اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA multiphysics and multiscale software environment for modeling astrophysical systems
82
0
0.0
(
0
)
تأليف
Simon Portegies Zwart
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present MUSE, a software framework for combining existing computational tools for different astrophysical domains into a single multiphysics, multiscale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a Noahs Ark milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multi-scale and multi-physics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe three examples calculated using MUSE: the merger of two galaxies, the merger of two evolving stars, and a hybrid N-body simulation. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.li
قيم البحث
اقرأ أيضاً
We present the open source Astrophysical Multi-purpose Software Environment (AMUSE, www.amusecode.org), a component library for performing astrophysical simulations involving different physical domains and scales. It couples existing codes within a P
ython framework based on a communication layer using MPI. The interfaces are standardized for each domain and their implementation based on MPI guarantees that the whole framework is well-suited for distributed computation. It includes facilities for unit handling and data storage. Currently it includes codes for gravitational dynamics, stellar evolution, hydrodynamics and radiative transfer. Within each domain the interfaces to the codes are as similar as possible. We describe the design and implementation of AMUSE, as well as the main components and community codes currently supported and we discuss the code interactions facilitated by the framework. Additionally, we demonstrate how AMUSE can be used to resolve complex astrophysical problems by presenting example applications.
This paper describes the design and implementation of our new multi-group, multi-dimensional radiation hydrodynamics (RHD) code Fornax and provides a suite of code tests to validate its application in a wide range of physical regimes. Instead of focu
sing exclusively on tests of neutrino radiation hydrodynamics relevant to the core-collapse supernova problem for which Fornax is primarily intended, we present here classical and rigorous demonstrations of code performance relevant to a broad range of multi-dimensional hydrodynamic and multi-group radiation hydrodynamic problems. Our code solves the comoving-frame radiation moment equations using the M1 closure, utilizes conservative high-order reconstruction, employs semi-explicit matter and radiation transport via a high-order time stepping scheme, and is suitable for application to a wide range of astrophysical problems. To this end, we first describe the philosophy, algorithms, and methodologies of Fornax and then perform numerous stringent code tests, that collectively and vigorously exercise the code, demonstrate the excellent numerical fidelity with which it captures the many physical effects of radiation hydrodynamics, and show excellent strong scaling well above 100k MPI tasks.
We present an extension of the PatchworkMHD code [1], itself an MHD-capable extension of the Patchwork code [2], for which several algorithms presented here were co-developed. Its purpose is to create a multipatch scheme compatible with numerical sim
ulations of arbitrary equations of motion at any discretization order in space and time. In the Patchwork framework, the global simulation is comprised of an arbitrary number of moving, local meshes, or patches, which are free to employ their own resolution, coordinate system/topology, physics equations, reference frame, and in our new approach, numerical method. Each local patch exchanges boundary data with a single global patch on which all other patches reside through a client-router-server parallelization model. In generalizing Patchwork to be compatible with arbitrary order time integration, PatchworkMHD and PatchworkWave have significantly improved the interpatch interpolation accuracy by removing an interpolation of interpolated data feedback present in the original Patchwork code. Furthermore, we extend Patchwork to be multimethod by allowing multiple state vectors to be updated simultaneously, with each state vector providing its own interpatch interpolation and transformation procedures. As such, our scheme is compatible with nearly any set of hyperbolic partial differential equations. We demonstrate our changes through the implementation of a scalar wave toy-model that is evolved on arbitrary, time dependent patch configurations at 4th order accuracy.
Clustering algorithms partition a dataset into groups of similar points. The clustering problem is very general, and different partitions of the same dataset could be considered correct and useful. To fully understand such data, it must be considered
at a variety of scales, ranging from coarse to fine. We introduce the Multiscale Environment for Learning by Diffusion (MELD) data model, which is a family of clusterings parameterized by nonlinear diffusion on the dataset. We show that the MELD data model precisely captures latent multiscale structure in data and facilitates its analysis. To efficiently learn the multiscale structure observed in many real datasets, we introduce the Multiscale Learning by Unsupervised Nonlinear Diffusion (M-LUND) clustering algorithm, which is derived from a diffusion process at a range of temporal scales. We provide theoretical guarantees for the algorithms performance and establish its computational efficiency. Finally, we show that the M-LUND clustering algorithm detects the latent structure in a range of synthetic and real datasets.
Physical processes influencing the properties of galaxies can be traced by the dependence and evolution of galaxy properties on their environment. A detailed understanding of this dependence can only be gained through comparison of observations with
models, with an appropriate quantification of the rich parameter space describing the environment of the galaxy. We present a new, multiscale parameterization of galaxy environment which retains an observationally motivated simplicity whilst utilizing the information present on different scales. We examine how the distribution of galaxy (u-r) colours in the Sloan Digital Sky Survey (SDSS), parameterized using a double gaussian (red plus blue peak) fit, depends upon multiscale density. This allows us to probe the detailed dependence of galaxy properties on environment in a way which is independent of the halo model. Nonetheless, cross-correlation with the group catalogue constructed by Yang et al, 2007 shows that galaxy properties trace environment on different scales in a way which mimics that expected within the halo model. This provides independent support for the existence of virialized haloes, and important additional clues to the role played by environment in the evolution of the galaxy population. This work is described in full by Wilman et al., 2010, MNRAS, accepted
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
