Prediction of engine performance for an alternative fuel using artificial neural network

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dc.contributor.authorÇay, Yusuf
dc.contributor.authorÇiçek, Adem
dc.contributor.authorKara, Fuat
dc.contributor.authorSağıroğlu, Selami
dc.date.accessioned2020-04-30T23:21:17Z
dc.date.available2020-04-30T23:21:17Z
dc.date.issued2012
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.descriptionKARA, Fuat/0000-0002-3811-3081en_US
dc.descriptionWOS: 000301026600027en_US
dc.description.abstractThis study deals with artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm was developed. Then, the performance of the ANN predictions was measured by comparing the predictions with the experimental results. Engine speed, engine torque, fuel flow, intake manifold mean temperature and cooling water entrance temperature have been used as the input layer, while brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature have also been used separately as the output layer. After training, it was found that the R-2 values are close to 1 for both training and testing data. RMS values are smaller than 0.015 and mean errors are smaller than 3.8% for the testing data. This shows that the developed ANN model is a powerful one for predicting the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of internal combustion engines. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.applthermaleng.2011.11.019en_US
dc.identifier.endpage225en_US
dc.identifier.issn1359-4311
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage217en_US
dc.identifier.urihttps://doi.org/10.1016/j.applthermaleng.2011.11.019
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4168
dc.identifier.volume37en_US
dc.identifier.wosWOS:000301026600027en_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.ispartofApplied Thermal Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpark ignition engineen_US
dc.subjectMethanol engine performanceen_US
dc.subjectArtificial neural networken_US
dc.titlePrediction of engine performance for an alternative fuel using artificial neural networken_US
dc.typeArticleen_US

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