Including stochastics in metamodel-based DEM model calibration

Abstract

In calibration of model parameters for discrete element method (DEM) based models the focus lies on matching the mean key performance indicator (KPI) values from laboratory experiments to those from simulation results. However, due to the stochastic nature of granular processes experimental results can show large variances. To include stochastic behaviour, interpolation-based and regression-based metamodels are trained with stochastic data. These metamodels are used in the standard mean calibration approach and newly introduced mean-variance calibration approach to predict the KPIs mean and variance. In addition, the effect of enriching data on the calibration is investigated up to 50 repetitions of experiments and simulations. Based on a hopper case study, use of regression-based metamodels trained with KPI data repeated at least 20 times is recommended. While differences between mean and mean-variance-based metamodels were minor in the considered case study, regression-based metamodeling clearly showed improved accuracy and stability over interpolation-based metamodels.