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Adaptive generalized multiscale approximation of a mixed finite element method with velocity elimination

机译:速度消除混合有限元法的自适应通用多尺度近似

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In this paper, we propose offline and online adaptive enrichment algorithms for the generalized multiscale approximation of a mixed finite element method with velocity elimination to solve the subsurface flow problem in high-contrast and heterogeneous porous media. In the offline adaptive method, we first derive an a-posteriori error indicator based on one weighted L-2-norm of the local residual operator, where the weighted L-2-norm is related to the pressure fields of the local snapshot space. Then we enrich the multiscale space by increasing the number of offline basis functions iteratively on coarse elements where the error indicator takes large values. While in the online adaptive method, we add online basis functions on selected coarse elements based on another weighted L-2-norm of the local residual operator to enrich the multiscale space, here the weighted L-2-norm is associated with the velocity fields of the local snapshot space. Online basis functions are constructed in the online stage depending on the solution of the previous iteration and some optimal estimates. We give theoretical analyses for the convergences of these two adaptive methods, which show that sufficient initial basis functions (belong to the offline space) lead to faster convergence rates. A series of numerical examples are provided to highlight the performances of both these two adaptive methods and also validate the theoretical analyses. Both offline and online adaptive methods are effective that can reduce the relative error substantially. In addition, the online adaptive method generally performs better than the offline adaptive method as online basis functions contain important global information such as distant effects that cannot be captured by offline basis functions. The numerical results also show that with a suitable initial multiscale space that includes all offline basis functions corresponding to relative smaller eigenvalues of each local spectral decomposition in the offline stage, the convergence rate of the online enrichment is independent of the permeability contrast.
机译:在本文中,我们提出了用于速度消除速度消除的混合有限元方法的广义和在线自适应富集算法,以解决高对比度和异质多孔介质中的地下流动问题。在离线自适应方法中,我们首先基于局部剩余操作员的一个加权L-2-NOM的A-Bouthiori误差指示器,其中加权L-2-Norm与本地快照空间的压力字段有关。然后,我们通过在粗略元素上迭代地增加离线基础函数的数量来丰富多尺度空间。虽然在在线自适应方法中,我们基于本地残差运算符的另一个加权L-2-NOW在局部剩余操作员的另一个加权L-2-NAR中添加在线基本函数,以丰富多尺度空间,这里加权L-2-NOM与速度字段相关联本地快照空间。在线基本函数在线阶段构建,具体取决于先前迭代的解决方案和一些最佳估计。我们为这两个自适应方法的收敛提供理论分析,这表明足够的初始基本函数(属于离线空间)导致更快的收敛速率。提供了一系列数值示例以突出两种自适应方法的性能,并且还验证了理论分析。离线和在线自适应方法都有效,可以大大降低相对误差。另外,在线自适应方法通常比脱机自适应方法更好,因为在线基本函数包含重要的全局信息,例如无法通过离线基本函数捕获的远程效果。数值结果还表明,利用适当的初始多尺度空间,该空间包括对应于离线阶段的每个局部光谱分解的相对小均值的所有离线基函数,在线富集的收敛速率与渗透率对比度无关。

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