Accelerating discrete particle simulation of particle-fluid systems

Abstract

Balancing the accuracy and efficiency is critical when employing the discrete particle method to simulate particle-fluid systems in industrial reactors. This article systematically reviews the methods for accelerating discrete particle simulation, including the coarse-graining (CG) methods and the multiscale coupling methods, and pinpoints current challenges and difficulties in each category. In this work, the CG methods are classified into the CG Computational Fluid Dynamics (CFD)-DEM (computational fluid dynamics-discrete element method) and the multiphase particle-in-cell method according to their treatment of interparticle collisions, and the multiscale coupling methods are summarized based on spatial and temporal coupling. Despite their preliminary application in simulating industrial reactors, these methods still face challenges related to accuracy and applicability. Recently, machine learning-based simulations have gained great attention and may offer new insights into the acceleration of discrete particle simulation. We hope this article can assist researchers in comprehending the development of accelerating simulation techniques and encourage the exploration of novel models in this field.