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    Comparison of effects of nanofluid utilization (Al2O3, SiO2, TiO2) with reference water in automotive radiators on exergetic properties of diesel engines
    (Springer Int Publ Ag, 2021) Erkan, Anil; Tuccar, Goekhan; Tosun, Erdi; Ozgur, Tayfun
    In this study, nanofluids formed by using ethylene glycol and three kinds of nanoparticles such as Al2O3, SiO2, and TiO2 were added to the four-stroke internal combustion engine radiator and compared with the conventional coolant (pure water). This comparison is based on the exergy performances which are the main theme of the second law of thermodynamics. The tests were carried out at a fixed engine speed of 1800 rpm using diesel fuel, and the outputs were obtained from the test setup experimentally. A total of six nanofluid tests were performed on two different dispersions (0.2% and 0.4%). As a result of this study, the best exergy efficiency was obtained by using TiO2 particles with a 35.67% value. Increasing the percentage of nanoparticles in the fluid from 0.2 to 0.4 positively affected efficiency. Pure water generally lagged behind nanofluid performance in experimental parameters. Compared to conventional coolant (pure water), the lowest exhaust temperature value was measured by using an Al2O3/Ethylene Glycol mixture with a difference of 59 K. Also, by using Al2O3 nanoparticles as a coolant, 8.858 kW of exergy exhaust value was obtained. This is the best emission value measured in the experimental study. While calculating values close to each other in the use of other nanoparticles, the worst exergy exhaust results were obtained by using the conventional refrigerant. Consequently, in this paper, exergetic outputs such as exergetic efficiency, exergy destruction, exergy heat, exergy work, exergy total exhaust, and entropy production rate were calculated for pure water and each nanofluid.
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    Estimation of crack propagation in polymer electrolyte membrane fuel cell under vibration conditions
    (Pergamon-Elsevier Science Ltd, 2017) Calik, Ahmet; Yildirim, Sefa; Tosun, Erdi
    In transportation applications, the main reasons of mechanical damage in polymer electrolyte membrane fuel cell (PEMFC) are road-induced vibrations and impact loads. The most vulnerable place of these cells is the interface between membrane and catalyst layer in the membrane electrode assembly (MEA). Hence, studies on mechanical strength of PEMFC should focus on that interface. The objective of present study lies in the fact that employing a prediction method to investigate the damage propagation behavior of vibration applied PEMFC using artificial neural network (ANN). The data available in the literature are used to constitute an ANN model. Three-layer model; input, hidden and output, are used for construction of ANN structure. Initial delamination length (a), amplitude (A), frequency (omega) and time (t) are used as input neurons whereas delamination length is output. Levenberg-Marquardt algorithm is selected as learning algorithm. On the other hand, number of hidden layer neuron is decided with the use of different neuron, numbers by trial and error method. It is concluded that prediction capability of ANN model is in allowable limits and model can be suggested as efficient way of delamination length estimation. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel-biodiesel blends
    (Pergamon-Elsevier Science Ltd, 2017) Uludamar, Erinc; Tosun, Erdi; Tuccar, Gokhan; Yildizhan, Safak; Calik, Ahmet; Yildirim, Sefa; Serin, Hasan
    There are two main reasons of alternative fuel search of scientists: environmental problems resulted from combustion of fossil fuels and limited reserves of crude oil. Biodiesel and Hydrogen (H-2) are two of the most promising alternative fuels with their environmental friendly combustion profiles. The aim of this study was to evaluate vibration level of a hydroxyl (HHO) gas generator installed and diesel engine using different kinds of biodiesel fuels. In this study, at different flow rates, the effect of HHO gas addition on engine vibration performance was investigated with a Mitsubishi Canter 4D34 -2A diesel engine. HHO gas introduced to the test engine via its intake manifold with 2, 4 and 6 L per minute (LPM) flow rates when the engine was fuelled with sunflower, canola, and corn biodiesels. The vibration data was collected between 1200 and 2400 rpm engine speeds by 300 rpm intervals. Finally, artificial neural network (ANN) approach was conducted in order to predict the effect of fuel properties and HHO amount on engine vibration level. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas addition
    (Elsevier Sci Ltd, 2017) Celebi, Kerimcan; Uludamar, Erinc; Tosun, Erdi; Yildizhan, Safak; Aydin, Kadir; Ozcanli, Mustafa
    Replacing conventional diesel fuel has gained great interest owing to environmental issues. Therefore all effect of alternative fuels must be well-known in order to forthcoming engine development concern. In this study acoustic and vibration effect of biodiesel and their blends were investigated on an unmodified diesel engine which enriched with natural gas. Throughout this work, experimental engine was fuelled with conventional diesel, sunflower and canola biodiesel blends with ratio of 20% and 40%, by volume. Furthermore, natural gas was inducted through intake manifold at various flow rates; 5 L/min, 10 L/min, and 15 L/min with intake air. Experiments revealed that, compared to conventional diesel fuel, sunflower and canola biodiesels decreased sound pressure level and vibration of the test engine. Addition of natural gas decreased the values even more. Furthermore, exhaust emission of the engine has been evaluated. Beside experimental study, an artificial neural network model was developed in order to predict sound pressure level and vibration of the engine. Artificial neural network results showed that, generated models were capable of estimation of parameters with high accuracy. (C) 2017 Elsevier Ltd. All rights reserved.
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    Investigation the fuel properties of apricot kernel biodiesel and diesel-biodiesel fuel blends
    (Elsevier Science Bv, 2018) Ozgur, Ceyla; Yakaryilmaz, Ali Cem; Tosun, Erdi; Akar, Mustafa Atakan; Ozgur, Tayfun
    [Abstract Not Available]
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    Investigations of Effects of Density and Viscosity of Diesel and Biodiesel Fuels on NOx and other Emission Formations
    (2018) Tüccar, Gökhan; Tosun, Erdi; Uludamar, Erinç
    In the present study, effects of fuel properties; such as viscosity and density of different biodiesels on engine exhaust emissions were investigated. Sunflower, corn and canola oils were used as raw materials of biodiesel fuels which were produced via transesterification method. In experiments, pure biodiesels were used as test fuels and diesel was used as reference fuel. The results indicated that viscosity and density of biodiesel fuels went up with a higher proportion of biodiesel. Engine experiments, which were conducted on a Mitsubishi Canter 4D34-2A, four-stroke, four-cylinder diesel engine indicated that carbon monoxide (CO) emission reduced with the utilization of fuels that have higher viscosity and density. On the other hand, carbon dioxide (CO2) and nitrogen oxide (NOx) emission had opposite trend. Both emission values were increased with higher biodiesel ratios.