A machine learning-based simplified collision model for granular flows

Abstract

This study aims to create an efficient, rapid, and reliable particle collision model utilizing machine learning techniques for granular flow simulations. A simplified surrogate collision model developed in the framework of a Hybrid Euler–Lagrange (HEL) technique was successfully applied to model particle interactions for flows with a low fraction of the granular phase. The precision of the simplified collision model was evaluated using experimental data obtained from the in-house, two-stream particle collision test rig, focusing on solid phase velocity profiles. The implemented model demonstrates strong concordance with the experimental results. The simulations carried out highlight the relation between the simulation time step and the collision rate, which affects the cost of the numerical simulation. The execution time for both the conventional Discrete Element Method (DEM) on a CPU and the streamlined collision HEL model saw a reduction exceeding 70%.