论文标题
数据驱动的颗粒介质模型订单降低
Data-driven model order reduction for granular media
论文作者
论文摘要
我们研究了减少阶建模以更高速度运行离散元件模拟的使用。采用数据驱动的方法，我们提前运行许多离线模拟并训练模型，以预测质量分布和系统控制信号的速度场。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激烈过程。在耦合的DEM和多体系统模拟中，可以训练预测模型，以输出界面反作用力。研究了一种自适应模型订购技术，将培养基分解为固体，液体和气态状态的介质。模型还原应用于固体和液体结构域，其中粒子运动与平均流程密切相关，而已解析的DEM则用于气态结构域。使用脊回归预测指标，对桩排放和推土机的模拟进行了测试。测得的精度分别约为90％和65％，速度范围在10到60之间。
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.
