Hydrogen and dual fuel mode performing in engine with different combustion chamber shapes: Modelling and analysis using RSM-ANN technique

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dc.authorscopusid55340981300
dc.authorscopusid57225664913
dc.authorscopusid57205548894
dc.authorscopusid57057224800
dc.authorscopusid57190847465
dc.authorscopusid57193789174
dc.authorscopusid57202959651
dc.contributor.authorKhandal, Sanjeevakumar Veerasangappa
dc.contributor.authorRazak, A.
dc.contributor.authorVeza, I.
dc.contributor.authorAfzal, Asif
dc.contributor.authorAlwetaishi, Mamdooh
dc.contributor.authorShaik, S.
dc.contributor.authorAğbulut, Ümit
dc.date.accessioned2023-07-26T11:50:26Z
dc.date.available2023-07-26T11:50:26Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractThis study investigates the impacts of hydrogen (H2) induction along with injected liquid honne biodiesel (BHO)/uppage biodiesel (BUO) as secondary pilot fuel in diesel engine. The effects of compression ratio (CR), hydrogen fuel flow rate (HFR) and different combustion chamber shapes in dual fuel (DF) mode were investigated. In the first phase of experiments, the effects of three different CR (15.5, 16.5, and 17.5) on engine efficacy and emission were presented. In the second phase, the effects of three HFR (0.1, 0.17, and 0.24 kg/h) on engine efficacy and emission, as well as the maximum possible HFR were reported. In the last phase, performance with different combustion chambers i.e., Hemispherical Combustion Chamber (HCC), Toroidal Reentrant Combustion Chamber (TRCC), and Toroidal Combustion Chamber (TCC) at maximum possible CR and HFR was highlighted. The study revealed that for knock free operation of the DF engine, the highest probable HFR was 0.24 kg/h at a CR of 17.5, fuel IT of 27obefore top dead center (bTDC) and injector opening pressure (IOP) of 250 bar. The toroidal re-entrant combustion chamber (TRCC) shape yielded 8%–12% better brake thermal efficiency (BTE) with lower emissions but 20–29% higher oxides of nitrogen (NOx) at 80% load in DF mode as contrasted to the single CI mode. Both peak pressure (PP) and heat release rate (HRR) were 12–15% higher. Response surface methodology (RSM) was used to design the experiments and to carry the optimization process. Artificial Neural Network (ANN) was used to forecast the performance and emission behaviors of the test engine. The findings demonstrated that RSM and ANN were excellent modelling techniques with good accuracy. In addition, ANN's prediction performance (R2 = 0.975 for BTE) was somewhat better than RSM's (R2 = 0.974 for BTE). Both the techniques were found to be successful in terms of agreement with experimental findings with ratios varying from 95% to 98% respectively. The prediction of BTE and NOx was also carried using different machine learning algorithms. It can be seen that R2 value for these models were slightly lower than ANN and RSM models indicating good predicting capability of ANN modelling. © 2022 Hydrogen Energy Publications LLCen_US
dc.description.sponsorshipTaif University, TU: TURSP-2020/196en_US
dc.description.sponsorshipThe authors acknowledge the support received by Taif University Researchers Supporting Project number ( TURSP-2020/196 ), Taif University, Taif, Saudi Arabia.en_US
dc.identifier.doi10.1016/j.ijhydene.2022.09.193
dc.identifier.issn0360-3199
dc.identifier.scopus2-s2.0-85141769525en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2022.09.193
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12347
dc.identifier.wosWOS:001139446000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAğbulut, Ümit
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofInternational Journal of Hydrogen Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectHonne biodiesel (BHO)en_US
dc.subjectHydrogen fuel flow Rate (HFR)en_US
dc.subjectResponse surface methodology (RSM)en_US
dc.subjectUppage oil biodiesel (BUO)en_US
dc.subjectBiodieselen_US
dc.subjectCombustion chambersen_US
dc.subjectHydrogenen_US
dc.subjectHydrogen fuelsen_US
dc.subjectSurface propertiesen_US
dc.subjectThermal efficiencyen_US
dc.subjectArtificial neural networken_US
dc.subjectBrake thermal efficiencyen_US
dc.subjectCombustion chamber shapeen_US
dc.subjectDual-fuelsen_US
dc.subjectFuel flow ratesen_US
dc.subjectHonne biodiesel (BHO)en_US
dc.subjectHydrogen fuel flow rateen_US
dc.subjectResponse surface methodologyen_US
dc.subjectResponse-surface methodologyen_US
dc.subjectUppage oil biodiesel ((BHO)/uppage biodiesel)en_US
dc.subjectNeural networksen_US
dc.titleHydrogen and dual fuel mode performing in engine with different combustion chamber shapes: Modelling and analysis using RSM-ANN techniqueen_US
dc.typeArticleen_US

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