Adaptive GPU Kinetic Solver for Fluid-Granular Flows

TL;DR

This paper introduces an adaptive GPU-based simulation framework that couples fluid and granular dynamics efficiently, enabling realistic large-scale phenomena like avalanches and sandstorms with high fidelity.

Contribution

It presents a novel multi-level adaptive solver combining LBM and MPM, with GPU acceleration and consistent rescaling for accurate fluid-granular coupling at large scales.

Findings

Supports large-scale phenomena such as snow avalanches and sandstorms

Achieves high physical fidelity in fluid-granular simulations

Demonstrates computational efficiency through adaptive multi-resolution

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.