Automated discrete element method calibration using genetic and optimization algorithms

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, ar...

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Published inEPJ Web of Conferences Vol. 140; p. 15011
Main Authors Do, Huy Q., Aragón, Alejandro M., Schott, Dingena L.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2017
Subjects
Algorithms
Automation
Calibration
Discrete element method
Genetic algorithms
Granular materials
Mathematical models
Nonlinear programming
Optimization
Parameter identification
Robustness (mathematics)
Rolling resistance
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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.
AbstractList 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.
Author Aragón, Alejandro M.
Schott, Dingena L.
Do, Huy Q.
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SubjectTerms Algorithms
Automation
Calibration
Discrete element method
Genetic algorithms
Granular materials
Mathematical models
Nonlinear programming
Optimization
Parameter identification
Robustness (mathematics)
Rolling resistance
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Title Automated discrete element method calibration using genetic and optimization algorithms
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