Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study

Neumann, Matthias; Abdallah, Bassam; Holzer, Lorenz; Willot, François; Schmidt, Volker

doi:10.1007/s11242-019-01240-y

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study
Authors:	Neumann, Matthias Abdallah, Bassam Holzer, Lorenz Willot, François Schmidt, Volker
et. al:	No
DOI:	10.1007/s11242-019-01240-y
Published in:	Transport in Porous Media
Volume(Issue):	128
Issue:	1
Page(s):	179
Pages to:	200
Issue Date:	2019
Publisher / Ed. Institution:	Springer
ISSN:	0169-3913 1573-1634
Language:	English
Subject (DDC):	621.3: Electrical, communications, control engineering
Abstract:	We compare two conceptually different stochastic microstructure models, i.e., a graph-based model and a pluri-Gaussian model, that have been introduced to model the transport properties of three-phase microstructures occurring, e.g., in solid oxide fuel cell electrodes. Besides comparing both models, we present new results regarding the relationship between model parameters and certain microstructure characteristics. In particular, an analytical expression is obtained for the expected length of triple phase boundary per unit volume in the pluri-Gaussian model. As a case study, we consider 3D image data which show a representative cutout of a solid oxide fuel cell anode obtained by FIB-SEM tomography. The two models are fitted to image data and compared in terms of morphological characteristics (like mean geodesic tortuosity and constrictivity) as well as in terms of effective transport properties. The Stokes flow in the pore phase and effective conductivities in the solid phases are computed numerically for realizations of the two models as well as for the 3D image data using Fourier methods. The local and effective physical responses of the model realizations are compared to those obtained from 3D image data. Finally, we assess the accuracy of the two methods to predict permeability as well as electronic and ionic conductivities of the anode.
URI:	https://hal.science/hal-02103129/ https://digitalcollection.zhaw.ch/handle/11475/19418
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Appears in collections:	Publikationen School of Engineering

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Neumann, M., Abdallah, B., Holzer, L., Willot, F., & Schmidt, V. (2019). Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study. Transport in Porous Media, 128(1), 179–200. https://doi.org/10.1007/s11242-019-01240-y

Neumann, M. et al. (2019) ‘Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study’, Transport in Porous Media, 128(1), pp. 179–200. Available at: https://doi.org/10.1007/s11242-019-01240-y.

M. Neumann, B. Abdallah, L. Holzer, F. Willot, and V. Schmidt, “Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study,” Transport in Porous Media, vol. 128, no. 1, pp. 179–200, 2019, doi: 10.1007/s11242-019-01240-y.

NEUMANN, Matthias, Bassam ABDALLAH, Lorenz HOLZER, François WILLOT und Volker SCHMIDT, 2019. Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study. Transport in Porous Media [online]. 2019. Bd. 128, Nr. 1, S. 179–200. DOI 10.1007/s11242-019-01240-y. Verfügbar unter: https://hal.science/hal-02103129/

Neumann, Matthias, Bassam Abdallah, Lorenz Holzer, François Willot, and Volker Schmidt. 2019. “Stochastic 3D Modeling of Three-Phase Microstructures for Predicting Transport Properties: A Case Study.” Transport in Porous Media 128 (1): 179–200. https://doi.org/10.1007/s11242-019-01240-y.

Neumann, Matthias, et al. “Stochastic 3D Modeling of Three-Phase Microstructures for Predicting Transport Properties: A Case Study.” Transport in Porous Media, vol. 128, no. 1, 2019, pp. 179–200, https://doi.org/10.1007/s11242-019-01240-y.