← 研踪 PaperTrailarXiv:2611.17156 · v1
A hierarchical graph neural network for efficient particle-resolved prediction of granular flow dynamics
面向颗粒流动力学高效粒子分辨预测的分层图神经网络
首次提交 2026-11-13 01:37
版本更新 2026-11-13 01:37
雷达观测 2026-11-13 14:15
摘要
Graph neural surrogates for granular flow can resolve the motion of individual grains, but propagating information across a large amount of grains requires repeated, costly updates on the full particle graph. This study introduce a Hierarchical Coarse-Grained Graph Neural Network (HCG-GNN) that couples direct interactions between neighboring grains with information exchange on two coarser graphs of occupied spatial cells. In discrete element method (DEM) benchmarks of granular flows down incline planes, HCG-GNN captured key flow characteristics, including front propagation, lateral spreading, deposition morphology, free surface evolution, internal shear behavior, and energy conversion. Results also show that preserving grain-specific features as coarse information returned to the particle graph and integrating predicted accelerations to advance particle motion both improved multi-step accuracy. With 5 of its 7 message-passing steps performed on coarse graphs, HCG-GNN achieved accuracy comparable to a conventional particle-level GNN with a 6.47-fold training speedup and 46.2% lower GPU memory use. A model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, compared with 2000 s for DEM under the benchmark conditions. This combination of speed and grain-scale resolution supports particle-resolved inverse design, with prescribed runout or discharge targets guiding the search for suitable geometries and grain dynamics revealing the basis for differences in performance.
作者备注：44 pages, 10 figures
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v1 · 2026-11-13 01:37 · 最新观测版本
A hierarchical graph neural network for efficient particle-resolved prediction of granular flow dynamics
Graph neural surrogates for granular flow can resolve the motion of individual grains, but propagating information across a large amount of grains requires repeated, costly updates on the full particle graph. This study introduce a Hierarchical Coarse-Grained Graph Neural Network (HCG-GNN) that couples direct interactions between neighboring grains with information exchange on two coarser graphs of occupied spatial cells. In discrete element method (DEM) benchmarks of granular flows down incline planes, HCG-GNN captured key flow characteristics, including front propagation, lateral spreading, deposition morphology, free surface evolution, internal shear behavior, and energy conversion. Results also show that preserving grain-specific features as coarse information returned to the particle graph and integrating predicted accelerations to advance particle motion both improved multi-step accuracy. With 5 of its 7 message-passing steps performed on coarse graphs, HCG-GNN achieved accuracy comparable to a conventional particle-level GNN with a 6.47-fold training speedup and 46.2% lower GPU memory use. A model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, compared with 2000 s for DEM under the benchmark conditions. This combination of speed and grain-scale resolution supports particle-resolved inverse design, with prescribed runout or discharge targets guiding the search for suitable geometries and grain dynamics revealing the basis for differences in performance.
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