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# Arepo wiki
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Arepo is a massively parallel code for gravitational n-body systems and
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magnetohydrodynamics, both on Newtonian as well as cosmological background.
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It is a flexible code that can be applied to a variety of different types of
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simulations, offering a number of sophisticated simulation algorithms. A
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description of the numerical algorithms employed by the code is given in the
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public release paper.
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For a more detailed discussion about these algorithms, the original code paper
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and subsequent publications are the best resource. This documentation only
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addresses the question how to use the different numerical algorithms.
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Arepo was written by Volker Springel (vspringel@mpa-garching.mpg.de) with further
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development by Rüdiger Pakmor (rpakmor@mpa-garching.mpg.de) and contributions by
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many other authors (www.arepo-code.org/people).
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The public version of the code was compiled by Rainer Weinberger
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(rainer.weinberger@cfa.harvard.edu).
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Arepo is a massively parallel code for gravitational N-body systems
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and magnetohydrodynamics, both on Newtonian as well as cosmological
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backgrounds. It is a flexible code that can be applied to a variety
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of different types of problems, offering a number of sophisticated
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simulation algorithms. A description of the numerical algorithms
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employed by the code is given in the public release code paper. For a
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more in depth discussion about these algorithms, the original code
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paper and subsequent publications are the best resource. This
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documentation only addresses the question how to use the different
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numerical algorithms.
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Arepo was written by Volker Springel (vspringel@mpa-garching.mpg.de)
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with further development by Rüdiger Pakmor
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(rpakmor@mpa-garching.mpg.de) and contributions by many other authors
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(www.arepo-code.org/people). The public version of the code was
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compiled by Rainer Weinberger (rainer.weinberger@cfa.harvard.edu).
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Overview
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========
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The Arepo code was initially developed to combine the advantages of
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finite-volume hydrodynamics schemes with the Lagrangian invariance of
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smoothed particle hydrodynamics (SPH) schemes. To this end, Arepo makes use of an
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unstructured Voronoi-mesh which is, in its standard setting, moving with
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the fluid in an quasi-Lagrangian fashion. The fluxes between cells are computed
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using a finite-volume approach, and further spatial adaptivity is
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provided by the possibility to add and remove cells from the
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mesh according to defined criteria. In addition to gas, Arepo allows for a
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number of additional particle types which interact only gravitationally, as
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well as for forces from external gravitational potentials.
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Arepo is optimized for, but not limited to, cosmological simulations of galaxy
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formation and consequently for simulations with very high dynamic ranges in
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space and time. Therefore, Arepo employs an adaptive timestepping for each
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individual cell and particle as well as a dynamic load and memory
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balancing scheme. In its current version, Arepo is fully MPI parallel, and tested
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to run with >10,000 MPI tasks. The exact performance is, however, highly problem and
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machine dependent.
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finite-volume hydrodynamics with the Lagrangian convenience of
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smoothed particle hydrodynamics (SPH). To this end, Arepo makes use of
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an unstructured Voronoi-mesh which is, in the standard mode of
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operating the code, moving with the fluid in a quasi-Lagrangian
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fashion. The fluxes between cells are computed using a finite-volume
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approach, and additional spatial adaptivity is provided by the
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possibility to add and remove cells from the mesh according to
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user-defined criteria. In addition to gas dynamics, Arepo allows for
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additional collisionless particle types which interact only
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gravitationally. Besides self-gravity, forces from external
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gravitational potentials can also be included.
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Arepo is optimized for, but not limited to, cosmological simulations
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of galaxy formation, and consequently can used for simulations with
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high dynamic range in space and time. This is in particular supported
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by Arepo's ability to employ adaptive local timestepping for each
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individual cell or particle, as well as a dynamic load and memory
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balancing domain decomposition. The current version of Arepo is fully
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MPI parallel, and has been tested in runs with >10,000 MPI tasks. The
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exact performance is, however, highly problem- and machine-dependent.
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Disclaimer
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==========
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It is important to note that the performance and accuracy of the code is a
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sensitive function of some of the code parameters. We also stress that Arepo
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comes without any warranty, and without any guarantee that it produces correct
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results. If in doubt about something, reading (and potentially improving) the
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source code is always the best strategy to understand what is going on!
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It is important to note that the performance and accuracy of the code
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is a sensitive function of some of the code parameters. We also stress
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that Arepo comes without any warranty, and without any guarantee that
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it produces correct results. If in doubt about something, reading (and
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potentially improving) the source code is always the best strategy to
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understand what is going on!
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**Please also note the following:**
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The numerical parameter values used in the examples contained in the code
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distribution do not represent a specific recommendation by the authors! In
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particular, we do not endorse these parameter settings in any way as standard
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values, nor do we claim that they will provide fully converged results for the
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example problems, or for other initial conditions. We think that it is extremely
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difficult to make general statements about what accuracy is sufficient for
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certain scientific goals, especially when one desires to achieve it with the
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smallest possible computational effort. For this reason we refrain from making
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The numerical parameter values used in the examples contained in the
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code distribution do not represent a specific recommendation by the
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authors! In particular, we do not endorse these parameter settings in
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any way as standard values, nor do we claim that they will provide
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fully converged results for the example problems, or for other initial
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conditions. We think that it is extremely difficult to make general
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statements about what accuracy is sufficient for certain scientific
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goals, especially when one desires to achieve it with the smallest
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possible computational effort. For this reason we refrain from making
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such recommendations. We encourage every simulator to find out for
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herself/himself what integration settings are needed to achieve sufficient
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accuracy for the system under study. We strongly recommend to make convergence
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and resolution studies to establish the range of validity and the uncertainty of
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any numerical result obtained with Arepo. |
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herself/himself what integration settings are needed to achieve
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sufficient accuracy for the system under study. We strongly recommend
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to make convergence and resolution studies to establish the range of
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validity and the uncertainty of any numerical result obtained with
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Arepo. |
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