tip and the counter electrode is 4.0 mm, potential difference is 3500 V, flow rate is 300 nL min−1 .

Modeling and characterization of a carbon fiber emitter for electrospray ionization

A K Sen1, J Darabi1, D R Knapp2 and J Liu21 MEMS and Microsystems Laboratory, Department of Mechanical Engineering,University of ...
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