Artificial neural network based modelling of performance of a beta-type Stirling engine

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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.accessioned2020-04-30T22:39:40Z
dc.date.available2020-04-30T22:39:40Z
dc.date.issued2013
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.descriptionWOS: 000322197800003en_US
dc.description.abstractIn this article, artificial neural network has been used in order to predict the power (P) and torque (T) values obtained from a beta-type Stirling engine that uses air as working fluid. Experimental data have been obtained for different charge pressures and hot source temperatures using ZrO2-coated and uncoated displacers. The closest artificial neural network results to experimental torque and power values were obtained with double hidden layer 5-13-9-1 and 5-13-7-1 network architectures, respectively. The best prediction values were obtained by Levenberg-Marquardt learning algorithm. Correlation coefficient (R-2) for the torque values were 0.998331 and 0.997231 for the training and test sets, respectively, while R-2 value for power values were 0.998331 and 0.997231 for the training and test sets, respectively. R-2 values show that the developed artificial neural network is an acceptable and powerful modelling technique in predicting the torque and power values of the beta-type Stirling engine.en_US
dc.identifier.doi10.1177/0954408912455763en_US
dc.identifier.endpage177en_US
dc.identifier.issn0954-4089
dc.identifier.issn2041-3009
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage166en_US
dc.identifier.urihttps://doi.org/10.1177/0954408912455763
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2793
dc.identifier.volume227en_US
dc.identifier.wosWOS:000322197800003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings Of The Institution Of Mechanical Engineers Part E-Journal Of Process Mechanical Engineeringen_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.subjectairen_US
dc.subjectartificial neural networksen_US
dc.subjectengine performanceen_US
dc.titleArtificial neural network based modelling of performance of a beta-type Stirling engineen_US
dc.typeArticleen_US

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