Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks
Fluid Dynamics
2021-11-02 v1 Computational Physics
Abstract
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 spanning 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.
Cite
@article{arxiv.2005.05363,
title = {Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks},
author = {B. Siddani and S. Balachandar and W. C. Moore and Y. Yang and R. Fang},
journal= {arXiv preprint arXiv:2005.05363},
year = {2021}
}