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

IF 0.5 Q4 ENGINEERING, MECHANICAL Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2018-12-01 DOI:10.1115/1.4043652
A. Choudhary, William C. Tyson, Christopher J. Roy
{"title":"Implementation and Assessment of a Residual-Based r-Adaptation Technique on Structured Meshes","authors":"A. Choudhary, William C. Tyson, Christopher J. Roy","doi":"10.1115/1.4043652","DOIUrl":null,"url":null,"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.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":" ","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4043652","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Verification, Validation and Uncertainty Quantification","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4043652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.60
自引率
16.70%
发文量
12
期刊最新文献
Automatic Ground-Truth Image Labeling for Deep Neural Network Training and Evaluation Using Industrial Robotics and Motion Capture Using Responsive Feedback in Scaling a Gender Norms-Shifting Adolescent Sexual and Reproductive Health Intervention in the Democratic Republic of Congo. A Solution Verification Study For Urans Simulations of Flow Over a 5:1 Rectangular Cylinder Using Grid Convergence Index And Least Squares Procedures Strategies for Computational Fluid Dynamics Validation Experiments On the Verification of Finite Element Determinations of Stress Concentration Factors for Handbooks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1