A calibration framework for discrete element model parameters using genetic algorithms

2018 ◽  
Vol 29 (6) ◽  
pp. 1393-1403 ◽  
Author(s):  
Huy Q. Do ◽  
Alejandro M. Aragón ◽  
Dingena L. Schott
Keyword(s):  
Genetic Algorithms ◽  
Element Model ◽  
Discrete Element ◽  
Model Parameters ◽  
Discrete Element Model

Calibration of discrete element model parameters: soybeans

2018 ◽  
Vol 6 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Bhupendra M Ghodki ◽  
Manish Patel ◽  
Rohit Namdeo ◽  
Gopal Carpenter
Keyword(s):  
Element Model ◽  
Discrete Element ◽  
Model Parameters ◽  
Discrete Element Model

Determination of discrete element model parameters for a cohesive soil and validation through narrow point opener performance analysis

2021 ◽  
Vol 213 ◽  
pp. 105123 ◽  
Author(s):  
Kojo Atta Aikins ◽  
Mustafa Ucgul ◽  
James B. Barr ◽  
Troy A. Jensen ◽  
Diogenes L. Antille ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Cohesive Soil ◽  
Element Model ◽  
Discrete Element ◽  
Model Parameters ◽  
Discrete Element Model

scholarly journals Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials

2020 ◽  
Vol 123 (2) ◽  
pp. 717-737 ◽  
Author(s):  
Rui Chen ◽  
Yong Wang ◽  
Ruitao Peng ◽  
Shengqiang Jiang ◽  
Congfang Hu and Ziheng Zhao
Keyword(s):  
Brittle Materials ◽  
Element Model ◽  
Discrete Element ◽  
Model Parameters ◽  
Discrete Element Model

scholarly journals Scaling of discrete element model parameters for cohesionless and cohesive solid

2016 ◽  
Vol 293 ◽  
pp. 130-137 ◽  
Author(s):  
Subhash C. Thakur ◽  
Jin Y. Ooi ◽  
Hossein Ahmadian
Keyword(s):  
Element Model ◽  
Discrete Element ◽  
Model Parameters ◽  
Discrete Element Model

scholarly journals An Effective Inverse Procedure for Identifying DEMParameters of Rock-Like Materials

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Rui Chen ◽  
Jisheng Li ◽  
Yangqing Qian ◽  
Ruitao Peng ◽  
Shengqiang Jiang ◽  
...  
Keyword(s):  
Inverse Operator ◽  
Optimization Procedure ◽  
Element Model ◽  
Discrete Element ◽  
Calculation Model ◽  
Orthogonal Experimental Design ◽  
Model Parameters ◽  
Discrete Element Model ◽  
Ant Colony Optimization Algorithm ◽  
Inverse Procedure

This study aims to identify discrete element model parameters of rock-like materials. An inverse procedure is developed to determine the discrete element model parameters from experimental measurements. This involves the solution of an inverse problem through minimizing the misfit function which describes the error between numerical computation and experiment by an optimization procedure. In this procedure, the discrete element method is adopted as the numerical calculation method of the forward problem. The orthogonal experimental design is used for parameter sensitivity analysis. Besides, the approximation model with radial basis function is adopted instead of the actual calculation model to reduce the time of forward calculation. The ant-colony optimization algorithm is employed as the inverse operator. Therefore, the parameters of the discrete element model are optimized by this procedure. The three-point bending experiment with discrete element simulation is provided to verify the validity and accuracy of the inversion results. The results indicate that it can rapidly obtain the available and reliable model parameters just through a few sets of experimental data. As a result, this inverse procedure can be applied more widely to parameter identification of the discrete element model for brittle materials.

Parameter Determination of Discrete Element Model of Conditioned Soil by Genetic Neural Network

2010 ◽  
Vol 150-151 ◽  
pp. 27-31
Author(s):  
Li Wu ◽  
Tian Min Guan ◽  
Fu Zheng Qu ◽  
Shou Ju Li
Keyword(s):  
Neural Network ◽  
Triaxial Test ◽  
Element Model ◽  
Discrete Element ◽  
Triaxial Tests ◽  
Model Parameters ◽  
Discrete Element Model ◽  
Discrete Element Simulation ◽  
Element Simulation ◽  
Genetic Neural Network

The inversion method combining the genetic neural network and the discrete element simulation of triaxial tests is firstly described for determining the discrete element model parameters of the conditioned soil. The purpose is to make the error of the simulation curves and the laboratory curves of the triaxial test minimum. The solve approach is the parameters identification based on the genetic neural network. The network training sample is provided by the discrete element simulation. The input sample is the simulation curves of triaxial test, and the output sample is the model parameters. The laboratory triaxial test curves of the conditioned soil are used to determine its model parameters. The simulation curves calculated with the inversed parameters match the laboratory curves well, which illustrate that the discrete element model can accurately predict the deformation characteristics and flow patterns of conditioned soils.

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