面向颗粒流动力学高效粒子分辨预测的分层图神经网络
A hierarchical graph neural network for efficient particle-resolved prediction of granular flow dynamics
Kuang Shi, Qingqi Zhao, Teng Man, Sergio Andres Galindo-Torres, Yimin Lu
AI 速读 基于摘要
作者提出分层粗粒化图神经网络HCG-GNN，在粗图与粒子图上联合传递信息，以较低计算成本实现颗粒流动力学的粒子级预测。
cs.CE
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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.