Implementation and Assessment of a Residual-Based r-Adaptation Technique on Structured Meshes

IF 0.5 Q4 ENGINEERING, MECHANICAL
A. Choudhary, William C. Tyson, Christopher J. Roy
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引用次数: 1

Abstract

In this study, an r-adaptation technique for mesh adaptation is employed for reducing the solution discretization error, which is the error introduced due to spatial and temporal discretization of the continuous governing equations in numerical simulations. In r-adaptation, mesh modification is achieved by relocating the mesh nodes from one region to another without introducing additional nodes. Truncation error (TE) or the discrete residual is the difference between the continuous and discrete form of the governing equations. Based upon the knowledge that the discrete residual acts as the source of the discretization error in the domain, this study uses discrete residual as the adaptation driver. The r-adaptation technique employed here uses structured meshes and is verified using a series of one-dimensional (1D) and two-dimensional (2D) benchmark problems for which exact solutions are readily available. These benchmark problems include 1D Burgers equation, quasi-1D nozzle flow, 2D compression/expansion turns, and 2D incompressible flow past a Karman–Trefftz airfoil. The effectiveness of the proposed technique is evident for these problems where approximately an order of magnitude reduction in discretization error (when compared with uniform mesh results) is achieved. For all problems, mesh modification is compared using different schemes from literature including an adaptive Poisson grid generator (APGG), a variational grid generator (VGG), a scheme based on a center of mass (COM) analogy, and a scheme based on deforming maps. In addition, several challenges in applying the proposed technique to real-world problems are outlined.
一种基于残差的结构网格自适应技术的实现与评价
本文采用网格自适应的r-自适应技术来减小数值模拟中连续控制方程的时空离散所带来的解离散误差。在r- adaptive中,网格修改是通过将网格节点从一个区域重新定位到另一个区域而不引入额外的节点来实现的。截断误差(TE)或离散残差是控制方程的连续形式和离散形式之间的差值。基于离散残差是域内离散化误差来源的认识,本研究采用离散残差作为自适应驱动。本文采用的r-自适应技术使用结构化网格,并使用一系列一维(1D)和二维(2D)基准问题进行验证,这些基准问题的精确解很容易获得。这些基准问题包括一维Burgers方程、准一维喷管流动、二维压缩/膨胀转弯以及二维不可压缩气流通过卡门- trefftz翼型。对于这些问题,所提出的技术的有效性是显而易见的,其中离散化误差(与均匀网格结果相比)大约降低了一个数量级。针对所有问题,比较了文献中不同的网格修改方案,包括自适应泊松网格生成器(APGG)、变分网格生成器(VGG)、基于质心类比(COM)的方案和基于变形映射的方案。此外,还概述了将所建议的技术应用于实际问题的几个挑战。
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
CiteScore
1.60
自引率
16.70%
发文量
12
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