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arXiv 0810.0322math.NAcs.NA
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张量扩散方程的非负混合有限元公式

Non-negative mixed finite element formulations for a tensorial diffusion equation

Texas A&M University(德克萨斯农工大学);University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

K. B. Nakshatrala, A. J. Valocchi

AI总结:

针对张量扩散方程,提出两种基于约束优化(二次规划)的非负混合有限元公式,确保低阶有限元在任意网格上得到非负数值解,并分析对局部质量守恒的影响。

摘要:

我们考虑张量扩散方程,并研究混合有限元公式的离散最大-最小原理。特别地,我们关注混合有限元公式的非负解(这是最大-最小原理的一个特例)。离散最大-最小原理是最大-最小原理的离散版本。在本文中,我们提出了两种基于约束优化技术(特别是二次规划)的张量扩散方程的非负混合有限元公式。这些提出的混合公式在任意网格上对低阶(即线性、双线性和三线性)有限元产生非负数值解。第一个公式基于Raviart-Thomas空间,通过向Raviart-Thomas公式的变分表述添加非负约束得到。第二个非负公式基于变分多尺度公式。对于前一个公式,我们评论了添加非负约束对Raviart-Thomas公式局部质量守恒性质的影响。我们还研究了活动集策略在求解所得约束优化问题中的性能。通过三个典型测试问题说明了所提公式的整体性能。

英文摘要:

We consider the tensorial diffusion equation, and address the discrete maximum-minimum principle of mixed finite element formulations. In particular, we address non-negative solutions (which is a special case of the maximum-minimum principle) of mixed finite element formulations. The discrete maximum-minimum principle is the discrete version of the maximum-minimum principle. In this paper we present two non-negative mixed finite element formulations for tensorial diffusion equations based on constrained optimization techniques (in particular, quadratic programming). These proposed mixed formulations produce non-negative numerical solutions on arbitrary meshes for low-order (i.e., linear, bilinear and trilinear) finite elements. The first formulation is based on the Raviart-Thomas spaces, and is obtained by adding a non-negative constraint to the variational statement of the Raviart-Thomas formulation. The second non-negative formulation based on the variational multiscale formulation. For the former formulation we comment on the affect of adding the non-negative constraint on the local mass balance property of the Raviart-Thomas formulation. We also study the performance of the active set strategy for solving the resulting constrained optimization problems. The overall performance of the proposed formulation is illustrated on three canonical test problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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