Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions

Abstract

Solid-fluid multiphase systems are ubiquitous in many chemical, pharmaceutical, and energy based applications. These flows are challenging to study experimentally, thus several numerical simulation techniques have been developed. In this paper we use Particle Resolved Simulations (PRS) combined with Deep Learning (DL) to predict drag forces on each particle in suspensions of ellipsoidal particles for use in lower fidelity Euler-Lagrange and Euler-Euler simulations. Suspensions with solid fraction of 0.1, 0.2 and 0.3 are investigated at Reynolds numbers of 10, 50, 100 and 200. A UNet architecture is used to predict the velocity and pressure fields and a CNN is used to predict the drag forces. It is shown that drag force predictions using the predicted velocity and pressure fields give more accurate results than predictions that only use geometric information.