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Cited by 184

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Astrophysical smooth particle hydrodynamics

Open access
Rosswog, Stephan·2009·New Astronomy Reviews

Abstract

Abstract The paper presents a detailed review of the smooth particle hydrodynamics (SPH) method with particular focus on its astrophysical applications. We start by introducing the basic ideas and concepts and thereby outline all ingredients that are necessary for a practical implementation of the method in a working SPH code. Much of SPH’s success relies on its excellent conservation properties and therefore the numerical conservation of physical invariants receives much attention throughout this review. The self-consistent derivation of the SPH equations from the Lagrangian of an ideal fluid is the common theme of the remainder of the text. We derive a modern, Newtonian SPH formulation from the Lagrangian of an ideal fluid. It accounts for changes of the local resolution lengths which result in corrective, so-called “grad-h-terms”. We extend this strategy to special relativity for which we derive the corresponding grad-h equation set. The variational approach is further applied to a general-relativistic fluid evolving in a fixed, curved background space-time. Particular care is taken to explicitly derive all relevant equations in a coherent way.

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Crossref · 10.1016/j.newar.2009.08.007

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Metadata

Authors
Rosswog, S.
Venue
New Astronomy Reviews
Publisher
Elsevier BV
Published
2009-04
Volume
53
Issue
4-6
Pages
78-104
ISSN
1387-6473 (ISSN-L)
OpenAlex
W2119141923
Cited by
184
References
1
FWCI
18.21
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Astrophysical smooth particle hydrodynamics. https://app.cochat.ai/papers/10.1016/j.newar.2009.08.007
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Rosswog, S. (2009). Astrophysical smooth particle hydrodynamics. New Astronomy Reviews. https://doi.org/10.1016/j.newar.2009.08.007

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.