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Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows

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.

Publication metadata

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


DOI: 10.1021/acs.iecr.4c00692


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Funder referenceFunder name
National Natural Science Foundation of China Excellent Young Scientists Fund Program (Overseas)
National Natural Science Foundation of China
University College Cork Eli Lilly Research Scholarships