Including stochastics in metamodel-based DEM model calibration

Fransen, M.P.; Langelaar, M.; Schott, D.L.

doi:10.1016/j.powtec.2022.117400

Including stochastics in metamodel-based DEM model calibration

Title

Including stochastics in metamodel-based DEM model calibration

Author

Fransen, M.P. (TU Delft Transport Engineering and Logistics)
Langelaar, M. (TU Delft Computational Design and Mechanics)
Schott, D.L. (TU Delft Transport Engineering and Logistics)

Date

2022

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.

Subject

Metamodels
Optimization
Random packing
Repeating experiments
Stochastic calibration
Verification and validation

To reference this document use:

http://resolver.tudelft.nl/uuid:785334e4-bc6e-4a86-9dfd-6e1d3aa58f85

DOI

https://doi.org/10.1016/j.powtec.2022.117400

ISSN

0032-5910

Source

Powder Technology, 406

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