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Öğe Drying Behavior of Polyester Based Yarn Bobbins in a Hot-Air Bobbin Dryer(Trans Tech Publications Ltd, 2011) Cihan, Ahmet; Kahveci, Kamil; Akyol, Ugur; Akal, DincerDrying behavior of polyester based yarn bobbins (67% polyester, 33% viscose) was simulated for different drying air temperatures by a simultaneous heat and mass transfer model. In the model, it was assumed that mass transfer is occurred by the diffusion mechanism. In the study, firstly drying behavior of polyester bobbins for different drying air temperatures has been determined experimentally. The experiments were conducted on an experimental hot-air bobbin dryer designed and manufactured based on hot-air bobbin dryers used in textile industry. In the experimental setup, temperatures of different points of the bobbins were measured by thermocouples placed inside the bobbins and weight of the bobbins during the drying period were determined by a load cell. Then moisture ratio and temperature values of the model have been fitted to the experimental ones. The fit was performed by selecting the values for the diffusion coefficient and the thermal diffusivity in the model in such a way that these values make the sum of the squared differences between the experimental and the model results for moisture ratio and temperature minimum. The results show that there is a good agreement between the model results and the experimental ones. The results also show that temperature has a significant effect on mass transfer and temperature dependence of the diffusion coefficient may be explained by an Arrhenius type relation.Öğe Drying kinetics of cotton based yarn bobbins in a pressurized hot-air convective dryer(Sage Publications Ltd, 2017) Akal, Dincer; Kahveci, Kamil; Akyol, Ugur; Cihan, AhmetIn this study, the drying kinetics of cotton bobbin drying process in a pressurized hot-air convective bobbin dryer was investigated, and a drying model was introduced for the simulation of drying. Tests were conducted for drying temperatures of 70?, 80?, and 90?; effective drying air pressures of 1, 2, and 3 bars; three volumetric flow rates of 42.5, 55, and 67.5 m(3)/h; and for three different bobbin diameters of 10, 14, and 18cm. Optimum drying conditions were specified in terms of drying time and energy consumption. Results indicate that the total drying time depends significantly on the drying temperature, pressure, and volumetric flow rate. Results show that the minimum energy consumption is obtained for low values of drying air temperatures and pressures, and for moderate and high values of drying air volumetric flow rates. It was also found that the Page model is suitable for simulating the drying behavior of cotton yarn bobbins. Finally, results show that effective diffusion coefficient values are between 1.132x10(-7) m(2)/s and 3.453x10(-7) m(2)/s depending on the values of drying parameters.Öğe Effects of clogged air filter on power, torque, fuel consumption and emissions of diesel engines in tractors(Taylor & Francis Inc, 2023) Akal, Dincer; Selvi, IlkerIn this study, The pressure drop in the intake system when the new air filter is -1mbar, then -21mbar, -40 mbar depending on the pollution, and -65 mbar in a fully clogged state, were investigated. While the pressure drop in the intake system was -1mbar, the maximum power produced in the engine was initially 64.2 kW, and the maximum torque was 386 Nm. When the pressure drop reached -65 mbar, the engine's maximum power dropped to 58.1 kW and its maximum torque to 360 Nm. When the engine is at maximum torque (1400 rpm), the pressure drop in the system is -1 mbar, while the fuel consumption is 16.4 L/h, and when the pressure drop reaches -65 mbar, the fuel consumption increases to 34.3 L/h. Similarly, at maximum engine power (2200 rpm), the pressure drop in the system is -1 mbar, while fuel consumption is 14.8 L/h. When the pressure drop reaches -65 mbar, fuel consumption increases to 31.2 L/h. At the same time, it has been observed that while all the performance values of the engine have decreased depending on the air filter pollution level, harmful exhaust emissions CO, NO, and NOx increased significantly.