Multiscale computation of pore-scale fluid dynamics: Single-phase flow

Multiscale computation of pore-scale fluid dynamics: Single-phase flow
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孔隙尺度流体动力学的多尺度计算:单相流

DOI:
10.1016/j.jcp.2018.08.045
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发表时间:
2018
期刊:
J. Comput. Phys.
影响因子:
--
通讯作者:
H. Tchelepi
H. Tchelepi
中科院分区:
--
文献类型:
--
作者:
Y. Mehmani;H. Tchelepi

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直接数值模拟(DNS)的孔隙介质中的间隙流体动力学是阻碍了庞大的规模和复杂的离散方程。虽然降低复杂性的方法,如孔隙网络模型(PNM),偶尔可以产生令人满意的解决方案,在一个低得多的成本,他们既不能估计,也不能控制他们的错误。我们专注于单相Navier-Stokes方程,并开发了一种计算效率高的多尺度方法,产生一系列越来越准确的近似DNS。孔隙水平多尺度方法(PLMM)将孔隙空间分解为若干个子域,并在这些子域上构造一组局部基函数。该基地是再加上一个全球性的接口问题,以获得一个初始近似DNS。近似是优秀的,因为子域符合物理孔隙的空隙空间和物理知情的边界条件被用来构建基地。提出了一种迭代策略,可以任意减小初始近似中的误差。该方法是并行化的,内存效率,并允许不同的物理,模型和网格被纳入每个子域。
Direct numerical simulation (DNS) of interstitial fluid dynamics in porous media is hindered by the sheer size and complexity of the discretized equations. While reduced-complexity methods such as pore-network models (PNM) can occasionally yield satisfactory solutions at a much lower cost, they can neither estimate nor control their error. We focus on the single-phase Navier–Stokes equations and develop a computationally efficient multiscale method that produces a sequence of increasingly accurate approximations to DNS. The pore-level multiscale method (PLMM) decomposes the pore space into several subdomains and constructs a set of local basis functions on them. The bases are coupled with a global interface problem to obtain an initial approximation to DNS. The approximation is excellent because subdomains coincide with physical pores in the void space and physics-informed boundary conditions are used to construct the bases. Errors in the initial approximation can be arbitrarily reduced with an iterative strategy presented. The method is parallelizable, memory efficient, and allows for different physics, models, and meshes to be incorporated within each subdomain.
DOI: 10.1111/gwat.12179
发表时间: 2015
期刊: Groundwater
影响因子: 2.6
作者:
Timothy Scheibe;E. M. Murphy;Xingyuan Chen;A. Rice;K. Carroll;B. Palmer;A. Tartakovsky;I. Battiato;B. Wood
通讯作者: Timothy Scheibe;E. M. Murphy;Xingyuan Chen;A. Rice;K. Carroll;B. Palmer;A. Tartakovsky;I. Battiato;B. Wood