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Lookup NU author(s): Professor Jarka Glassey
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© 2024 American Chemical Society. Granular 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.
Author(s): Jiang Y, Byrne E, Glassey J, Chen X
Publication type: Article
Publication status: Published
Journal: Industrial and Engineering Chemistry Research
Year: 2024
Volume: 63
Issue: 20
Pages: 9225-9235
Print publication date: 22/05/2024
Online publication date: 13/05/2024
Acceptance date: 30/04/2024
ISSN (print): 0888-5885
ISSN (electronic): 1520-5045
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acs.iecr.4c00692
DOI: 10.1021/acs.iecr.4c00692
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