Efficient Calibration of Discrete Element Material Model Parameters Using Latin Hypercube Sampling and Kriging

Abstract

Abstract. Material model parameter identification for discrete element models (DEM) is typically done using a trial-and-error approach and its outcome depends largely on the experience of the DEM user. This paper describes a work flow which facilitates the efficient and systematic calibration of discrete element material models against experimental data. The described workflow comprises three steps. In the first step, an approach based on the design and analysis of computer experiments (DACE) is adopted in which data is generated for the parametrisation of Kriging models based on Latin hypercube sampling. In the second step, multi-objective optimisation is applied to the Kriging models. This study introduces an additional cost criterion, which includes the Rayleigh time step, in order to reduce the solution set size to one element. In the third step, the optimisation procedure is repeated with the actual DEM models, using the optimal parameter set from the Kriging models as the start value. This final step with the full DEM models refines the parameter set against experimental data. Since DEM material model calibration is time-consuming, the workflow is implemented into an automated process chain. In this paper, the methodology is described in detail and results are shown which illustrate the usefulness and effectiveness of this approach. Initial verification simulations run using the calibrated parameters give good agreement with experimental results.

Verification of an Automated Work Flow for Discrete Element Material Parameter Calibration

“…The results of this study showed that the automatic DEM material model calibration process described in Rackl et al (2016) is capable of robustly identifying suitable contact law parameters to fit physical measurements for the angle of repose and bulk density, under various boundary conditions. Involving the time step in the optimization process helps the algorithm to select efficient contact law parameters.…”

“…For verification of the calibration process, the DEM model described in Rackl et al (2016) was used as a reference. As in this paper, particle density and rolling friction coefficient were used to calibrate the angle of repose (AoR) and bulk density (BD) based on data from literature.…”

“…The calibration method applied in this study was described in detail by Rackl et al (2016). In summary, it uses a two-step optimization process implemented in Octave (Eaton et al, 2015).…”

“…The robustness of the calibration method described in Rackl et al (2016) was to be tested against four factors. With regard to the intended use of the calibration method, two of them can be viewed as stochastic, whereas the other two are based on human decisions.…”

“…An automated workflow for DEM material model calibration was described by Rackl et al (2016). The DEM code used for this demonstration was LIGGGHTS (Kloss et al, 2012), the methodology was based on Latin hypercube sampling (LHS), Kriging and numerical optimization, and the simulations were planned and controlled using GNU Octave (Eaton et al, 2015).…”