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首页> 外文期刊>International Journal for Numerical Methods in Engineering >Dual-grid-based tree/cotree decomposition of higher-order interpolatory H(?∧,Ω) basis
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Dual-grid-based tree/cotree decomposition of higher-order interpolatory H(?∧,Ω) basis

机译:基于双网格的高阶插值H(?∧,Ω)基的树/共树分解

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摘要

This work extends the zeroth-order tree/cotree (TC) decomposition method into higher order (HO) interpolatory elements and develops the constraints operator required for the elimination of spurious solutions for general HO spectral basis. Earlier methods explicitly enforce the divergence condition that requires a mixed finite element (FE) formulation with both H~1 and H(?∧) expansions and involves repeated solutions of the Poisson equation. A recent approach, which avoids the mixed formulation and the Poisson problem, uses TC decomposition of edge DoF over the primal graph and construction of integration and gradient matrices. The approach is easily applied to HO hierarchical elements but becomes quite complex for HO spectral elements. In the presence of internal DoF, it is difficult to utilize the primal graph for an explicit decomposition of the spectral DoF. In contrast, this work utilizes the dual grid, resulting in an explicit decomposition of DoF and construction of constraint equations from a fixed element matrix. Thus, mixed formulation and the Poisson problems are avoided while eliminating the need for evaluation of integration and gradient matrices. The proposed constraints matrix is element-geometry independent and possesses an explicit sparsity formulation reducing the need for dynamic memory allocation. Numerical examples are included for verification.
机译:这项工作将零阶树/共树(TC)分解方法扩展为高阶(HO)插值元素,并开发了消除普通HO频谱基础的杂散解所需的约束算子。较早的方法明确地规定了发散条件,该条件需要同时具有H〜1和H(?∧)展开的混合有限元(FE)公式,并涉及Poisson方程的重复解。一种避免混合公式和泊松问题的最新方法是在原始图上使用边缘自由度的TC分解,并构造积分和梯度矩阵。该方法很容易应用于HO层次元素,但对于HO频谱元素而言变得相当复杂。在存在内部自由度的情况下,很难利用原始图对光谱自由度进行显式分解。相比之下,这项工作利用了双重网格,从而导致了DoF的显式分解以及从固定元素矩阵构造约束方程式。因此,避免了混合公式和泊松问题,同时消除了对积分和梯度矩阵进行评估的需要。所提出的约束矩阵与元素几何无关,并且具有显式的稀疏性表述,从而减少了对动态内存分配的需求。包含数值示例以进行验证。

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