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Smoothness-Increasing Accuracy-Conserving (SIAC) Filters for Discontinuous Galerkin Solutions: Application to Structured Tetrahedral Meshes

机译:用于不连续Galerkin解决方案的提高平滑度的精度保持(SIAC)滤波器:在结构四面体网格中的应用

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In this paper, we attempt to address the potential usefulness of smoothness-increasing accuracy-conserving (SIAC) filters when applied to real-world simulations. SIAC filters as a class of post-processors were initially developed in Bramble and Schatz (Math Comput 31:94, 1977) and later applied to discontinuous Galerkin (DG) solutions of linear hyperbolic partial differential equations by Cockburn et al. (Math Comput 72:577, 2003), and are successful in raising the order of accuracy from k + 1 to 2k + 1 in the L~2-norm when applied to a locally translation-invariant mesh. While there have been several attempts to demonstrate the usefulness of this filtering technique to nontrivial mesh structures (Curtis et al. in SIAM J Sci Comput 30(1):272,2007; Mirzaee et al. in SIAM J Numer Anal 49:1899, 2011; King et al. in J Sci Comput, 2012), the application of the SIAC filter never exceeded beyond two-space dimensions. As tetrahedral meshes are often the type considered in more realistic simulations, we contribute to the class of SIAC post-processors by demonstrating the effectiveness of SIAC filtering when applied to structured tetrahedral meshes. These types of meshes are generated by tetrahedralizing uniform hexahedra and therefore, while maintaining the structured nature of a hexahedral mesh, they exhibit an unstructured tessellation within each hexahedral element. Moreover, we address the computationally intensive task of performing numerical integrations when one considers tetrahedral elements for SIAC filtering and provide guidelines on how to ameliorate these challenges through the use of more general cubature rules. We consider two examples of a hyperbolic equation and confirm the usefulness of SIAC filters in obtaining the superconvergence accuracy of 2k +1 when applied to structured tetrahedral meshes. Additionally, the DG methodology merely requires weak constraints on the fluxes between elements. As SIAC filters improve this weak continuity to C~(k-1) -continuity at the element interfaces, we provide results that show how post-processing is useful in extracting smooth isosurfaces of DG fields.
机译:在本文中,我们尝试解决将平滑度提高的精度保持(SIAC)滤波器应用于实际仿真时的潜在用途。 SIAC滤波器作为一类后处理器,最初是在Bramble和Schatz(Math Comput 31:94,1977)中开发的,后来由Cockburn等人应用于线性双曲偏微分方程的不连续Galerkin(DG)解。 (Math Comput 72:577,2003),并且在将L〜2-范数应用于局部平移不变网格时,成功地将精度从k + 1提升到2k + 1。尽管已进行了多次尝试来证明此过滤技术对非平凡的网格结构的有用性(Curtis等,SIAM J Sci Comput 30(1):272,2007; Mirzaee等,SIAM J Numer Anal 49:1899, 2011; King等人在J Sci Comput,2012)中,SIAC滤波器的应用从未超过二维空间尺寸。由于四面体网格通常是在更逼真的模拟中考虑的类型,因此我们通过证明将SIAC过滤应用于结构化四面体网格时的有效性,为SIAC后处理器类别做出了贡献。这些类型的网格是通过将均匀的六面体四面体化而生成的,因此,在保持六面体网格的结构化性质的同时,它们在每个六面体元素内显示出非结构化的细分。此外,当我们考虑将四面体元素用于SIAC滤波时,我们解决了执行数值积分的计算量大的任务,并提供了有关如何通过使用更通用的孵化规则来缓解这些挑战的指南。我们考虑了一个双曲方程的两个示例,并确认了SIAC滤波器在应用于结构化四面体网格时获得2k +1的超收敛精度的有用性。另外,DG方法仅要求对元素之间的通量进行弱约束。由于SIAC滤波器改善了元素界面处C〜(k-1)-连续性的弱连续性,因此我们提供的结果表明,后处理如何在提取DG场的平滑等值面中有用。

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