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Data-driven model order reduction for granular media
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. (Digital fysik)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. (Digital Physics)ORCID-id: 0000-0002-0787-4988
2022 (engelsk)Inngår i: Computational Particle Mechanics, ISSN 2196-4378, Vol. 9, s. 15-28Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-drivenapproach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distributionand system control signals. Rapid model inference of particle velocities replaces the intense process of computing contactforces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to outputthe interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the mediain domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particlemotion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regressionpredictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90%and 65%, respectively, and the speed-up range between 10 and 60. 

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Springer Nature, 2022. Vol. 9, s. 15-28
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fysik
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URN: urn:nbn:se:umu:diva-169604DOI: 10.1007/s40571-020-00387-6ISI: 000616428800001Scopus ID: 2-s2.0-85101460553OAI: oai:DiVA.org:umu-169604DiVA, id: diva2:1422771
Forskningsfinansiär
Vinnova, 2019-04832eSSENCE - An eScience CollaborationTilgjengelig fra: 2020-04-08 Laget: 2020-04-08 Sist oppdatert: 2023-03-24bibliografisk kontrollert

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