Avoiding ambiguity in DEM in-situ calibration for dry bulk materials

Discrete element simulation (DEM) of the behavior of particulate solids experiences a continuous growth in popularity. However, simulations often require an iterative approach for the determination of the material dependent contact parameters, which often requires a significant portion of the time and budget of a study. Several more efficient procedures for model calibration have been suggested in recent times, but most of them struggle to offer a unique solution to the calibration problem. In this study, a calibration procedure that seeks to avoid ambiguity by attempting to reproduce different time signals instead of a single target parameter is presented. The calibration was performed in a drop test and the search for a suitable parameter set was performed on a meta model obtained from a regression of samples of the parameter space. The calibrated models were then validated by comparison to experimental data. The results show that not all time signals used are equally suitable for the task and that there is some inconsistency in the predictions of the models. However, under the right circumstances, the models are capable of reproducing industrially relevant process parameters. The method can be easily adapted to other industrial processes where time dependent data is available.