Minimum requirements for predictive pore-network modeling of solute transport in micromodels
Minimum requirements for predictive pore-network modeling of solute transport in micromodels
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DOI：
10.1016/j.advwatres.2017.07.014
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发表时间：
2017-10-01
影响因子：
4.7
通讯作者：
Tchelepi, Hamdi A.
中科院分区：
文献类型：
--
作者：
Mehmani, Yashar;Tchelepi, Hamdi A.
孔隙尺度模型现在是分析多孔材料中流体动力学的组成部分（例如，岩石、土壤、燃料电池）。孔隙网络模型（PNM）由于其计算效率而特别有吸引力。然而，PNM的定量预测并不总是成功的。我们专注于单相输运的被动示踪剂在平流为主的制度和比较PNM高保真直接数值模拟（DNS）的微观模型的异质性范围。我们确定了预测PNM的运输的最低要求。（a）基于流的网络提取，即，基于底层速度场离散孔隙空间，（B）拉格朗日（颗粒跟踪）模拟框架，和（c）颗粒从一个孔喉到下一个孔喉的精确转移。我们开发新的网络提取和粒子跟踪PNM方法，满足这些要求。此外，我们表明，某些既定的PNM的做法，在文献中可能会导致一阶误差对流为主的运输建模。它们包括：所有欧拉PNM，网络提取的基础上的几何度量，和基于通量的节点转移概率。还介绍了三维球包的初步结果。这项工作的模拟输入是公开的，作为研究界的基准。(C)2017爱思唯尔有限公司版权所有
Pore-scale models are now an integral part of analyzing fluid dynamics in porous materials (e.g., rocks, soils, fuel cells). Pore network models (PNM) are particularly attractive due to their computational efficiency. However, quantitative predictions with PNM have not always been successful. We focus on single-phase transport of a passive tracer under advection-dominated regimes and compare PNM with high-fidelity direct numerical simulations (DNS) for a range of micromodel heterogeneities. We identify the minimum requirements for predictive PNM of transport. They are: (a) flow-based network extraction, i.e., discretizing the pore space based on the underlying velocity field, (b) a Lagrangian (particle tracking) simulation framework, and (c) accurate transfer of particles from one pore throat to the next. We develop novel network extraction and particle tracking PNM methods that meet these requirements. Moreover, we show that certain established PNM practices in the literature can result in first-order errors in modeling advection-dominated transport. They include: all Eulerian PNMs, networks extracted based on geometric metrics only, and flux-based nodal transfer probabilities. Preliminary results for a 3D sphere pack are also presented. The simulation inputs for this work are made public to serve as a benchmark for the research community. (C) 2017 Elsevier Ltd. All rights reserved.