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.

使用离散粒子方法模拟工业反应器中的粒子-流体系统时，平衡精度和效率至关重要。本文系统回顾了加速离散粒子模拟的方法，包括粗粒度（CG）方法和多尺度耦合方法，并指出了每个类别当前的挑战和困难。本文根据对粒子间碰撞的处理和多尺度耦合的不同，将CG方法分为CG计算流体动力学（CFD）-DEM（计算流体动力学-离散元法）和多相粒子单元法。基于空间和时间耦合总结了方法。尽管这些方法已初步应用于模拟工业反应堆，但仍然面临准确性和适用性方面的挑战。最近，基于机器学习的模拟受到了极大的关注，并可能为离散粒子模拟的加速提供新的见解。我们希望本文能够帮助研究人员理解加速模拟技术的发展，并鼓励该领域新模型的探索。