Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks

被引：0
|
作者：
B. Siddani
S. Balachandar
W. C. Moore
Y. Yang
R. Fang
机构：
[1] University of Florida,Center for Compressible Multiphase Turbulence
[2] University of Florida,J. Crayton Pruitt Family Department of Biomedical Engineering
来源：
Theoretical and Computational Fluid Dynamics | 2021年 / 35卷
关键词：
Pseudo-turbulence; Multiphase flow prediction; Generative adversarial network (GAN); Convolutional neural network (CNN);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：807 / 830
页数：23
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