Radiation-hydrodynamical simulations of massive star formation using Monte Carlo radiative transfer – I. Algorithms and numerical methods

We present a set of new numerical methods that are relevant to calculating radiation pressure terms in hydrodynamics calculations, with a particular focus on massive star formation. The radiation force is determined from a Monte Carlo estimator and enables a complete treatment of the detailed microp...

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Vydáno v:Monthly notices of the Royal Astronomical Society Ročník 448; číslo 4; s. 3156 - 3166
Hlavní autor: Harries, Tim J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Oxford University Press 21.04.2015
Témata:
Algorithms
Astronomy
Computational fluid dynamics
Computer simulation
Fluid flow
Fluid mechanics
Hydrodynamics
Massive stars
Mathematical analysis
Monte Carlo methods
Monte Carlo simulation
Numerical analysis
Radiation
Scattering
Simulation
methods: numerical
stars: formation
ISSN:0035-8711, 1365-2966
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Shrnutí:We present a set of new numerical methods that are relevant to calculating radiation pressure terms in hydrodynamics calculations, with a particular focus on massive star formation. The radiation force is determined from a Monte Carlo estimator and enables a complete treatment of the detailed microphysics, including polychromatic radiation and anisotropic scattering, in both the free-streaming and optically thick limits. Since the new method is computationally demanding we have developed two new methods that speed up the algorithm. The first is a photon packet splitting algorithm that enables efficient treatment of the Monte Carlo process in very optically thick regions. The second is a parallelization method that distributes the Monte Carlo workload over many instances of the hydrodynamic domain, resulting in excellent scaling of the radiation step. We also describe the implementation of a sink particle method that enables us to follow the accretion on to, and the growth of, the protostars. We detail the results of extensive testing and benchmarking of the new algorithms.
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ISSN:0035-8711
1365-2966
DOI:10.1093/mnras/stv158