Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network

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dc.contributor.authorÖzgören, Yaşar Önder
dc.contributor.authorÇetinkaya, Selim
dc.contributor.authorSarıdemir, Suat
dc.contributor.authorÇiçek, Adem
dc.contributor.authorKara, Fuat
dc.date.accessioned2020-04-30T23:21:20Z
dc.date.available2020-04-30T23:21:20Z
dc.date.issued2013
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.descriptionKARA, Fuat/0000-0002-3811-3081en_US
dc.descriptionWOS: 000316831100038en_US
dc.description.abstractIn this study, an artificial neural network (ANN) model was developed to predict the torque and power of a beta-type Stirling engine using helium as the working fluid. The best results were obtained by 5-11-7-1 and 5-13-7-1 network architectures, with double hidden layers for the torque and power respectively. For these network architectures, the Levenberg-Marquardt (LM) learning algorithm was used. Engine performance values predicted with the developed ANN model were compared with the actual performance values measured experimentally, and substantially coinciding results were observed. After ANN training, correlation coefficients (R-2) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively. These results showed that the ANN is an acceptable model for prediction of the torque and power of the beta-type Stirling engine. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.enconman.2012.12.007en_US
dc.identifier.endpage368en_US
dc.identifier.issn0196-8904
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage357en_US
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2012.12.007
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4178
dc.identifier.volume67en_US
dc.identifier.wosWOS:000316831100038en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergy Conversion And Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBeta type Stirling engineen_US
dc.subjectHeliumen_US
dc.subjectANNen_US
dc.subjectEngine performanceen_US
dc.titlePredictive modeling of performance of a helium charged Stirling engine using an artificial neural networken_US
dc.typeArticleen_US

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