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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29152
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dc.contributor.authorMarmet, Philip-
dc.contributor.authorHolzer, Lorenz-
dc.contributor.authorHocker, Thomas-
dc.contributor.authorMuser, Vinzenz-
dc.contributor.authorBoiger, Gernot Kurt-
dc.contributor.authorFingerle, Mathias-
dc.contributor.authorReeb, Sarah-
dc.contributor.authorMichel, Dominik-
dc.contributor.authorBrader, Joseph M.-
dc.date.accessioned2023-11-17T10:10:42Z-
dc.date.available2023-11-17T10:10:42Z-
dc.date.issued2023-10-09-
dc.identifier.issn2753-1457de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29152-
dc.descriptionZugehörige Dateien: https://zenodo.org/records/7744110 https://doi.org/10.1039/D3YA00132F https://doi.org/10.21256/zhaw-28430de_CH
dc.description.abstractDigital 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.de_CH
dc.language.isoende_CH
dc.publisherRoyal Society of Chemistryde_CH
dc.relation.ispartofEnergy Advancesde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSolid oxide fuel cell (SOFC)de_CH
dc.subjectStochastic geometryde_CH
dc.subjectPluri-Gaussian methodde_CH
dc.subjectGaussian random fieldsde_CH
dc.subjectDigital material designde_CH
dc.subjectVirtual material testingde_CH
dc.subjectMicrostructure optimizationde_CH
dc.subjectEffective transport propertyde_CH
dc.subjectMixed ionic electronic conductor (MIEC)de_CH
dc.subjectTitanatede_CH
dc.subjectGadolinium doped Ceria (CGO)de_CH
dc.subjectNickel-free SOC electrodede_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleStochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian methodde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitute of Computational Physics (ICP)de_CH
dc.identifier.doi10.1039/D3YA00332Ade_CH
dc.identifier.doi10.21256/zhaw-29152-
zhaw.funding.euNot specifiedde_CH
zhaw.issue11de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1967de_CH
zhaw.pages.start1942de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedMicrostructure analysisde_CH
zhaw.webfeedMultiphysics Modelingde_CH
zhaw.funding.zhawVersatile oxide fuel cell microstructures employing WGS active titanate anode current collectors compatible to ferritic stainless steel interconnects (VOLTA)de_CH
zhaw.funding.zhawGeoCloud – Simulation Software for Cloud-based Digital Microstructure Design of New Fuel Cell Materialsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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.


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