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Nonlocal multicontinua with representative volume elements. Bridging separable and non-separable scales

机译:与代表卷元素的非局部多晶瓜。桥接可分离和不可分离的尺度

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Recently, several approaches for multiscale simulations for problems with high contrast and no scale separation are introduced. Among them is nonlocal multicontinua (NLMC) method, which introduces multiple macroscopic variables in each computational grid. These approaches explore the entire coarse block resolution and one can obtain optimal convergence results independent of contrast and scales. However, these approaches are not amenable to many multiscale simulations, where the subgrid effects are much smaller than the coarse-mesh resolution. For example, molecular dynamics of shale gas occurs in much smaller length scales compared to the coarse-mesh size, which is of orders of meters. In this case, one cannot explore the entire coarse-grid resolution in evaluating effective properties. In this paper, we merge the concepts of nonlocal multicontinua methods and Representative Volume Element (RVE) concepts to explore problems with extreme scale separation. The first step of this approach is to use sub-grid scale (sub to RVE) to write a large-scale macroscopic system. We call it intermediate scale macroscale system. In the next step, we couple this intermediate macroscale system to the simulation grid model, which are used in simulations. This is done using RVE concepts, where we relate intermediate macroscale variables to the macroscale variables defined on our simulation coarse grid. Our intermediate coarse model allows formulating macroscale variables correctly and coupling them to the simulation grid. We present the general concept of our approach and present details of single-phase flow. Some numerical results are presented. For nonlinear examples, we use machine learning techniques to compute macroscale parameters. (C) 2021 Elsevier B.V. All rights reserved.
机译:最近,引入了多尺度模拟对对比度和没有比例分离的问题的几种方法。其中包括非本体多数体(NLMC)方法,其在每个计算网格中引入多个宏观变量。这些方法探讨整个粗块分辨率,并且可以获得与对比度和尺度无关的最佳收敛结果。然而,这些方法不适合许多多尺度仿真,其中底图效应远小于粗网格分辨率。例如,与粗网格尺寸相比,页岩气的分子动力学发生在较小的长度范围内,这是米的粗糙度。在这种情况下,人们不能探索整个粗网分辨率,评估有效属性。在本文中,我们合并了非本体多数量方法和代表卷元素(RVE)概念的概念来探讨极度分离的问题。这种方法的第一步是使用子网格刻度(子至rve)来写入大规模的宏观系统。我们称之为中间尺度Macroscale系统。在下一步中,我们将该中间Macroscale系统耦合到模拟网格模型,这些网格模型用于仿真。这是使用rve概念完成的,其中,我们将中间宏尺度变量与在模拟粗略网格上定义的Macroscale变量相关联。我们的中间粗略模型允许正确配制宏观变量并将其耦合到模拟网格。我们展示了我们方法的一般概念,并提出了单相流的细节。提出了一些数值结果。对于非线性示例,我们使用机器学习技术来计算Macroscale参数。 (c)2021 Elsevier B.v.保留所有权利。

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