Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method

Marmet, Philip; Holzer, Lorenz; Hocker, Thomas; Muser, Vinzenz; Boiger, Gernot Kurt; Fingerle, Mathias; Reeb, Sarah; Michel, Dominik; Brader, Joseph M.

doi:10.1039/D3YA00332A

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29152

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method
Authors:	Marmet, Philip Holzer, Lorenz Hocker, Thomas Muser, Vinzenz Boiger, Gernot Kurt Fingerle, Mathias Reeb, Sarah Michel, Dominik Brader, Joseph M.
et. al:	No
DOI:	10.1039/D3YA00332A 10.21256/zhaw-29152
Published in:	Energy Advances
Volume(Issue):	2
Issue:	11
Page(s):	1942
Pages to:	1967
Issue Date:	9-Oct-2023
Publisher / Ed. Institution:	Royal Society of Chemistry
ISSN:	2753-1457
Language:	English
Subjects:	Solid oxide fuel cell (SOFC); Stochastic geometry; Pluri-Gaussian method; Gaussian random fields; Digital material design; Virtual material testing; Microstructure optimization; Effective transport property; Mixed ionic electronic conductor (MIEC); Titanate; Gadolinium doped Ceria (CGO); Nickel-free SOC electrode
Subject (DDC):	005: Computer programming, programs and data 621.3: Electrical, communications, control engineering
Abstract:	Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in 3D-imaging, experimental microstructure investigations are typically limited to only a few tomography analyses. In this publication, a DMD workflow is presented for extensive virtual microstructure variation, which is based on a limited number of real tomography analyses. Real 3D microstructures, which are captured with FIB-tomography from LSTN-CGO anodes, are used as a basis for stochastic modeling. Thereby, digital twins are constructed for each of the three real microstructures. The virtual structure generation is based on the pluri-Gaussian method (PGM). In order to match the properties of selected virtual microstructures (i.e., digital twins) with real structures, the construction parameters for the PGM-model are determined by interpolation of a database of virtual structures. Moreover, the relative conductivities of the phases are optimized with morphological operations. The digital twins are then used as anchor points for virtual microstructure variation of LSTN-CGO anodes, covering a wide range of compositions and porosities. All relevant microstructure properties are determined using our standardized and automated microstructure characterization procedure, which was recently published. The microstructure properties can then e.g., be used as input for a multiphysics electrode model to predict the corresponding anode performances. This set of microstructure properties with corresponding performances is then the basis to provide design guidelines for improved electrodes. The PGM-based structure generation is available as a new Python app for the GeoDict software package.
Further description:	Zugehörige Dateien: https://zenodo.org/records/7744110 https://doi.org/10.1039/D3YA00132F https://doi.org/10.21256/zhaw-28430
URI:	https://digitalcollection.zhaw.ch/handle/11475/29152
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Computational Physics (ICP)
Published as part of the ZHAW project:	Versatile oxide fuel cell microstructures employing WGS active titanate anode current collectors compatible to ferritic stainless steel interconnects (VOLTA) GeoCloud – Simulation Software for Cloud-based Digital Microstructure Design of New Fuel Cell Materials
Appears in collections:	Publikationen School of Engineering

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Marmet, P., Holzer, L., Hocker, T., Muser, V., Boiger, G. K., Fingerle, M., Reeb, S., Michel, D., & Brader, J. M. (2023). Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method. Energy Advances, 2(11), 1942–1967. https://doi.org/10.1039/D3YA00332A

Marmet, P. et al. (2023) ‘Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method’, Energy Advances, 2(11), pp. 1942–1967. Available at: https://doi.org/10.1039/D3YA00332A.

P. Marmet et al., “Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method,” Energy Advances, vol. 2, no. 11, pp. 1942–1967, Oct. 2023, doi: 10.1039/D3YA00332A.

MARMET, Philip, Lorenz HOLZER, Thomas HOCKER, Vinzenz MUSER, Gernot Kurt BOIGER, Mathias FINGERLE, Sarah REEB, Dominik MICHEL und Joseph M. BRADER, 2023. Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method. Energy Advances. 9 Oktober 2023. Bd. 2, Nr. 11, S. 1942–1967. DOI 10.1039/D3YA00332A

Marmet, Philip, Lorenz Holzer, Thomas Hocker, Vinzenz Muser, Gernot Kurt Boiger, Mathias Fingerle, Sarah Reeb, Dominik Michel, and Joseph M. Brader. 2023. “Stochastic Microstructure Modeling of SOC Electrodes Based on a Pluri-Gaussian Method.” Energy Advances 2 (11): 1942–67. https://doi.org/10.1039/D3YA00332A.

Marmet, Philip, et al. “Stochastic Microstructure Modeling of SOC Electrodes Based on a Pluri-Gaussian Method.” Energy Advances, vol. 2, no. 11, Oct. 2023, pp. 1942–67, https://doi.org/10.1039/D3YA00332A.