Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine

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dc.contributor.authorYıldırım, Sefa
dc.contributor.authorTosun, Erdi
dc.contributor.authorÇalık, Ahmet
dc.contributor.authorUluocak, İhsan
dc.contributor.authorAvsar, Ercan
dc.date.accessioned2021-02-19T21:16:43Z
dc.date.available2021-02-19T21:16:43Z
dc.date.issued2019
dc.departmentALKÜ
dc.descriptionCalik, Ahmet/0000-0001-7425-4546; Yildirim, Sefa/0000-0002-9204-5868; Avsar, Ercan/0000-0002-1356-2753
dc.description.abstractThe present paper investigates the prediction of vibration, noise level, and emission characteristics of a four-stroke, four-cylinder diesel engine fueled with sunflower, canola, and corn biodiesel blends while H-2 injected through inlet manifold using two different artificial intelligence methods: artificial neural network(ANN) and support vector machines(SVM). The aim of using these methods is to predict vibration, noise, carbon monoxide (CO), CO2, and NOx based on the initial experimental study by varying engine speed, blends of biodiesel, and H-2 energy substitution ratio. Experimental data weregathered from the literature. For theANN method, LevenbergMarquardt backpropagation training algorithm with logarithmic sigmoid and linear transfer function for hidden and output layers, respectively, gives the best results for prediction of vibration, noise, and emission characteristics. For SVM, a regression model is implemented with Gaussian kernel function. Results show that the ANN performs better than SVM, and the bestmean average percent error and R-2 for the models developed are 2.03 and 0.988 for vibration acceleration, 0.39 and 0.9615 for noise, 7.27 and 0.8549 for CO, 5.09 and 0.9398 for NOx, and2.21 and 0.993 for CO2 values, respectively. Eventually, it is found that the ANN method is a good choice for simulation and prediction of dual fueled hydrogen sunflower, canola, and corn biodiesel blends.
dc.identifier.doi10.1080/15567036.2018.1550540
dc.identifier.endpage2206en_US
dc.identifier.issn1556-7036
dc.identifier.issn1556-7230
dc.identifier.issue18en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage2194en_US
dc.identifier.urihttps://doi.org/10.1080/15567036.2018.1550540
dc.identifier.urihttps://hdl.handle.net/20.500.12868/528
dc.identifier.volume41en_US
dc.identifier.wosWOS:000468365900003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofEnergy Sources Part A-Recovery Utilization And Environmental Effects
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectVibration
dc.subjectnoise
dc.subjecthydrogen fuel
dc.subjectartificial neural network
dc.subjectsupport vector machines
dc.subjectbiodiesel
dc.titleArtificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine
dc.typeArticle

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