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

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.

Automated summary

The study introduces a machine learning approach for simulating fluid flow around a random distribution of spherical particles using a generative adversarial network and convolutional neural network. The model was tested for various Reynolds numbers and particle volume fractions, showing promising performance across the studied cases.