神经节网络模型：多孔介质中截留相的演化

中文摘要 AI 辅助

部分混溶的神经节截留在多孔介质中，跨越一个或多个孔隙，并通过扩散传质演化，这在 subsurface（如 CO$_2$ 和 H$_2$ 储存）以及制造业（如燃料电池）应用中很常见。我们提出了神经节网络模型（GNM），这是一种降阶方法，用于模拟此类神经节群体在任意多孔微结构内部的演化。GNM 基于从给定样品的孔隙尺度图像中提取的树状图——神经节网络。图上的每个点编码了空隙空间中一种可能的神经节构型，且不损失任何几何或拓扑复杂性。群体的演化通过将每个神经节表示为图上的一个粒子，并根据本文制定的规则集对其进行跟踪来建模。这些规则捕获了毛细事件，如孔隙侵入、回缩、卡断、碎裂和合并。与孔隙网络模型（另一种孔隙尺度的基于图的建模工具）不同，GNM 不求解方程组，其成本随神经节数量而非域大小变化。我们在 2.5D 和 3D 域中，针对经历熟化、溶解和生长的群体，将 GNM 与基于图像的孔隙网络模型进行验证。我们发现神经节统计、总体属性和空间构型方面具有良好的一致性。我们进一步论证，神经节网络是扩展 Ostwald 熟化动力学理论从单孔隙到多孔隙神经节所需的统计空间，并提供了实现此目标的概要。GNM 为模拟多孔介质中截留相的其他动力学打开了大门。

英文摘要

Partially miscible ganglia trapped within porous media, spanning one or multiple pores and evolving through diffusive mass transfer, are common in subsurface (e.g., CO$_2$ and H$_2$ storage) and manufacturing (e.g., fuel cells) applications. We present the ganglion network model (GNM), a reduced-order method for simulating how a population of such ganglia evolves inside an arbitrary porous microstructure. GNM operates on a tree graph, the ganglion network, extracted from the pore-scale image of a given sample. Each point on the graph encodes a possible ganglion configuration in the void space, without any loss of geometric or topological complexity. The evolution of a population is modeled by representing each ganglion as a particle on the graph and tracking it according to a set of rules formulated herein. The rules capture capillary events such as pore invasion, retraction, snap-off, fragmentation, and merger. Unlike pore-network models, another graph-based modeling tool at the pore scale, GNM solves no system of equations and its cost scales with ganglion count, not domain size. We validate GNM against an image-based pore-network model in 2.5D and 3D domains with populations undergoing ripening, dissolution, and growth. We find good agreement in ganglion statistics, aggregate properties, and spatial configuration. We further argue that the ganglion network is the statistical space needed for extending kinetic theories of Ostwald ripening from single- to multi-pore ganglia, and provide an outline for how to do this. GNM opens the door to modeling other dynamics of trapped phases in porous media.

发表机构

• The Pennsylvania State University（宾夕法尼亚州立大学）

机构由 AI 辅助整理，请以论文原文为准。