Performance study of iterative Bayesian filtering to develop an efficient calibration framework for DEM

This work presents an efficient probabilistic framework for the Bayesian calibration of micro-mechanical parameters Discrete Element Method (DEM) modelling. Firstly, superior behaviour iterative filter over sequential Monte Carlo calibrating is shown. The linear contact model with rolling resistance used simulating triaxial responses Toyoura sand under different confining pressures. Secondly, synthetic data from DEM simulations compression are to assess reliability filtering respect user-defined parameters, such as number samples and predefined parameter ranges. Excellent results errors between 1 2% obtained when chosen high enough. It crucial that sample size representative distribution individual within wider ranges, more required. investigation also shows necessity including both stress strain histories, at certain confidence levels, estimation correct mechanical responses, especially fabric responses. Finally, based on findings this a fully-automated open-source tool developed demonstrated selected paths.