Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation

TL;DR

This paper presents a robust, memory-efficient preprocessing pipeline for particle-based simulations that generates high-quality, geometry-preserving particle distributions in 2D and 3D, suitable for complex shapes.

Contribution

The authors introduce a novel preprocessing method combining surface point cloud generation, signed distance fields, and hierarchical inside-outside segmentation for particle initialization.

Findings

Particle distributions converge to the exact geometry at higher resolutions.

The method is robust to imperfect input geometries.

It is memory-efficient and easily integrable into existing frameworks.

Abstract

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.