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Boosting the accuracy of SPH techniques: Newtonian and special-relativistic tests
Stockholm University, Faculty of Science, Department of Astronomy. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Jacobs University Bremen, Germany; University of California, USA.
Number of Authors: 12015 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 448, no 4, p. 3628-3664Article in journal (Refereed) Published
Abstract [en]

We study the impact of different discretization choices on the accuracy of smoothed particle hydrodynamics (SPH) and we explore them in a large number of Newtonian and special-relativistic benchmark tests. As a first improvement, we explore a gradient prescription that requires the (analytical) inversion of a small matrix. For a regular particle distribution, this improves gradient accuracies by approximately 10 orders of magnitude and the SPH formulations with this gradient outperform the standard approach in all benchmark tests. Secondly, we demonstrate that a simple change of the kernel function can substantially increase the accuracy of an SPH scheme. While the 'standard' cubic spline kernel generally performs poorly, the best overall performance is found for a high-order Wendland kernel which allows for only very little velocity noise and enforces a very regular particle distribution, even in highly dynamical tests. Thirdly, we explore new SPH volume elements that enhance the treatment of fluid instabilities and, last, but not least, we design new dissipation triggers. They switch on near shocks and in regions where the flow - without dissipation - starts to become noisy. The resulting new SPH formulation yields excellent results even in challenging tests where standard techniques fail completely.

Place, publisher, year, edition, pages
2015. Vol. 448, no 4, p. 3628-3664
Keywords [en]
hydrodynamics, instabilities, relativistic processes, shock waves, methods: numerical
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-159586DOI: 10.1093/mnras/stv225ISI: 000351529500053OAI: oai:DiVA.org:su-159586DiVA, id: diva2:1245125
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2018-09-04Bibliographically approved

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