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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Luding, Stefan
University of Twente
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Densification of visco-elastic powders during free and pressure-assisted sinteringcitations
- 2022Visco-elastic sintering kinetics in virgin and aged polymer powderscitations
- 2021Neck growth kinetics during polymer sintering for powder-based processescitations
- 2020Elastic wave propagation in dry granular mediacitations
- 2019Sintering—Pressure- and Temperature-Dependent Contact Modelscitations
- 2018An iterative sequential Monte Carlo filter for Bayesian calibration of DEM models
- 2018Effect of particle size and cohesion on powder yielding and flowcitations
- 2017Initial stage sintering of polymer particles - Experiments and modelling of size-, temperature- and time-dependent contactscitations
- 2017From soft and hard particle simulations to continuum theory for granular flows
- 2017Multiscale modelling of agglomeration
- 2017Powders and Grains 2017
- 2016Sintering of polymer particles
- 2015Hydraulic properties of sintered porous glass bead systems
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document
An iterative sequential Monte Carlo filter for Bayesian calibration of DEM models
Abstract
The nonlinear history-dependent macroscopic behavior of granular materials is rooted in the micromechanics at contacts and irreversible rearrangements of the microstructure. This paper presents an iterative sequential Monte Carlo filter to infer micromechanical parameters for DEM modeling of granular materials from macroscopic measurements. To demonstrate the performance of the new Bayesian filter, the stress–strain behavior of fine glass beads under oedometric compression is considered. The parameter sets are initially sampled uniformly in parameter space and then resampled around highly probable subspaces, which shrink towards optimal solutions iteratively. The proposed calibration approach is fast, efficient and automated, because it uses the posterior distribution after a completed iteration as the proposal distribution for the succeeding iteration, and thereby allocating computational power to more probable simulation runs. The Bayesian filter can also serve as a powerful tool for uncertainty quantification and propagation across various scales in multiscale simulation of granular materials.