Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks [article]

B. Siddani, S. Balachandar, W. C. Moore, Y. Yang, R. Fang
2020 arXiv   pre-print
Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations
more » ... ing over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.
arXiv:2005.05363v1 fatcat:a64v2iimbzg7npti5aor4up2lm