A comparison of linear consistent correction methods for first-order SPH derivatives
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Date
2023
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ACM Association for Computing Machinery
Abstract
Awell-known issue with the widely used Smoothed Particle Hydrodynamics (SPH) method is the neighborhood deficiency. Near the surface, the SPH interpolant fails to accurately capture the underlying fields due to a lack of neighboring particles. These errors may introduce ghost forces or other visual artifacts into the simulation. In this work we investigate three different popular methods to correct the first-order spatial derivative SPH operators up to linear accuracy, namely the Kernel Gradient Correction (KGC), Moving Least Squares (MLS) and Reproducing Kernel Particle Method (RKPM). We provide a thorough, theoretical comparison in which we remark strong resemblance between the aforementioned methods. We support this by an analysis using synthetic test scenarios. Additionally, we apply the correction methods in simulations with boundary handling, viscosity, surface tension, vorticity and elastic solids to showcase the reduction or elimination of common numerical artifacts like ghost forces. Lastly, we show that incorporating the correction algorithms in a state-of-the-art SPH solver only incurs a negligible reduction in computational performance.
Description
CCS Concepts: Computing methodologies -> Physical simulation smoothed particle hydrodynamics, computer animation, moving least squares, reproducing kernel particle method"
@inproceedings{10.1145:3606933,
booktitle = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
editor = {Wang, Huamin and Ye, Yuting and Victor Zordan},
title = {{A comparison of linear consistent correction methods for first-order SPH derivatives}},
author = {Westhofen, Lukas and Jeske, Stefan and Bender, Jan},
year = {2023},
publisher = {ACM Association for Computing Machinery},
ISSN = {2577-6193},
ISBN = {},
DOI = {10.1145/3606933}
}