Quantifying the influence of microstructure on effective conductivity and permeability: Virtual materials testing

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

卷:184

Article; Proceedings Paper

Neumann, Matthias¹  Stenzel, Ole²  Willot, Francois³  Holzer, Lorenz²  Schmidt, Volker¹ 

[1] Ulm Univ, Inst Stochast, D-89069 Ulm, Germany

[2] ZHAW Winterthur, Inst Computat Phys, CH-8400 Winterthur, Switzerland

[3] PSL Res Univ, Ctr Math Morphol, MINES ParisTech, F-77300 Fontainebleau, France

关键词: Permeability;    Effective conductivity;    Constrictivity;    Mean geodesic tortuosity;    Predictive simulation;    Stochastic microstructure modeling;   

DOI  :  10.1016/j.ijsolstr.2019.03.028

来源: Elsevier

【 摘 要 】

Effective conductivity and permeability of a versatile, graph-based model of random structures are investigated numerically. This model, originally introduced in Gaiselmann et al. (2014) allows one to simulate a wide class of realistic materials. In the present work, an extensive dataset of two-phase microstructures with wide-ranging morphological features is used to assess the relationship between microstructure and effective transport properties, which are computed using Fourier-based methods on digital images. Our main morphological descriptors are phase volume fractions, mean geodesic tortuosity, two hydraulic radii for characterizing the length scales of heterogeneities, and a constrictivity parameter that describes bottleneck effects. This additional parameter, usually not considered in homogenization theories, is an essential ingredient for predicting transport properties, as observed in Gaiselmann et al. (2014). We modify the formula originally developed in Stenzel et al. (2016) for predicting the effective conductivity and propose a formula for permeability. For the latter one, different geometrical definitions of the hydraulic radius are compared. Our predictions are validated using tomographic image data of fuel cells. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free