Dispersed Multiphase Flow Generation using 3D Steerable Convolutional Neural Network

This work deals with recreating particle-resolved fluid flow around a random distribution of particles in a dispersed multiphase setup using Convolutional Neural Networks (\textbf{CNN}s). The considered problem is rotationally invariant about the mean velocity (streamwise) direction. Thus, the objective of our work is to enforce this symmetry using \textbf{SE(3)-equivariant} CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities of SE(3)-equivariant network when it is used in conjunction with physics-based loss terms. Synthetic flow fields that are 75-95{\%} accurate are produced for Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively with careful application of physics-constrained data-driven approach, whose computational cost is more than four orders of magnitude lower compared to an equivalent CFD approach.