Local accuracy in finite element analysis using curved isoparametric elements

IF 0.7 4区 数学 Q4 MATHEMATICS, APPLIED
Pranjal Saxena, Chandra Shekhar Upadhyay
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引用次数: 0

Abstract

The finite element method (FEM) is popularly used for numerically approximating PDE(s) over complicated domains due to its rich mathematical background, versatility, and ease of implementation. In this article, we investigate one of its important features, i.e., the approximation of PDE(s) over nonpolygonal Lipschitz domains by higher-order simplicial elements in 2D and 3D. This important issue is not well understood and often ignored by engineers due to its mathematical complexity, i.e., the FEM approximation of curved domains results in inexact boundary conditions, which is a variational crime. This article explores the role of approximation at curved boundaries. Further, the effect of incompleteness of the approximation space also contributes to the error induced in the curved elements. A simple benchmark test for errors is proposed. Tests are conducted for subparametric and isoparametric approximations. Comparison with isogeometric analysis (IGA) is also presented to highlight the basic differences and advantages of isoparametric elements.

曲面等参单元有限元分析中的局部精度
有限元法(FEM)由于其丰富的数学背景、通用性和易于实现而被广泛用于复杂域上的偏微分方程的数值逼近。在本文中,我们研究了它的一个重要特征,即二维和三维中非多边形Lipschitz域上的PDE(s)的高阶简单元逼近。由于数学上的复杂性,这个重要的问题并没有被很好地理解,并且经常被工程师忽视,即,曲面域的有限元近似导致不精确的边界条件,这是一种变分犯罪。本文探讨了近似在曲线边界处的作用。此外,近似空间的不完备性也导致了曲线单元的误差。提出了一种简单的误差基准测试方法。对次参数近似和等参数近似进行了试验。并与等几何分析(IGA)进行了比较,以突出等参数单元的基本区别和优势。
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
Applications of Mathematics
Applications of Mathematics 数学-应用数学
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
3.0 months
期刊介绍: Applications of Mathematics publishes original high quality research papers that are directed towards applications of mathematical methods in various branches of science and engineering. The main topics covered include: - Mechanics of Solids; - Fluid Mechanics; - Electrical Engineering; - Solutions of Differential and Integral Equations; - Mathematical Physics; - Optimization; - Probability Mathematical Statistics. The journal is of interest to a wide audience of mathematicians, scientists and engineers concerned with the development of scientific computing, mathematical statistics and applicable mathematics in general.
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