Quantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network

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dc.authoridTimurkutluk, Bora/0000-0001-6916-7720
dc.authoridSEVUK, YELDA/0009-0001-2066-3245
dc.authoridGENC, Omer/0000-0003-0849-6867
dc.contributor.authorTimurkutluk, Bora
dc.contributor.authorCiflik, Yelda
dc.contributor.authorSonugur, Guray
dc.contributor.authorAltan, Tolga
dc.contributor.authorGenc, Omer
dc.date.accessioned2024-11-07T13:32:22Z
dc.date.available2024-11-07T13:32:22Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractVirtual solid oxide fuel cell (SOFC) electrode microstructures composed of pore, electrolyte and catalyst phases with various particle sizes and volume fractions are reconstructed to design high-performance electrodes by investigating the role of microstructural properties on the electrodes and thereby the cell performance. The active TPB (triple phase boundary) densities in these microstructures are numerically measured and the data are used to train numerous artificial neural networks established with different model parameters and learning methods. Based on the results of 10,000 trainings of each model, the network that employs a backpropagation method of Bayesian regulation and has 2 hidden layers with 15 neurons is found to be the best one. It is then used to simulate new cases, whose parameters are in the range of those used in training. Further validation of the best network is also performed by considering a few randomly selected cases. The simulation results providing active TPB densities quantitatively are discussed regarding the microstructural properties. The overall results reveal that active TPBs can be increased by reducing the particle size of the phases and volume fraction of any phase should be selected according to the particle size to improve the number of active TPBs.
dc.identifier.doi10.1016/j.fuel.2023.129687
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85170071448
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2023.129687
dc.identifier.urihttps://hdl.handle.net/11480/15357
dc.identifier.volume357
dc.identifier.wosWOS:001074700900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofFuel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectSolid oxide fuel cell
dc.subjectMicrostructural electrode design
dc.subjectSynthetic microstructure
dc.subjectThree/triple phase boundaries
dc.subjectArtificial neural network
dc.titleQuantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network
dc.typeArticle

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