Probabilistic Calibration of a Discrete Particle Model
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A discrete element model (DEM) capable of reproducing the mechanistic behavior of a triaxial compressive test performed on a Vosges sandstone specimen is presented considering similar experimental testing conditions and densely packed spherical elements with low lock-in stress. The main aim of this paper is to illustrate the calibration process of the model‟s micro-parameters when obtained from the experimental meso-parameters measured in the lab. For this purpose, a probabilistic inverse method is introduced to fully define the micro-parameters of the particle models through a joint probability density function, which is conditioned on the experimental observations obtained during a series of tests performed at the L3S-R France. The DEM captures successfully some of the rock mechanical behavior features, including the global stress-strain and failure mechanisms. Results include a detailed parametric analysis consisting of varying each DEM parameter at the time and measuring the model response on the strain-stress domain. First order statistics on probabilistic results show the adequacy of the model to capture the experimental data, including a measure of the DEM performance for different parameter combinations. Also, joint probability density functions and cross-correlations among the micro-parameters are presented.
discrete element modeling
Zhang, Yanbei (2010). Probabilistic Calibration of a Discrete Particle Model. Master's thesis, Texas A&M University. Available electronically from