Adaptive GPU Kinetic Solver for Fluid-Granular Flows

2026-03-16

AI Summary

This work addresses the challenge of reconciling physical fidelity and computational efficiency in large-scale fluid–particle two-phase flow simulations, which arises from the strong nonlinear coupling between fluid and particles. The authors propose a unified framework that couples the lattice Boltzmann method (LBM) with the material point method (MPM), featuring an innovative geometry-aware adaptive block-structured multilevel HOME-LBM solver. The approach introduces a consistent rescaling law across resolution interfaces and dynamically maintains multilevel grids on GPUs to respond to particle motion, enabling efficient bidirectional coupling. Demonstrated in large-scale scenarios such as avalanches, dust storms, and dune migration, the method significantly enhances computational efficiency while preserving high physical fidelity.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• fluid-granular flows
• nonlinear coupling
• computational efficiency
• physical fidelity
• large-scale simulation

Innovation

• adaptive multi-level LBM
• GPU-based dynamic block management
• two-way fluid-granular coupling
• moment rescaling laws
• LBM-MPM unified framework