Obtaining high-quality particle distributions for stable and accurate particle-based simulations poses significant challenges, especially for complex geometries. We introduce a preprocessing technique for 2D and 3D geometries, optimized for smoothed particle hydrodynamics (SPH) and other particle-based methods. Our pipeline begins with the generation of a resolution-adaptive point cloud near the geometry's surface employing a face-based neighborhood search. This point cloud forms the basis for a signed distance field, enabling efficient, localized computations near surface regions. To create an initial particle configuration, we apply a hierarchical winding number method for fast and accurate inside-outside segmentation. Particle positions are then relaxed using an SPH-inspired scheme, which also serves to pack boundary particles. This ensures full kernel support and promotes isotropic distributions while preserving the geometry interface. By leveraging the meshless nature of particle-based methods, our approach does not require connectivity information and is thus straightforward to integrate into existing particle-based frameworks. It is robust to imperfect input geometries and memory-efficient without compromising performance. Moreover, our experiments demonstrate that with increasingly higher resolution, the resulting particle distribution converges to the exact geometry.

翻译：为稳定精确的粒子法模拟获取高质量粒子分布面临重大挑战，对复杂几何体尤为如此。本文提出一种适用于二维与三维几何体的预处理技术，专为光滑粒子流体动力学（SPH）及其他粒子方法优化。我们的处理流程始于在几何表面附近生成分辨率自适应的点云，该过程采用基于面片的邻域搜索算法。此点云构成有符号距离场的基础，支持在表面区域附近进行高效的局部计算。为创建初始粒子构型，我们采用分层环绕数方法实现快速精确的内外区域分割。随后通过SPH启发的弛豫方案调整粒子位置，该方案同时实现边界粒子的紧密排布。这确保了完整核函数支持并促进各向同性分布，同时保持几何界面完整性。通过发挥粒子方法无网格的特性，本方法无需连接信息，可便捷集成至现有粒子法框架。其对不完美输入几何具有鲁棒性，在保持性能的同时实现内存高效性。此外，实验表明随着分辨率提升，所得粒子分布将收敛至精确几何形态。