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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02425310
Contributor : François Willot <>
Submitted on : Friday, December 25, 2020 - 9:30:44 PM
Last modification on : Thursday, February 4, 2021 - 10:45:50 AM

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Matthias Neumann, Ole Stenzel, François Willot, Lorenz Holzer, Volker Schmidt. Quantifying the influence of microstructure on effective conductivity and permeability: Virtual materials testing. International Journal of Solids and Structures, Elsevier, 2020, Special Issue on Physics and Mechanics of Random Structures: From Morphology to Material Properties, 184, pp.211-220. ⟨10.1016/j.ijsolstr.2019.03.028⟩. ⟨hal-02425310⟩

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