Integrating graph neural network-based surrogate modeling with inverse design for granular flows

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dc.check.date2025-05-12en
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisheren
dc.contributor.authorJiang, Yuen
dc.contributor.authorByrne, Edmonden
dc.contributor.authorGlassey, Jarkaen
dc.contributor.authorChen, Xizhongen
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderUniversity College Corken
dc.contributor.funderEli Lilly and Companyen
dc.date.accessioned2024-06-06T08:53:37Z
dc.date.available2024-06-06T08:53:37Z
dc.date.issued2024-05-12en
dc.description.abstractGranular flows are central to a wide range of natural phenomena and industrial processes such as landslides, industrial mixing, and material handling and present intricate particle dynamics challenges. This study introduces a novel approach utilizing a Graph Neural Network-based Simulator (GNS) integrated with an inverse design for optimizing Discrete Element Method (DEM) parameters in granular flow simulations. The GNS model, trained on data sets generated from high-fidelity DEM simulations, exhibits enhanced predictive accuracy and generalization capabilities across various materials and granular collapse scenarios. Methodologically, the study contrasts the GNS approach with conventional Design of Experiment (DoE) methods, highlighting its enhanced computational efficiency and dynamic optimization capacity for complex parameter interactions in granular flows. The results demonstrate the GNS method superiority over the DoE in terms of computational speed and handling intricate parameter relationships. This work offers an advancement in computational techniques for granular flow studies, showing the potential of using differential simulations for realistic problems.en
dc.description.sponsorshipUniversity College Cork (Eli Lilly Research Scholarships); National Natural Science Foundation of China (Excellent Young Scientists Fund Program (Overseas); Grant No. 22308212)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJiang, Y., Byrne, E., Glassey, J. and Chen, X. (2024) 'Integrating graph neural network-based surrogate modeling with inverse design for granular flows', Industrial & Engineering Chemistry Research, 63(20), pp. 9225–9235. https://doi.org/10.1021/acs.iecr.4c00692en
dc.identifier.doihttps://doi.org/10.1021/acs.iecr.4c00692en
dc.identifier.eissn1520-5045en
dc.identifier.endpage9235en
dc.identifier.issn0888-5885en
dc.identifier.issued20en
dc.identifier.journaltitleIndustrial & Engineering Chemistry Researchen
dc.identifier.startpage9225en
dc.identifier.urihttps://hdl.handle.net/10468/15990
dc.identifier.volume63en
dc.language.isoenen
dc.publisherAmerican Chemical Societyen
dc.rights© 2024, American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Industrial & Engineering Chemistry Research, 63(20), pp. 9225–9235 after technical editing by the publisher. To access the final edited and published work see: https://doi.org/10.1021/acs.iecr.4c00692en
dc.subjectGraph neural networksen
dc.subjectDiscrete element methoden
dc.subjectGranular collapseen
dc.subjectInverse designen
dc.subjectOptimization efficiencyen
dc.subjectSurrogate modelen
dc.titleIntegrating graph neural network-based surrogate modeling with inverse design for granular flowsen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue20en
oaire.citation.volume63en
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