EPJ Web Conf.
Volume 140, 2017Powders and Grains 2017 – 8th International Conference on Micromechanics on Granular Media
|Number of page(s)||4|
|Section||Particle simulations and particle-based methods|
|Published online||30 June 2017|
Calibration of DEM parameters on shear test experiments using Kriging method
1 Centre SPIN, LGF UMR CNRS 5307, École Nationale Supérieure des Mines de Saint-Étienne, 158 Cours Fauriel, F-42023 Saint-Étienne Cedex 2, France
2 Areva-Melox, B.P. 93124, 30203 Bagnols sur Cèze Cedex, France
* e-mail: firstname.lastname@example.org
Published online: 30 June 2017
Calibration of powder mixing simulation using Discrete-Element-Method is still an issue. Achieving good agreement with experimental results is difficult because time-efficient use of DEM involves strong assumptions. This work presents a methodology to calibrate DEM parameters using Efficient Global Optimization (EGO) algorithm based on Kriging interpolation method. Classical shear test experiments are used as calibration experiments. The calibration is made on two parameters - Young modulus and friction coefficient. The determination of the minimal number of grains that has to be used is a critical step. Simulations of a too small amount of grains would indeed not represent the realistic behavior of powder when using huge amout of grains will be strongly time consuming. The optimization goal is the minimization of the objective function which is the distance between simulated and measured behaviors. The EGO algorithm uses the maximization of the Expected Improvement criterion to find next point that has to be simulated. This stochastic criterion handles with the two interpolations made by the Kriging method : prediction of the objective function and estimation of the error made. It is thus able to quantify the improvement in the minimization that new simulations at specified DEM parameters would lead to.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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