Conditioning and spectral properties of isogeometric collocation matrices for acoustic wave problems

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-03-04 DOI:10.1007/s10444-024-10113-y
Elena Zampieri, Luca F. Pavarino
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Abstract

The conditioning and spectral properties of the mass and stiffness matrices for acoustic wave problems are here investigated when isogeometric analysis (IGA) collocation methods in space and Newmark methods in time are employed. Theoretical estimates and extensive numerical results are reported for the eigenvalues and condition numbers of the acoustic mass and stiffness matrices in the reference square domain with Dirichlet, Neumann, and absorbing boundary conditions. This study focuses in particular on the spectral dependence on the polynomial degree p, mesh size h, regularity k, of the IGA discretization and on the time step size \(\Delta t\) and parameter \(\beta \) of the Newmark method. Results on the sparsity of the matrices and the eigenvalue distribution with respect to the number of degrees of freedom d.o.f. and the number of nonzero entries nz are also reported. The results show that the spectral properties of the IGA collocation matrices are comparable with the available spectral estimates for IGA Galerkin matrices associated with the Poisson problem with Dirichlet boundary conditions, and in some cases, the IGA collocation results are better than the corresponding IGA Galerkin estimates, in particular for increasing p and maximal regularity \(k=p-1\).

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声波问题等几何配位矩阵的条件和频谱特性
本文研究了声波问题的质量矩阵和刚度矩阵的条件和频谱特性,其中采用了空间等距分析(IGA)配准法和时间纽马克法。报告了在迪里夏特、诺伊曼和吸收边界条件下,参考方域中声学质量和刚度矩阵的特征值和条件数的理论估计和大量数值结果。这项研究特别关注频谱与 IGA 离散化的多项式度 p、网格大小 h、正则性 k 以及 Newmark 方法的时间步长 \(\Delta t\) 和参数 \(\beta \)的关系。还报告了矩阵的稀疏性和特征值分布与自由度数 d.o.f.和非零条目数 nz 有关的结果。结果表明,IGA 拼合矩阵的频谱特性与具有迪里夏特边界条件的泊松问题相关的 IGA Galerkin 矩阵的现有频谱估计值相当,在某些情况下,IGA 拼合结果优于相应的 IGA Galerkin 估计值,特别是在 p 增加和最大正则性 \(k=p-1\)的情况下。
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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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