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Analysis of stochastic mimetic finite difference methods and their applications in single-phase stochastic flows

机译:随机模拟有限差分法分析及其在单相随机流中的应用

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Stochastic modeling has become a widely accepted approach to quantify uncertainty in applications where diffusion plays a central role. In many of these applications the geometry is complex, and important properties of the underlying deterministic continuum model need to be captured accurately. To address these problems we present a stochastic mimetic finite difference (MFD) method for diffusion equations with random input data. Specifically, we use the MFD methodology for the spatial approximation to ensure the necessary accuracy and robustness is achieved. To treat the high-dimensionality of the stochastic approximation efficiently, we use a stochastic collocation method. We consider the stochastic MFD approximation in hybrid form, and perform a rigorous analysis of its semi-discretization and full discretization for the pressure, flux and Lagrange multipliers. Convergence rates are developed for statistical moments of the quantities of interest. Numerical results are presented for single-phase flow in random porous media, and support the efficiency of the stochastic MFD.
机译:随机建模已成为一种广泛接受的方法,用于量化在扩散起主要作用的应用中的不确定性。在许多此类应用中,几何形状很复杂,并且需要准确地捕获基本确定性连续模型的重要属性。为了解决这些问题,我们为随机输入数据的扩散方程提供了一种随机的模拟有限差分(MFD)方法。具体来说,我们使用MFD方法进行空间逼近,以确保获得必要的精度和鲁棒性。为了有效地处理随机逼近的高维数,我们使用了随机配置方法。我们考虑混合形式的随机MFD近似,并对压力,通量和拉格朗日乘数的半离散和完全离散进行严格的分析。针对感兴趣量的统计矩开发收敛速率。数值结果给出了随机多孔介质中单相流动的结果,并支持了随机MFD的效率。

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