Yazar "Celik, Mustafa Bahattin" seçeneğine göre listele

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    Dynamic Modeling and Simulation of a PEM Fuel Cell (PEMFC) during an Automotive Vehicle's Driving Cycle
    (Eos Assoc, 2020) Biberci, Mehmet Ali; Celik, Mustafa Bahattin
    Polymer Electrolyte Membrane Fuel Cells (PEMFCs) are the most appropriate type of fuel cells for application in vehicles due to their low operational temperature and high-power density. In this paper, a zero-dimensional, steady state thermodynamic modeling for an automotive 90kW PEMFC system has been built up in order to investigate the effects of operating parameters such as vehicle acceleration and operating pressure on the size of the system elements, heat and water system constitution, fuel consumption, and efficiency. A dynamic model was formed for the fuel cell power system in MATLAB. Power output and power losses of the system were investigated at 3atm operation pressures.
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    The effect of fusel oil and waste biodiesel fuel blends on a CI engine performance, emissions, and combustion characteristics
    (Springer, 2024) Ciftci, Burak; Karagoz, Mustafa; Aydin, Mustafa; Celik, Mustafa Bahattin
    In this study, experimental engine tests were conducted to investigate the combustion, performance, and emission characteristics of a diesel engine using a fuel blend composed of diesel, biodiesel, and fusel oil. In the study, which was carried out by using fuels obtained from different wastes together in a diesel engine. Seven different fuels were prepared for experiments by adding waste cooking oil (30% and 50%) and fusel oil (5% and 10%) by volume to commercial diesel fuel. The tests were carried out on the Lombardini LDW 1003 engine, a three-cylinder diesel engine, at four different engine loads (10, 20, 30, and 40 Nm), and a constant speed (2000 rpm). The experimental results revealed that the use of WCO generally led to increased NOx emissions which generally decreased with the fusel oil addition to the fuel mixture. Considering diesel fuel as a reference at maximum load conditions, there was a 12.63% increase in NOx emissions with 50% WCO. A 2.45% decrease in NOx emissions was achieved by adding 10% fusel oil. Furthermore, HC emissions decreased with the addition of both fusel oil and WCO at all load levels. When diesel fuel is taken as a reference at maximum load conditions, a 90% reduction in HC emissions was achieved by adding 50% WCO, and a 50% reduction in HC emissions was achieved by adding 10% fusel oil. Additionally, when diesel fuel is taken as a reference at maximum load condition, it was observed that a 0.05% increase in the maximum cylinder pressure value with the addition of 50% WCO and a 2.09% increase in the maximum cylinder pressure value with the addition of 10% fusel oil.
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    Modeling the effect of plastic oil obtained from XLPE cable waste on diesel engine performance and emission parameters with the response surface method
    (Edp Sciences S A, 2024) Sen, Sedat; Celik, Mustafa Bahattin
    The world's expanding population requires alternative energy sources to meet its energy needs. One such alternative is the efficient recovery of plastic waste through pyrolysis. The liquid produced from waste plastics via pyrolysis is a valuable commodity that may serve as fuel substituted for internal combustion engines. In this study, waste plastic oil (WPO) and diesel fuel (D100) blends (10%, 20%, 30%, 40%, and 50% by volume) obtained by pyrolysis of waste XLPE cables were experimentally investigated and analyzed using response surface methodology (RSM) to determine their effects on the combustion parameters of a four-stroke, single cylinder diesel engine at different engine loads (750, 1500, 2250, 3000, 3750, and 4500 W). A response surface model was constructed using a two-factor central composite complete design and analysis of variance based on the experimental results obtained. The model determined the optimum values of WPO ratio and engine load that correspond to one of the finest brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), hydrocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOx), and smoke emission levels. The study's optimization findings indicated that the optimal WPO ratio is 19.6%, and the optimal engine load is 2600 W. The BTE, BSFC, CO, HC, NOx, and smoke were found to be 22.3%, 332.3 g/kWh, 0.033%, 31.5 ppm, 397.9 ppm, and 1.63%, respectively, at the optimal WPO ratio and engine load. The R2 (correlation coefficient) values for BTE, BSFC, CO, HC, NOx, and smoke emissions were determined to be 99.95%, 97.76%, 98.10%, 99.74%, 99.74%, 99.79%, and 95.67%, respectively. The mean error rates, ranging from 0.64% to 4.27%, were deemed satisfactory when comparing the replies to the experimental data. The findings of this study demonstrated that the response surface method is a very efficient approach for forecasting and enhancing a diesel engine's performance and emission characteristics by using waste plastic oil.
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    Performance and Exhaust Emission Prediction of a SI Engine Fueled with I-amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based Optimization
    (Elsevier Sci Ltd, 2020) Uslu, Samet; Celik, Mustafa Bahattin
    In this study, effects of i-amyl alcohol/gasoline fuel blends on spark ignition (SI) engine performance and emissions were investigated experimentally, predicted by Artificial Neural Network (ANN) and optimized with Response Surface Methodology (RSM). Test engine was operated with pure gasoline and gasoline-isoamyl alcohol (isopentanol) fuel mixtures with different proportion at different engine speeds and various compression ratios (CR). With respect to obtained data from experiments, an ANN model, which is an Artificial Intelligence (AI) application, has been developed to estimate outputs such as brake mean effective pressure (BMEP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), nitrogen oxides (NOx), hydrocarbon emission (HC) and carbon monoxide (CO) according to CR, fuel blending ratio (by vol.%) and engine speed (rpm). In addition, RSM was applied to find suitable engine operating conditions. According to results, the ANN model can estimate performance and emission parameters of engine by correlation coefficient (R-2) between 0.94 and 0.99. It is seen that the max. mean relative error (MRE) is less than 7% compared with outcomes obtained from tests. The RSM study demonstrated that, i-AA ratio of 15% at 8.31 CR and 2957.58 rpm engine speed are the optimal engine operating parameters. In this way, the ANN model with RSM support was found to be an effective tool for predicting and optimizing engine outputs with minimum test.
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    Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2018) Uslu, Samet; Celik, Mustafa Bahattin
    In the present study, the performance and exhaust emissions of a single-cylinder, direct-injection and air-cooled diesel engine using diethyl ether (DEE)-diesel fuel mixtures were estimated by artificial neural networks (ANN). The test engine was run with pure diesel and diesel-DEE blends at different engine speeds and loads to obtain the test and training data required to build the ANN model. In the designed ANN model, brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), brake thermal efficiency (BTE), nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxides (CO) and smoke were selected as the output layer while engine load, engine speed and fuel blending ratio were selected as input layer. An ANN model was developed using 75% of the experimental results for training. The performance of the ANN model was measured by comparing the test data generated from the unused part of the training. According to the obtained data, ANN model predicts exhaust emissions and engine performance with a regression coefficient (R2) at 0.964-0.9878 interval. At the same time, mean relative error (MRE) values ranged from 0.51% to 4.8%. These results show that the ANN model is able to use for estimating low-power diesel engine emissions and performance. (C) 2018 Karabuk University. Publishing services by Elsevier B.V.