New lab member: Simon Candelaresi
Simon Candelaresi has joined our team at the University of Augsburg on July 1st as a postdoctoral researcher. He will mainly work as part of the DFG Research Unit
SNuBIC, which he already joint in 2022 while still being at the University of Stuttart. Here, he investigates novel algorithms for adaptive multi-physics simulations in the project "C2: Parallel Execution of Adaptive Multi-Physics Simulations on Hierarchical Grids". Simon's scientific background is at the intersection at mathematics and physics, especially in the area of numerical methods for computational plasma physics. He holds a PhD in astronomy from the University of Stockholm and has extensive experience in developing parallel numerical simulation codes. Before joining the SNuBIC team, he was a Rankin-Sneddon Research Fellow at the University of Glasgow and spent time as Postdoctoral Research Fellow at the University of Dundee. Welcome to the HPSC Lab, Simon 👋! We are looking forward to continuing to work with you!
Together with Erik Faulhaber, Sven Berger, Christian Weißenfels und Gregor Gassner, we have submitted our paper "Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation".
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.