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Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows.
2002
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Hydrodynamic and transport properties of packed beds in small tube-to-sphere diameter ratio: pore scale simulation using an eulerian and a lagrangian approach,
P. Magnico, · 2003
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2003
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An immersed boundary method with direct forcing for the simulation of particulate flows,
M. Uhlmann, · 2005
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Drag force of intermediate reynolds number flow past mono- and bidisperse arrays of spheres,
R. Beetstra, M. A. van der Hoef, J. A. M. Kuipers, · 2007
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C. Rycroft, · 2009
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Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres,
S. Tenneti, R. Garg, S. Subramaniam, · 2011
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Lattice-boltzmann simulation of fluid flow through packed beds of uniform spheres: Effect of porosity,
L. Rong, K. Dong, A. Yu, · 2013
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A new relation of drag force for high stokes number monodisperse spheres by direct numerical simulation,
A. A. Zaidi, T. Tsuji, T. Tanaka, · 2014
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Drag correlation for dilute and moderately dense fluid-particle systems using the lattice boltzmann method,
S. Bogner, S. Mohanty, U. Rüde, · 2014
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2014
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D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, 2014. arXiv:1412.6980
2014
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Immersed boundary method with non-uniform distribution of lagrangian markers for a non-uniform eulerian mesh,
G. Akiki, S. Balachandar, · 2015
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Direct numerical simulation of moderate-reynolds-number flow past arrays of rotating spheres,
Q. Zhou, L.-S. Fan, · 2015
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A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres,
Y. Tang, E. A. J. F. Peters, J. A. M. Kuipers, S. H. L. Kriebitzsch, M. A. van der Hoef, · 2015
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2015
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Convolutional neural networks for steady flow approximation,
X. Guo, W. Li, F. Iorio, · 2016
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Computing curvature for volume of fluid methods using machine learning,
Y. Qi, J. Lu, R. Scardovelli, S. Zaleski, G. Tryggvason, · 2018
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Tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow,
Y. Xie, E. Franz, M. Chu, N. Thuerey, · 2018
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2018
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Turbulence modeling in the age of data,
K. Duraisamy, G. Iaccarino, H. Xiao, · 2019
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Prediction of aerodynamic flow fields using convolutional neural networks,
S. Bhatnagar, Y. Afshar, S. Pan, K. Duraisamy, S. Kaushik, · 2019
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Physics-informed neural networks for high-speed flows,
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2016
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Force variation within arrays of monodisperse spherical particles,
G. Akiki, T. L. Jackson, S. Balachandar, · 2016
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Pairwise interaction extended point-particle model for a random array of monodisperse spheres,
G. Akiki, T. L. Jackson, S. Balachandar, · 2016
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2016
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2016
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2017
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Pairwise-interaction extended point-particle model for particle-laden flows,
G. Akiki, W. Moore, S. Balachandar, · 2017
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Z. Mao, A. D. Jagtap, G. E. Karniadakis, · 2019
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A hybrid point-particle force model that combines physical and data-driven approaches,
W. Moore, S. Balachandar, G. Akiki, · 2019
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Lagrangian investigation of pseudo-turbulence in multiphase flow using superposable wakes,
W. C. Moore, S. Balachandar, · 2019
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Improving generalization and stability of generative adversarial networks,
H. Thanh-Tung, T. Tran, S. Venkatesh, · 2019
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A high-bias, low-variance introduction to machine learning for physicists,
P. Mehta, M. Bukov, C.-H. Wang, A. G. Day, C. Richardson, C. K. Fisher, D. J. Schwab, · 2019
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Pytorch: An imperative style, high-performance deep learning library,
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, · 2019
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Machine learning for fluid mechanics,
S. L. Brunton, B. R. Noack, P. Koumoutsakos, · 2020
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