Deep Learning for flow field and drag force predictions in dispersed particle flows

Accurate modeling of the flow field and drag forces in fluid-particle systems in Euler-Lagrange and Euler-Euler methods is essential for lab-scale and industry-scale simulations. In this work, we utilize deep learning to predict pressure and velocity fields in multiphase flows with dispersed prolate ellipsoidal particles of aspect ratio 2.5 generated by Particle Resolved Simulations at different Reynolds numbers and solid fractions. A 3D U-Net based model is used, which given the spatial information of an ellipsoid’s neighboring region in the form of a distance function, is trained to generate the pressure and velocity fields. We additionally introduce a novel method to perform super-resolution in the predicted domain near the surface of the particle. The trained model is also tested on its ability to generalize on unseen datasets, and the flow field predictions are then evaluated on their ability to predict particle drag forces. We show that this method of predicting the pressure and velocity field first before performing the downstream task of drag force prediction is more accurate than training the model to predict drag forces directly using the distance function input. We also show the predicted drag forces perform better than correlations being used by CFD-DEM.