Access to this article is restricted until 12 months after publication by request of the publisher. Restriction lift date: 2025-05-12
Integrating graph neural network-based surrogate modeling with inverse design for granular flows
Loading...
Files
Date
2024-05-12
Authors
Jiang, Yu
Byrne, Edmond
Glassey, Jarka
Chen, Xizhong
Journal Title
Journal ISSN
Volume Title
Publisher
American Chemical Society
Published Version
Abstract
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.
Description
Keywords
Graph neural networks , Discrete element method , Granular collapse , Inverse design , Optimization efficiency , Surrogate model
Citation
Jiang, 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.4c00692
Link to publisher’s version
Copyright
© 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.4c00692