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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.14982 |
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| _version_ | 1866915865662849024 |
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| author | Li, Xingqiao Wu, Kui Su, Haozhe Gao, Tianhong Chu, Mengyu Jiang, Chenfanfu Li, Wei Chen, Baoquan |
| author_facet | Li, Xingqiao Wu, Kui Su, Haozhe Gao, Tianhong Chu, Mengyu Jiang, Chenfanfu Li, Wei Chen, Baoquan |
| contents | Simulating fluid-granular flows is crucial for understanding natural disasters, industrial processes, and visually realistic phenomena in computer graphics. These systems are challenging to simulate because of the strong nonlinear coupling between continuum fluids and discrete granular media, making it difficult to achieve both physical fidelity and computational efficiency at large scales. In this work, we present a unified framework for large-scale fluid-granular simulation that couples the Lattice Boltzmann Method (LBM) for fluids with the Material Point Method (MPM) for granular materials such as sand and snow. We introduce an adaptive block-based multi-level HOME-LBM solver based on solid geometric structures, enabling efficient memory usage and computational performance across multiple lattice resolutions. Consistent rescaling laws for moments allow accurate transfer of macroscopic quantities across refinement interfaces, while a GPU-based algorithm dynamically maintains the multi-level blocks in response to particle motion. By enforcing that all MPM particles reside within the finest fluid nodes, we achieve accurate two-way coupling between fluid and granular phases. Our framework supports a wide range of large-scale phenomena, including snow avalanches, sandstorms, and sand migration, demonstrating high physical fidelity and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14982 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive GPU Kinetic Solver for Fluid-Granular Flows Li, Xingqiao Wu, Kui Su, Haozhe Gao, Tianhong Chu, Mengyu Jiang, Chenfanfu Li, Wei Chen, Baoquan Graphics Simulating fluid-granular flows is crucial for understanding natural disasters, industrial processes, and visually realistic phenomena in computer graphics. These systems are challenging to simulate because of the strong nonlinear coupling between continuum fluids and discrete granular media, making it difficult to achieve both physical fidelity and computational efficiency at large scales. In this work, we present a unified framework for large-scale fluid-granular simulation that couples the Lattice Boltzmann Method (LBM) for fluids with the Material Point Method (MPM) for granular materials such as sand and snow. We introduce an adaptive block-based multi-level HOME-LBM solver based on solid geometric structures, enabling efficient memory usage and computational performance across multiple lattice resolutions. Consistent rescaling laws for moments allow accurate transfer of macroscopic quantities across refinement interfaces, while a GPU-based algorithm dynamically maintains the multi-level blocks in response to particle motion. By enforcing that all MPM particles reside within the finest fluid nodes, we achieve accurate two-way coupling between fluid and granular phases. Our framework supports a wide range of large-scale phenomena, including snow avalanches, sandstorms, and sand migration, demonstrating high physical fidelity and computational efficiency. |
| title | Adaptive GPU Kinetic Solver for Fluid-Granular Flows |
| topic | Graphics |
| url | https://arxiv.org/abs/2603.14982 |