论文标题

在多面眼网上的不合格有限元

Non-conforming finite elements on polytopal meshes

论文作者

Droniou, Jerome, Eymard, Robert, Gallouet, Thierry, Herbin, Raphaele

论文摘要

在这项工作中,我们提出了一个通用框架,用于在多面眼网上不合格的有限元元素,其特征是可以是通用多边形/polyhedra的元素。我们首先以线性椭圆问题为例,代表多孔介质中的单相流量。该框架收集了多种可能的不合格方法,并为此简单模型提供了错误估计。然后,我们将功能框架应用于稳定的退化椭圆方程的情况,为此,需要进行质量倾斜技术。在这里,该技术仅包括使用所选自由度的不同 - 绘制常数 - 功能重建。该退化模型指出了收敛结果。然后,我们引入了一种新型的特异性不合格方法,该方法被称为局部富集的多面体不合格(LEPNC)。这些基础函数包含用于网格的每个面的功能(并与这些面上的平均值相关联),以及跨越局部$ \ mathbb {p}^1 $空间的功能。提供了这些基础功能的插值特性的分析,并提供了质量倾斜技术。提出了数值测试以评估该方法在各种示例中的效率和准确性。最后,我们表明,包括LEPNC在内的通用多面有非构型方法可以插入梯度离散方法框架中,这使得它们可以适合所有误差估计和收敛结果,这些结果已在此框架中为各种模型建立。

In this work we present a generic framework for non-conforming finite elements on polytopal meshes, characterised by elements that can be generic polygons/polyhedra. We first present the functional framework on the example of a linear elliptic problem representing a single-phase flow in porous medium. This framework gathers a wide variety of possible non-conforming methods, and an error estimate is provided for this simple model. We then turn to the application of the functional framework to the case of a steady degenerate elliptic equation, for which a mass-lumping technique is required; here, this technique simply consists in using a different --piecewise constant-- function reconstruction from the chosen degrees of freedom. A convergence result is stated for this degenerate model. Then, we introduce a novel specific non-conforming method, dubbed Locally Enriched Polytopal Non-Conforming (LEPNC). These basis functions comprise functions dedicated to each face of the mesh (and associated with average values on these faces), together with functions spanning the local $\mathbb{P}^1$ space in each polytopal element. The analysis of the interpolation properties of these basis functions is provided, and mass-lumping techniques are presented. Numerical tests are presented to assess the efficiency and the accuracy of this method on various examples. Finally, we show that generic polytopal non-conforming methods, including the LEPNC, can be plugged into the gradient discretization method framework, which makes them amenable to all the error estimates and convergence results that were established in this framework for a variety of models.

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