Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

By Hongyang Cheng, T. Shuku, Klaus Thoeni, Haruyuki Yamamoto. 2018

64 citations recorded by the source corpus.

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.

Subfields: Mechanics of Materials, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/60bcaf9842ee60ee759910a900a64b09281ce807

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In Mechanics of Materials

Citations in the corpus

An iterative Bayesian filtering framework for fast and automated calibration of DEM models (2019, methodology): “The previously developed calibration tool [36] is extended to enable an automated coarse-to-fine search in high-dimensional parameter space, by introducing a resampling step between iterations of nonlinear, non-Gaussian Bayesian filtering.”