1. Automated discrete element method calibration using genetic and optimization algorithms
- Author
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Do, H.Q., Aragon, A.M., Schott, D.L., Radjai, F., Nezamabadi, S., Luding, S., and Delenne, J.Y.
- Subjects
Optimization algorithm ,Computer science ,Physics ,QC1-999 ,Rolling resistance ,Model parameters ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Granular material ,Discrete element method ,Computational science ,Test case ,020401 chemical engineering ,Robustness (computer science) ,0204 chemical engineering ,0210 nano-technology ,Algorithm - Abstract
This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental set-ups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different set-ups.
- Published
- 2017