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首页> 外文期刊>Journal of Computational Physics >High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver
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High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver

机译：高阶精确熵稳定的不连续的搭配Galerkin方法，具有备份CFD框架的备份特性：可扩展的SSDC算法和流量求解器

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摘要

This work reports on the performances of a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Naiver-Stokes equations. The resulting code framework is denoted by SSDC, the first S for entropy, the second for stable, and DC for discontinuous collocated. The method is endowed with the summation-by-parts property, allows for arbitrary spatial and temporal order, and is implemented in an unstructured high performance solver. The considered class of fully-discrete algorithms are systematically designed with mimetic and structure preserving properties that allow the transfer of continuous proofs to the fully discrete setting. Our goal is to provide numerical evidence of the adequacy and maturity of these high-order methods as potential base schemes for the next generation of unstructured computational fluid dynamics tools. We provide a series of test cases of increased difficulty, ranging from non-smooth to turbulent flows, in order to evaluate the numerical performance of the algorithms. Results on weak and strong scaling of the distributed memory implementation demonstrate that the parallel SSDC solver can scale efficiently over 100,000 processes. (C) 2020 Elsevier Inc. All rights reserved.
机译：本文研究了可压缩Naiver-Stokes方程的全离散hp自适应熵稳定间断同位Galerkin方法的性能。生成的代码框架用SSDC表示，第一个S表示熵，第二个表示稳定，DC表示不连续并置。该方法具有部件求和特性，允许任意的空间和时间顺序，并在非结构化高性能求解器中实现。所考虑的一类完全离散算法系统地设计了模拟和结构保持特性，允许将连续证明转移到完全离散设置。我们的目标是为这些高阶方法的充分性和成熟性提供数值证据，作为下一代非结构化计算流体力学工具的潜在基础方案。为了评估算法的数值性能，我们提供了一系列难度增加的测试用例，从非光滑流到湍流。对分布式内存实现的弱扩展和强扩展的结果表明，并行SSDC解算器可以有效地扩展超过100000个进程。（C） 2020爱思唯尔公司版权所有。

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