EPJ Web Conf.
Volume 140, 2017Powders and Grains 2017 – 8th International Conference on Micromechanics on Granular Media
|Number of page(s)||4|
|Published online||30 June 2017|
Calibration of micromechanical parameters for DEM simulations by using the particle filter
1 Multi Scale Mechanics (MSM), Faculty of Engineering Technology, MESA+, University of Twente, The Netherlands
2 Graduate School of Environmental and Life Science, Okayama University, Japan
3 School of Engineering, The University of Newcastle, Australia
4 Graduate School for International Development and Cooperation, Hiroshima University, Japan
* e-mail: firstname.lastname@example.org
Published online: 30 June 2017
The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as ‘particles’, based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in ‘stress-free’ states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting ‘particles’ with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response.
© 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.