线性弹性的新低阶混合有限元方法

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-03-06 DOI:10.1007/s10444-024-10112-z
Xuehai Huang, Chao Zhang, Yaqian Zhou, Yangxing Zhu
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引用次数: 0

摘要

在任意维度上为对称张量构造了新的低阶 \({H}({{text\ {div}}) \)-符合有限元。形状函数空间是通过用 \({(d+1)}\)-order normal-normal face bubble space 丰富对称二次多项式空间来定义的。缩小后的对应空间只有 \({d(d+1)}^{{2}}\) 个自由度。基函数以重心坐标明确给出。从贝尔元素开始的低阶符合有限元弹性复合体在二维中得到了发展。这些用于对称张量的有限元被应用于设计线性弹性问题的稳健混合有限元方法,这些方法具有关于拉梅系数({\lambda }\)的均匀误差估计和位移的超收敛性。数值结果验证了理论收敛率。
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New low-order mixed finite element methods for linear elasticity

New low-order \({H}({{\text {div}}})\)-conforming finite elements for symmetric tensors are constructed in arbitrary dimension. The space of shape functions is defined by enriching the symmetric quadratic polynomial space with the \({(d+1)}\)-order normal-normal face bubble space. The reduced counterpart has only \({d(d+1)}^{{2}}\) degrees of freedom. Basis functions are explicitly given in terms of barycentric coordinates. Low-order conforming finite element elasticity complexes starting from the Bell element, are developed in two dimensions. These finite elements for symmetric tensors are applied to devise robust mixed finite element methods for the linear elasticity problem, which possess the uniform error estimates with respect to the Lamé coefficient \({\lambda }\), and superconvergence for the displacement. Numerical results are provided to verify the theoretical convergence rates.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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