We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60.

我们研究了使用降阶模型以更高的速度运行离散元素模拟。我们采用数据驱动的方法，预先运行了许多离线模拟，并训练了一个模型，以根据质量分布和系统控制信号预测速度场。粒子速度的快速模型推断取代了计算接触力和速度更新的繁琐过程。在耦合DEM和多体系统仿真中，可以训练预测器模型以输出界面反作用力。研究了一种自适应模型降阶技术，将介质分解为固态，液态和气态。模型简化适用于固体和液体域，其中粒子运动与平均流密切相关，而解析的DEM用于气体域。使用山脊回归预测器，在桩卸料和推土的模拟上测试了性能。测得的精度分别约为90％和65％，加速范围在10和60之间。