Vol.123, No.2, 2020, pp.717-737, doi:10.32604/cmes.2020.07438
OPEN ACCESS
ARTICLE
Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials
• Rui Chen1, 2, Yong Wang1, 2, Ruitao Peng1, 2, *, Shengqiang Jiang1, 2, Congfang Hu1, 2, Ziheng Zhao1, 2
1 School of Mechanical Engineering, Xiangtan University, Xiangtan, 411105, China.
2 Engineering Research Center for Complex Track Processing Technology and Equipment, Xiangtan University, Xiangtan, 411105, China.
* Corresponding Author: Ruitao Peng. Email: pengruitao@xtu.edu.cn.
(This article belongs to this Special Issue: Numerical Modeling and Simulation for Structural Safety and Disaster Mitigation)
Received 23 May 2019; Accepted 10 March 2020; Issue published 01 May 2020
Abstract
An inverse method for parameters identification of discrete element model combined with experiment is proposed. The inverse problem of parameter identification is transmitted to solve an optimization problem by minimizing the distance between the numerical calculations and experiment responses. In this method, the discrete element method is employed as numerical calculator for the forward problem. Then, the orthogonal experiment design with range analysis was used to carry out parameters sensitivity analysis. In addition, to improve the computational efficiency, the approximate model technique is used to replace the actual computational model. The intergeneration projection genetic algorithm (IP-GA) is employed as the optimization algorithm. Consequently, the parameters of the discrete element model are determined. To verify the effectiveness and accuracy of the inverse results, the comparisons of shape deviation experiments with discrete element simulations are provided. It indicates that the effective and reliable discrete element model parameters can be quickly obtained through several sets of experimental data. Hence, this inverse method can be applied more widely to determine the parameters of discrete element model for other materials.
Keywords
Discrete element model, parameter determination, rock-like materials, IP-GA, inverse method.