Workshop on Particle-based simulation methods 5-6 Nov 2024
We would like to invite you to the upcoming CAAPS Workshop on Particle-based numerical simulations, which will take place at the Centre for Advanced Analytics and Predictive Sciences of the 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Augsburg on 5th and 6th November 2024. ? Abstract Particle-based simulations are a versatile tool for numerically investigating complex physical systems, including fluid dynamics, solid mechanics, and multi-physics problems. They are well-suited for a number of applications that are difficult to address using traditional grid-based methods, but present their own set of challenges in terms of accuracy, stability, and computational efficiency. In this workshop, researchers from various scientific disciplines come together to discuss recent advancements in particle-based simulations. In addition to exploring innovative modeling approaches and developments in numerical methods, the workshop places special emphasis on efficient algorithms and modern implementation techniques. The first day features presentations from experts in the field, while the second day hosts a hackathon, offering practitioners the chance to work on their own projects and collaborate with fellow participants. ? You can find further information
here. Registration is not mandatory but appreciated, as it helps us with organization and catering.
Together with Erik Faulhaber, Sven Berger, Christian Wei?enfels und Gregor Gassner,?we have submitted our paper "Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation".
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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.