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

This repository contains information and code to reproduce the results presented in the article

@misc{neher2025robustefficientpreprocessingtechniques,
      title={Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation},
      author={Niklas S. Neher and Erik Faulhaber and Sven Berger and Christian Weißenfels and Gregor J. Gassner and Michael Schlottke-Lakemper},
      year={2025},
      eprint={2506.21206},
      archivePrefix={arXiv},
      primaryClass={math.NA},
      url={https://arxiv.org/abs/2506.21206},
}

If you find these results useful, please cite the article mentioned above. If you use the implementations provided here, please also cite this repository as

@misc{Neher2025reproducibility,
  title={Reproducibility repository for
         "{R}obust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation"},
  author={Neher, Niklas S. and Faulhaber, Erik and Berger, Sven and Weißenfels Christian and Gassner, Gregor J. and Schlottke-Lakemper, Michael},
  year= {2025},
  howpublished={\url{https://github.com/trixi-framework/paper-2025-particle-based_preprocessing}},
  doi={10.5281/zenodo.15730554}
}

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.

Numerical experiments

The numerical experiments presented in the paper use TrixiParticles.jl. To reproduce the numerical experiments, you need to install Julia.

The subfolder code of this repository contains a README.md file with instructions to reproduce the numerical experiments. The subfolders also include the input data, result data and scripts for postprocessing.

All numerical experiments were carried out using Julia v1.11.5.

Authors

• Niklas S. Neher
• Erik Faulhaber
• Sven Berger
• Christian Weißenfels
• Gregor J. Gassner
• Michael Schlottke-Lakemper

License

The contents of this repository are available under the MIT license. If you reuse our code or data, please also cite us (see above).

Disclaimer

Everything is provided as is and without warranty. Use at your own risk!