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.

中文翻译：

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用于模拟和渲染平滑粒子流体动力学的多级内存结构

在本文中，我们提出了一种新的基于哈希图的平滑粒子流体动力学稀疏数据结构，它允许在空间自适应模拟中进行有效的邻域查询以及流体表面的直接光线追踪。通过使用多个独立的数据结构，利用相同的底层自相似粒子排序，改进了自适应模拟的邻域查询，以显着减少非邻域粒子访问。使用辅助数据结构执行直接光线追踪，内存消耗恒定，这允许有效遍历基于哈希映射的数据结构以及有效的交叉测试。全面的，