基于数据驱动的非球形散体颗粒休止角智能建模方法

Data driven intelligent modeling method for angle of repose of non-spherical discrete particles

针对离散单元法(DEM)仿真非球形散体颗粒休止角计算量大、耗时长的问题,本文基于DEM历史仿真数据,采用数据驱动的智能建模方法—BP、RBF神经网络建立非球形散体颗粒的休止角模型,并与传统克里金回归方法进行比较。结果表明,智能模型的运算速度相比DEM计算速度有很大提升;智能模型相比传统克里金回归模型具有更佳的预测性能,其中BP神经网络模型综合性能最优。最后,采用BP神经网络模型分析颗粒形状及摩擦因数对休止角的影响,发现休止角随颗粒形状变量、摩擦因数的增加都呈现增大的趋势,与现有研究结果一致,进一步证明了智能模型进行休止角预测的可靠性。

The discrete element method(DEM)simulation of the angle of repose(AoR)of non-spherical is computationally intensive and time consuming.Based on the obtained DEM simulation data,the data driven intelligent modeling methods—the BP neural network and RBF neural networ were used to model the AoR of non-spherical discrete particles,and were compared with the traditional Kriging regression methods.The results show that the speed of the intelligent models is dramatically faster than the speed of the DEM simulation;the intelligent model has better predictive performance than the traditional Kriging regression model,and the BP neural network model has the best overall performance.Finally,based on the BP neural network model,the influences of particle shape and friction coefficient on the AoR were analyzed.It is found that the AoR increases with the increase of particle shape variable and friction coefficient,which further indicates the credibility of the intelligent model.