https://github.com/trixi-framework/paper-2025-particle-based_preprocessing
Reproducibility repository for the paper "Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation"
https://github.com/trixi-framework/paper-2025-particle-based_preprocessing
Last synced: 4 months ago
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Reproducibility repository for the paper "Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation"
- Host: GitHub
- URL: https://github.com/trixi-framework/paper-2025-particle-based_preprocessing
- Owner: trixi-framework
- License: mit
- Created: 2025-06-17T09:09:42.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-10-18T09:39:19.000Z (8 months ago)
- Last Synced: 2025-12-27T12:58:32.838Z (6 months ago)
- Language: Julia
- Homepage:
- Size: 106 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Zenodo: .zenodo.json
Awesome Lists containing this project
README
# Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation
[](https://opensource.org/licenses/MIT)
[](https://doi.org/10.5281/zenodo.15730554)
[](https://doi.org/10.1016/j.cpc.2025.109898)
[](https://arxiv.org/abs/2506.21206)
This repository contains information and code to reproduce the results presented in the
article
```bibtex
@Article{neher2026robust,
author = {Neher, Niklas S. and Faulhaber, Erik and Berger, Sven and Weißenfels,
Christian and Gassner, Gregor J. and Schlottke-Lakemper, Michael},
journal = {Computer Physics Communications},
title = {Robust and efficient pre-processing techniques for particle-based
methods including dynamic boundary generation},
year = {2026},
pages = {109898},
volume = {318},
doi = {10.1016/j.cpc.2025.109898}
}
```
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
```bibtex
@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](https://github.com/trixi-framework/TrixiParticles.jl).
To reproduce the numerical experiments, you need to install
[Julia](https://julialang.org/).
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](LICENSE.md). 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!