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Characterization of microstructures using contour tree connectivity for fluid flow analysis

机译:使用轮廓树连通性进行流体流动分析的微结构表征

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摘要

Quantifying the connectivity of material microstructures is important for a wide range of applications from filters to biomaterials. Currently, the most used measure of connectivity is the Euler number, which is a topological invariant. Topology alone, however, is not sufficient for most practical purposes. In this study, we use our recently introduced connectivity measure, called the contour tree connectivity (CTC), to study microstructures for flow analysis. CTC is a new structural connectivity measure that is based on contour trees and algebraic graph theory. To test CTC, we generated a dataset composed of 120 samples and six different types of artificial microstructures. We compared CTC against the Euler parameter (EP), the parameter for connected pairs, the nominal opening dimension (dnom) and the permeabilities estimated using direct pore scale modelling. The results show that dnom is highly correlated with permeability (R2 = 0.91), but cannot separate the structural differences. The groups are best classified with feature combinations that include CTC. CTC provides new information with a different connectivity interpretation that can be used to analyse and design materials with complex microstructures.
机译:量化材料微观结构的连通性对于从过滤器到生物材料的广泛应用非常重要。当前,最常用的连通性度量是欧拉数,它是拓扑不变式。但是,仅拓扑不足以满足大多数实际目的。在这项研究中,我们使用我们最近引入的连通性度量(称为轮廓树连通性(CTC))来研究用于流动分析的微观结构。 CTC是基于轮廓树和代数图论的一种新的结构连通性度量。为了测试CTC,我们生成了一个包含120个样本和六种不同类型的人工微观结构的数据集。我们将CTC与Euler参数(EP),连接对的参数,标称开口尺寸(dnom)和使用直接孔垢模型估算的渗透率进行了比较。结果表明,dnom与渗透率高度相关(R 2 = 0.91),但不能区分结构差异。最好用包括CTC的功能组合对组进行分类。 CTC提供了具有不同连通性解释的新信息,可用于分析和设计具有复杂微结构的材料。

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