Gravitational N-body Simulations

By M. Trenti, P. Hut. 2008

401 citations recorded by the source corpus.

Gravitational N-body simulations, that is numerical solutions of the equations of motions for N particles interacting gravitationally, are widely used tools in astrophysics, with applications from few body or solar system like systems all the way up to galactic and cosmological scales. In this article we present a summary review of the field highlighting the main methods for N-body simulations and the astrophysical context in which they are usually applied.

Subfields: Astronomy and Astrophysics, Oceanography, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0bed1a0c2de2e17a77cd035147744342d3566e2b

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In Astronomy and Astrophysics

In Oceanography

In Statistical and Nonlinear Physics

Citations in the corpus

Linear tSNE optimization for the Web (2018, methodology): “The gradient descent can be seen as an N -body simulation [1], where each data-point exerts an attractive and a repulsive force on all other points (F attr i and F rep i ).”
Toward Understanding Massive Star Formation (2007, background): “type 8 – 16 M Early B-type massive stars B3V to B0V 16 – 32 M Late O-type massive stars O9V to O6V 32 – 64 M Early O-type massive stars 05V to O2V 64 – 128 M O/WR-type massive stars WNL-H O2V main sequence stars have been identified by Walborn et al. (2002). WNL-H: N-rich late-type Wolf-Rayet (WR) s”
Tidal tails of star clusters (2009, methodology): “Only one cluster was computed for a point-mass galactic potential; for all other clusters, a Milky Way potential consisting of bulge, disc and spherical halo as described in Allen & Santillan (1991) was used.”
The effects of fly‐bys on planetary systems (2010, background): “In Section 3, we describe the properties of planetary systems immediately after a fly-by, as measured through both numerical simulations and analytical calculations. We measure the fraction of systems from which planets are immediately ejected, and the rate at which planets are captured by the intru”
Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, background): “The gradient descent can be seen as a N-body simulation [32], where each data-point exerts an attractive and a repulsive force on all the other points (F attr i and F rep i ).”
Dawes Review 6: The Impact of Companions on Stellar Evolution (2016, background): “The bse code is available for download and it is embedded in nbody6 (Aarseth 2003).”
Galaxies as simple dynamical systems: observational data disfavor dark matter and stochastic star formation (2014, methodology): “Star clusters require collisional dynamics methods such as the direct N -body method [79] to be employed to follow the time-evolving phase-space distribution of its stellar population.”
Particles approximations of Vlasov equations with singular forces : Propagation of chaos (2011, methodology): “– Theorem 3 is also interesting for numerical simulations with particle’s methods [1, 7, 30] because one obvious way to fulfill the assumption i) is to put particles initially on a mesh (with a grid length of N−1/2d in R).”
Merging black holes in young star clusters (2019, methodology): “In this work we have simulated 4 × 10(3) fractal young SCs: half of them with D = 1.6 (HF sample) and the remaining half with D = 2.3 (LF sample). The total mass MSC of each SC (ranging from 1000 M to 30000 M ) is drawn from a distribution dN/dMSC ∝ M−2 SC , as the embedded SC mass function describe”
A sub-parsec resolution simulation of the Milky Way: global structure of the interstellar medium and properties of molecular clouds (2013, methodology): “Since such a hot gas requires to set a small timestep to fulfill the Courant, Friedrichs & Lewy (1928) condition, this represents a significant slowing down of the simulation.”
IMPLEMENTING FEW-BODY ALGORITHMIC REGULARIZATION WITH POST-NEWTONIAN TERMS (2007, methodology): “(1974) and Mikkola & Aarseth (1993) applied the KS transformation to the general three-body and N -body problems. An entirely new way of regularizing close encounters was invented simultaneously by Mikkola & Tanikawa (1999a,b) and by Preto & Tremaine (1999). This new method introduced the so-called ”