Estimation of crack propagation in polymer electrolyte membrane fuel cell under vibration conditions

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dc.authoridCalik, Ahmet/0000-0001-7425-4546
dc.authoridYildirim, Sefa/0000-0002-9204-5868
dc.authoridTosun, Erdi/0000-0001-5733-2047
dc.contributor.authorCalik, Ahmet
dc.contributor.authorYildirim, Sefa
dc.contributor.authorTosun, Erdi
dc.date.accessioned2025-01-06T17:44:02Z
dc.date.available2025-01-06T17:44:02Z
dc.date.issued2017
dc.description1st International Mediterranean Science and Engineering Congress (IMSEC) -- OCT 26-28, 2016 -- Adana, TURKEY
dc.description.abstractIn transportation applications, the main reasons of mechanical damage in polymer electrolyte membrane fuel cell (PEMFC) are road-induced vibrations and impact loads. The most vulnerable place of these cells is the interface between membrane and catalyst layer in the membrane electrode assembly (MEA). Hence, studies on mechanical strength of PEMFC should focus on that interface. The objective of present study lies in the fact that employing a prediction method to investigate the damage propagation behavior of vibration applied PEMFC using artificial neural network (ANN). The data available in the literature are used to constitute an ANN model. Three-layer model; input, hidden and output, are used for construction of ANN structure. Initial delamination length (a), amplitude (A), frequency (omega) and time (t) are used as input neurons whereas delamination length is output. Levenberg-Marquardt algorithm is selected as learning algorithm. On the other hand, number of hidden layer neuron is decided with the use of different neuron, numbers by trial and error method. It is concluded that prediction capability of ANN model is in allowable limits and model can be suggested as efficient way of delamination length estimation. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.ijhydene.2017.02.119
dc.identifier.endpage23351
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue36
dc.identifier.scopus2-s2.0-85015317080
dc.identifier.scopusqualityQ1
dc.identifier.startpage23347
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2017.02.119
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2895
dc.identifier.volume42
dc.identifier.wosWOS:000412033800074
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectPolymer electrolyte membrane fuel cell
dc.subjectMechanical vibration
dc.subjectCrack propagation
dc.subjectArtificial neural network
dc.titleEstimation of crack propagation in polymer electrolyte membrane fuel cell under vibration conditions
dc.typeConference Object

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