ZEUS-MP/2: Computational Fluid Dynamics Code

Details ZEUS-MP/2: Computational Fluid Dynamics Code ZEUS-MP/2: Computational Fluid Dynamics Code

Feb 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011ascl.soft02028h&link_type=abstract

Astrophysics Source Code Library, record ascl:1102.028

Astronomy and Astrophysics

Astrophysics

Scientific paper

ZEUS-MP is a multiphysics, massively parallel, message-passing implementation of the ZEUS code. ZEUS-MP offers an MHD algorithm that is better suited for multidimensional flows than the ZEUS-2D module by virtue of modifications to the method of characteristics scheme first suggested by Hawley & Stone. This MHD module is shown to compare quite favorably to the TVD scheme described by Ryu et al. ZEUS-MP is the first publicly available ZEUS code to allow the advection of multiple chemical (or nuclear) species. Radiation hydrodynamic simulations are enabled via an implicit flux-limited radiation diffusion (FLD) module. The hydrodynamic, MHD, and FLD modules can be used, singly or in concert, in one, two, or three space dimensions. In addition, so-called 1.5D and 2.5D grids, in which the "half-D'' denotes a symmetry axis along which a constant but nonzero value of velocity or magnetic field is evolved, are supported. Self-gravity can be included either through the assumption of a GM/r potential or through a solution of Poisson's equation using one of three linear solver packages (conjugate gradient, multigrid, and FFT) provided for that purpose. Point-mass potentials are also supported. Because ZEUS-MP is designed for large simulations on parallel computing platforms, considerable attention is paid to the parallel performance characteristics of each module in the code. Strong-scaling tests involving pure hydrodynamics (with and without self-gravity), MHD, and RHD are performed in which large problems (2563 zones) are distributed among as many as 1024 processors of an IBM SP3. Parallel efficiency is a strong function of the amount of communication required between processors in a given algorithm, but all modules are shown to scale well on up to 1024 processors for the chosen fixed problem size.

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