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    Hydrogen and dual fuel mode performing in engine with different combustion chamber shapes: Modelling and analysis using RSM-ANN technique
    (Elsevier Ltd, 2022) Khandal, Sanjeevakumar Veerasangappa; Razak, A.; Veza, I.; Afzal, Asif; Alwetaishi, Mamdooh; Shaik, S.; Ağbulut, Ümit
    This 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 LLC
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    Numerical and experimental investigation of CI engine behaviours supported by zinc oxide nanomaterial along with diesel fuel
    (Elsevier Ltd, 2021) Rajak, U.; Ağbulut, Ü.; Veza, I.; Dasore, A.; Sarıdemir, S.; Verma, T. N.
    Zinc oxide nano additives of 250 ppm, 500 ppm, and 1000 ppm were blended with diesel fuel. The prepared fuels which were designated as DF-250 ppm ZnO, DF-500 ppm ZnO, and DF - 1000 ppm ZnO were tested for engine characteristics along with diesel fuel (DF) in a standard bench-scale engine. All the tests were carried out at different speeds of the engine ranging between 2000 and 3000 rpm with unvarying engine load and advanced injection timing. The outcomes from these experiments exhibited higher brake thermal efficiency and cylinder pressure for fuels with ZnO nano additives than that of diesel fuel. The emission gas temperature and brake-specific fuel consumption were noticed to be lower for fuels blended with ZnO nano additive than those of diesel fuel. The level of SPM emissions also increased in compression ratio from CR = 15.5 to CR = 16.5, but starting from CR of 17.5, the SPM emissions for all the investigated fuels were relatively constant with a slight decrease at the maximum compression ratio. In addition, at all test conditions, NO and SO2 emissions from the engine tail pipe were higher with ZnO mixed diesel fuel. © 2021 Elsevier Ltd