A Petrov-Galerkin method for nonlocal convection-dominated diffusion problems

A Petrov-Galerkin method for nonlocal convection-dominated diffusion problems
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求解非局部对流主导扩散问题的 Petrov-Galerkin 方法

DOI:
10.1016/j.jcp.2021.110919
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发表时间:
2022
影响因子:
4.1
通讯作者:
Foster, John T.
Foster, John T.
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
Leng, Yu;Tian, Xiaochuan;Demkowicz, Leszek;Gomez, Hector;Foster, John T.

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给出了一类非局部对流占优扩散问题的Petrov-Galerkin(PG)方法。在我们的方法中有两个主要因素。首先,我们定义了测试空间上的范数是由测试空间范数诱导的,即最优测试范数,使得inf-sup条件可以与问题无关地一致满足。在一般的非局部扩散和对流核假设下,证明了一类非局部对流占优扩散问题在最优检验范数下的适定性。第二,遵循科恩等人的框架。(2012),我们将原始的非局部对流占优扩散问题嵌入到一个更大的混合问题中,以选择一个丰富的测试空间作为数值算法的稳定化。在数值实验中,我们使用了一维可有效实现的近似最优测试范数,并在测试空间上研究了它相对于能量范数的性能。我们对非局部问题进行了一致h-和p-精化的研究,并对光滑的人造解和过渡层中具有尖锐梯度的解进行了自适应h-精化。此外,我们还证实了PG方法是渐近相容的,遵循了Tian和Du(2014)的概念。
We present a Petrov-Galerkin (PG) method for a class of nonlocal convection-dominated diffusion problems. There are two main ingredients in our approach. First, we define the norm on the test space as induced by the trial space norm, i.e., the optimal test norm, so that the inf-sup condition can be satisfied uniformly independent of the problem. We show the well-posedness of a class of nonlocal convection-dominated diffusion problems under the optimal test norm with general assumptions on the nonlocal diffusion and convection kernels. Second, following the framework of Cohen et al. (2012), we embed the original nonlocal convection-dominated diffusion problem into a larger mixed problem so as to choose an enriched test space as a stabilization of the numerical algorithm. In the numerical experiments, we use an approximate optimal test norm which can be efficiently implemented in 1d, and study its performance against the energy norm on the test space. We conduct convergence studies for the nonlocal problem using uniformh- andp-refinements, and adaptiveh-refinements on both smooth manufactured solutions and solutions with sharp gradient in a transition layer. In addition, we confirm that the PG method is asymptotically compatible, following the notion in Tian and Du (2014).
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