Refinement and Connectivity Algorithms for Adaptive Discontinuous Galerkin Methods

Refinement and Connectivity Algorithms for Adaptive Discontinuous Galerkin Methods
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自适应间断伽辽金法的细化和连通算法

DOI:
10.1137/090767418
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发表时间:
2011
期刊:
SIAM J. Sci. Comput.
影响因子:
--
通讯作者:
Alexander Voss
Alexander Voss
中科院分区:
--
文献类型:
--
作者:
Kolja Brix;Ralf Massjung;Alexander Voss

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自适应多尺度方法是偏微分方程数值求解的众多有效技术之一。高效的网格管理是这些求解器的一项重要任务。在本文中,我们重点关注 2 和 3 空间维度的不连续 Galerkin 离散化方法的问题,并提出了一种以统一方法处理不同单元类型的自适应网格的数据结构。我们不是通过指针存储连接的基于树的技术,而是将细化层次结构中出现的每个单元与单元标识符相关联,并构建建立层次和空间连接的算法。通过按位运算,连接算法的复杂性可以不受级别限制。网格由哈希表表示,这会产生低内存数据结构并确保快速访问单元格数据。空间连通性算法还支持对不连续伽辽金离散化中出现的面积分应用求积规则。这个概念使我们能够实现很大程度上独立于空间维度和细胞类型的不连续伽辽金方法。我们通过概述如何使用我们的数据结构执行这些实现中出现的典型算法任务来证明这一点。在计算测试中,我们将我们的方法与使用指针的经典实现的方法进行比较。
Adaptive multiscale methods are among the many effective techniques for the numerical solution of partial differential equations. Efficient grid management is an important task in these solvers. In this paper we focus on this problem for discontinuous Galerkin discretization methods in 2 and 3 spatial dimensions and present a data structure for handling adaptive grids of different cell types in a unified approach. Instead of tree-based techniques where connectivity is stored via pointers, we associate each cell that arises in the refinement hierarchy with a cell identifier and construct algorithms that establish hierarchical and spatial connectivity. By means of bitwise operations, the complexity of the connectivity algorithms can be bounded independent of the level. The grid is represented by a hash table which results in a low-memory data structure and ensures fast access to cell data. The spatial connectivity algorithm also supports the application of quadrature rules for face integrals that occur in discontinuous Galerkin discretizations. The concept allows us to implement discontinuous Galerkin methods largely independent of spatial dimension and cell type. We demonstrate this by outlining how typical algorithmic tasks that arise in these implementations can be performed with our data structure. In computational tests we compare our approach with that of a classical implementation using pointers.
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