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ENZO: AN ADAPTIVE MESH REFINEMENT CODE FOR ASTROPHYSICS

Authors
N/A The Enzo Collaboration,Greg L. Bryan
Michael L. Norman,Brian W. O'Shea,Tom Abel,John H. Wise,Matthew J. Turk,Daniel R. Reynolds,David C. Collins,Peng Wang,Samuel W. Skillman,Britton Smith,Robert P. Harkness,James Bordner,Ji-hoon Kim,Michael Kuhlen,Hao Xu,Nathan Goldbaum,Cameron Hummels,Alexei G. Kritsuk,Elizabeth Tasker,Stephen Skory,Christine M. Simpson,Oliver Hahn,Jeffrey S. Oishi,Geoffrey C So,Fen Zhao,Renyue Cen,Yuan Li,Greg Bryan,Michael Norman,Brian O’Shea,John Wise,Matthew Turk,Daniel Reynolds,David Collins,Samuel Skillman,Robert Harkness,M. Kuhlen,Alexei Kritsuk,Christine Simpson,Jeffrey Oishi
+40 authors
,Geoffrey So
Published
Mar 20, 2014
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Abstract

This paper describes the open-source code Enzo, which uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows. The code is Cartesian, can be run in 1, 2, and 3 dimensions, and supports a wide variety of physics including hydrodynamics, ideal and non-ideal magnetohydrodynamics, N-body dynamics (and, more broadly, self-gravity of fluids and particles), primordial gas chemistry, optically-thin radiative cooling of primordial and metal-enriched plasmas (as well as some optically-thick cooling models), radiation transport, cosmological expansion, and models for star formation and feedback in a cosmological context. In addition to explaining the algorithms implemented, we present solutions for a wide range of test problems, demonstrate the code's parallel performance, and discuss the Enzo collaboration's code development methodology.

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