Multi‐Level Memory Structures for Simulating and Rendering Smoothed Particle Hydrodynamics

Abstract

In this paper, we present a novel hash map‐based sparse data structure for Smoothed Particle Hydrodynamics, which allows for efficient neighbourhood queries in spatially adaptive simulations as well as direct ray tracing of fluid surfaces. Neighbourhood queries for adaptive simulations are improved by using multiple independent data structures utilizing the same underlying self‐similar particle ordering, to significantly reduce non‐neighbourhood particle accesses. Direct ray tracing is performed using an auxiliary data structure, with constant memory consumption, which allows for efficient traversal of the hash map‐based data structure as well as efficient intersection tests. Overall, our proposed method significantly improves the performance of spatially adaptive fluid simulations and allows for direct ray tracing of the fluid surface with little memory overhead.