Stencil selection algorithms for WENO schemes on unstructured meshes

Panagiotis Tsoutsanis
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引用次数: 28

Abstract

In this paper, a family of stencil selection algorithms is presented for WENO schemes on unstructured meshes. The associated freedom of stencil selection for unstructured meshes, in the context of WENO schemes present a plethora of various stencil selection algorithms. The particular focus of this paper is to assess the performance of various stencil selection algorithm, investigate the parameters that dictate their robustness, accuracy and computational efficiency. Ultimately, efficient and robust stencils are pursued that can provide significant savings in computational performance, while retaining the non-oscillatory character of WENO schemes. This is achieved when making the stencil selection algorithms adaptive, based on the quality of the cells for unstructured meshes, that can in turn reduce the computational cost of WENO schemes. For assessing the performance of the developed algorithms well established test problems are employed. These include the least square approximation of polynomial functions, linear advection equation of smooth functions and solid body rotation test problem. Euler and Navier-Stokes equations test problems are also pursued such as the Shu-Osher test problem, the Double Mach Reflection, the supersonic Forward Facing step, the Kelvin-Helmholtz instability, the Taylor-Green Vortex, and the flow past a transonic circular cylinder.

非结构化网格上WENO方案的模板选择算法
本文针对非结构化网格上的WENO格式,提出了一类模板选择算法。在WENO方案的背景下,非结构化网格的模板选择的相关自由度提供了过多的各种模板选择算法。本文的重点是评估各种模板选择算法的性能,研究决定其稳健性、准确性和计算效率的参数。最终,追求高效和稳健的模板,可以显著节省计算性能,同时保留WENO方案的非振荡特性。这是在基于非结构化网格的单元质量使模板选择算法自适应时实现的,这反过来可以降低WENO方案的计算成本。为了评估所开发算法的性能,采用了成熟的测试问题。其中包括多项式函数的最小二乘逼近、光滑函数的线性平流方程和固体旋转试验问题。Euler和Navier-Stokes方程的试验问题也在研究中,如Shu-Osher试验问题、双马赫反射、超音速前向台阶、Kelvin-Helmholtz不稳定性、Taylor Green涡和跨声速圆柱体流动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Physics: X
Journal of Computational Physics: X Physics and Astronomy-Physics and Astronomy (miscellaneous)
CiteScore
6.10
自引率
0.00%
发文量
7
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