Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol-gasoline blend fuel

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dc.contributor.authorSerdar Yücesu, H.
dc.contributor.authorSözen, Adnan
dc.contributor.authorTopgul, Tolga
dc.contributor.authorArcaklioğlu, Erol
dc.date.accessioned2020-06-25T17:43:59Z
dc.date.available2020-06-25T17:43:59Z
dc.date.issued2007
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207;
dc.description.abstractThis study consists of two cases: (i) The experimental analysis: Ethanol obtained from biomass can be used as a fuel in spark ignition engines. As renewable energy source ethanol, due to the high octane number, low emissions and high engine performance is preferred alternative fuel. First stage of this study, ethanol-unleaded gasoline blends (E10, E20, E40 and E60) were tested in a single cylinder, four-stroke spark ignition and fuel injection engine. The tests were performed by varying the ignition timing, relative air-fuel ratio (RAFR) and compression ratio at a constant speed of 2000 rpm and at wide open throttle (WOT). Effect of ethanol-unleaded gasoline blends and tests variables on engine torque and specific fuel consumption were examined experimentally. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the engine torque and specific fuel consumption based on the ignition timing, RAFR and compression ratio at a constant speed of 2000 rpm and at WOT for different fuel densities using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained Levenberg-Marquardt (LM) algorithm with five neurons in the hidden layer, which made it possible to the engine torque and specific fuel consumption with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the RZ values are 0.999996 and 0.999991 for, the engine torque and specific fuel consumption, respectively. Similarly, these values for testing data are 0.999977 and 0.999915, respectively. As seen from the results of mathematical modeling, the calculated engine torque and specific fuel consumption are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.applthermaleng.2006.07.027
dc.identifier.endpage368en_US
dc.identifier.issn1359-4311
dc.identifier.issue2-3en_US
dc.identifier.scopus2-s2.0-33749651573
dc.identifier.scopusqualityQ1
dc.identifier.startpage358en_US
dc.identifier.urihttps://doi.org10.1016/j.applthermaleng.2006.07.027
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3970
dc.identifier.volume27en_US
dc.identifier.wosWOS:000241706500009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofApplied Thermal Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectalternative fuelen_US
dc.subjectgasoline-ethanol blendsen_US
dc.subjectartificial neural networken_US
dc.subjectcompression ratioen_US
dc.subjectbrake specific fuel consumptionen_US
dc.titleComparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol-gasoline blend fuelen_US
dc.typeArticle

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