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    Diesel engine vibration analysis using artificial neural networks method: Effect of NH3 additive in biodiesels
    (Elsevier Ltd, 2024) Raja Ahsan Shah, Raja Mazuir; Böyükdipi, Ömer; Tüccar, Gökhan; Al-Otoom, Awni; Soyhan, Hakan Serhad
    Diesel engine parameters, such as fuel and its additives, play an essential role in minimising the effects of engine vibration. This study aimed to use artificial neural networks (ANN) to model and analyse diesel engine vibration characteristics at different engine speeds using NH3 as an additive in hazelnut (HD), peanut (PD), and waste-cooking oil (WD) biodiesels. The results showed good correlations between the ANN models and experimental results using regression analysis methods. The ANN models for diesel engines showed high accuracy. The ANN models indicated that a 5 % NH3 additive decreased engine vibration for HD and PD. In comparison, 10 % and 15 % NH3 additive ratios increased engine vibration for HD, PD, and WD due to low combustion quality. The lowest vibration levels occurred with P100, P95A5, P90A10, and P85A15 at 1200 rpm. H100 and H95A5 produced the highest diesel engine resultant vibration (DERV) values. All ANN models generated the lowest and highest DERV values at 1200 rpm and 2100 rpm, respectively. The RMS method showed that H95A5, P85A15, and W85A15 contributed the most to diesel engine vibration. Using a low amount of NH3 additive positively affected DERV for HD and PD but not for WD. © 2024
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    Öğe
    Experimental investigation and artificial neural networks (ANNs) based prediction of engine vibration of a diesel engine fueled with sunflower biodiesel - NH3 mixtures
    (Elsevier Sci Ltd, 2021) Boyukdipi, Omer; Tuccar, Gokhan; Soyhan, Hakan Serhad
    Ammonia (NH3) has a high utilization potential in the world's fuel sectors. Therefore, it is recognized as an important alternative according to other conventional fuels. Besides, the fuel additive is intensively used to eliminate the deficiencies in engine emission, performance, and etc. This study was experimentally conducted to explore the effects of NH3 as a fuel additive on engine vibration parameter. In the scope of this study, NH3 was blended into the sunflower biodiesel at volumetric ratios of 5%, 10%, and 15%. Besides, all mixtures (B95A5, B90A10, and B85A15) were combusted under no-load conditions in varying engine speed (1200-2400 rpm) by using Mitsubishi Canter 4D31 diesel engine. Experimental vibration data were recorded with the accelerometer sensor placed on the engine block for all NH3 ratios. Artificial neural networks (ANN) model was generated to predict the experimental vibration results. Vibration data in the x-axis, y-axis, R (resultant), time (second), and engine speed (rpm) were considered as the input parameters to predict z-axis vibration data. Therefore, the z-axis was selected as the target parameter. All experimental vibration data was compared with the predicted vibration data and a high accuracy rate has been achieved in this study. As a result of the vibration experiments, the increment was observed in the vibration data when the NH3 additive was gradually increased. In this sense, it has been revealed that the NH3 additive in the sunflower biodiesel negatively affected the engine vibration. An apparent vibration change was observed for all the NH3 additive depending on the varying engine speed. The highest and lowest vibration data were respectively recorded as 12.96 m/s(2) and 9.12 m/s(2) for 15% and 5% NH3 additive. Besides, the accuracy rates of the generated ANNs models were respectively calculated as 99.206%, 99.675%, and 99.505% for the sunflower biodiesel blended with 5%, 10%, and 15% NH3 additive.
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    Öğe
    Optimisation of exhaust emissions, vibration, and noise of unmodified diesel engine fuelled with canola biodiesel-diesel blends with natural gas addition by using response surface methodology
    (Edp Sciences S A, 2024) Ozgur, Ceyla; Uludamar, Erinc; Soyhan, Hakan Serhad; Shah, Raja Mazuir Raja Ahsan
    The paper presents methods to determine the optimum input parameters of CNG addition, biodiesel blend ratio, and engine speed to improve engine responses in terms of exhaust emissions, vibration, and noise of CNG-biodiesel-diesel fuelled engines. Box-Behnken based on response surface methodology was used to predict and optimise input parameters. Variance analysis was applied to determine the significant relationship between the input parameters and engine responses. At optimum input parameters (CNG addition = 9.24 L/min, biodiesel blend ratio = 40%, engine speed = 1524.24 rpm), the optimum engine responses of NOx, CO, CO2, O2, engine vibration acceleration, and noise were 93.77 ppm, 438.05 ppm, 1.47%, 18.59%, 37.17 m/s2 and 91.34 dB[A], respectively. In terms of coefficient determination of R2, the values were 99.11%, 99.22%, 99.41%, 99.70%, 98.65%, and 98.60% respectively. The correlation between the optimised result and the engine test result showed an acceptable error limit for NOx, CO, CO2, O2, engine vibration acceleration, and noise as 4.2%, 3.8%, 4.9%, 0.25%, 4.12%, and 0.17%, respectively.