| Issue |
EPJ Web Conf.
Volume 140, 2017
Powders and Grains 2017 – 8th International Conference on Micromechanics on Granular Media
|
|
|---|---|---|
| Article Number | 15011 | |
| Number of page(s) | 4 | |
| Section | Particle simulations and particle-based methods | |
| DOI | https://doi.org/10.1051/epjconf/201714015011 | |
| Published online | 30 June 2017 | |
https://doi.org/10.1051/epjconf/201714015011
Automated discrete element method calibration using genetic and optimization algorithms
Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands
* Corresponding author: d.l.schott@tudelft.nl
Published online: 30 June 2017
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.
© 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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.