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

B. Siddani; S. Balachandar; W. C. Moore; Y. Yang; R. Fang

doi:10.1007/s00162-021-00593-9

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arXiv · 2005.05363

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

B. Siddani·S. Balachandar·W. C. Moore·Y. Yang·R. Fang

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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.

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B. Siddani, S. Balachandar, W. C. Moore, Y. Yang, R. Fang. 2020-05-11. Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks. https://doi.org/10.1007/s00162-021-00593-9

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