Öğe Estimation of power output and thermodynamic analysis of standard and finned photovoltaic panels(Taylor & Francis Inc, 2023) Akyol, Ugur; Akal, Dincer; Durak, AhmetThis study deals with the estimation of power output and also thermodynamic analysis of two different photovoltaic panels. One of the panels is a standard photovoltaic module without fins (SPV), and the other one is a photovoltaic module with fins (FPV). First, a multi-layer feed-forward neural network structure is designed to estimate the daily power produced by photovoltaic modules. Furthermore, energy and exergy analyses were carried out to compare the performance of SPV and FPV panels. According to the thermodynamic analysis results using the experimental data obtained for two days (July 3, 2020 and August 4, 2020), it was calculated that the energy efficiency increased by a maximum of 8.77% and the exergy efficiency increased by a maximum of 25.9% in the FPV panel compared to the SPV panel. Moreover, considering the data obtained for each day during three months (July, August, and September), the total energy production increase in the FPV panel is approximately 6.7% compared to the SPV panel.Öğe Increasing energy and exergy efficiency in photovoltaic panels by reducing the surface temperature with thermoelectric generators(Taylor & Francis Inc, 2022) Akal, Dincer; Turk, SerayMany factors affect the efficiency of photovoltaic panels (PV), which convert solar energy directly into electrical energy. Among these factors, temperature is one of the most important one. While some of the radiation from the sun is converted into electrical energy, part of it emerges as heat energy. This causes the photovoltaic cells to heat up and reduce their electrical efficiency. Different methods are used in the literature to reduce the temperature in PV panels. In this study, in order to reduce the adverse effects caused by the high temperature in the PV panels, 30 Thermoelectric Generators (TEG) were applied to the back surface of the PV panel to increase the PV panel output power and to produce additional electrical energy. Energy and exergy analysis made on the data obtained from both PV panels in the climatic conditions of the installation site showed that the temperature of the PV panel is reduced, and the energy and exergy efficiency is increased with the TEG application. At the end of July, August, and September, when the experiments were carried out, an average of 8.4% more electrical energy was obtained from a single PV panel with TEG, compared to the standard PV panel. Our results suggests that combination of TEG with PV panels could significantly increase the electrical energy, especially when a series of PV panels are used together.Öğe Monitoring of electricity generation from exhaust waste heat and wireless data recording from a mobile phone in real driving conditions of a vehicle(Springer Heidelberg, 2023) Akal, Dincer; Umut, IlhanIn this study, a system is designed to generate electrical energy from the exhaust waste heat of vehicles using a thermoelectric generator. Electronic hardware that can communicate wirelessly, firmware, and mobile software specific to the system have been developed to control and monitor this system. The system comprises hexagonal aluminum components, thermoelectric generators, a cooler, sensors, software, and electronic hardware. The easily removable hexagonal modular aluminum component is designed to transmit heat from the exhaust pipe to thermoelectric generators. It used a thermoelectric generator (TEG-SP1848) on each edge of this hexagonal component and a heatsink to cool the generator. The voltage and current values of the electrical energy produced in the observations made under real driving conditions are recorded on the SD card on the system. In addition, system-specific mobile software has been developed by the work team. With this software, the system can be controlled, as well as visualizing the instantaneous parameters of the system. According to the results obtained from the test drives, electrical energy was obtained at a maximum voltage of 9.8 V and a current of 0.32 A. This electrical energy from the exhaust waste heat can be stored in the vehicle's existing battery. In this way, since the alternator used for the vehicle's electricity generation will be activated less, fuel savings will be achieved in the engine, and harmful exhaust emissions will be reduced. In addition, the electrical energy obtained by this method can be stored in an external battery independent of the vehicle battery and used for various purposes. In contrast, the vehicle is stationary or has a portable battery.Öğe A novel thermoelectric CPU cooling system controlled by artificial intelligence(Gazi Univ, Fac Engineering Architecture, 2024) Umut, Ilhan; Akal, DincerFigure A shows the components of the additional TEC unit designed in addition to the CPU cooling fan. In order to realize the heat transfer by conduction between the TEC unit and the CPU, the aluminum plate (TEC Module Interface Connection), seen in (Figure A), is designed. On the plate used in this TEC unit, there are thermoelectric module (TEC-12706), heatsink and fan. Since the temperature of the thermoelectric cooler will always be lower than the CPU temperature, effective cooling will be ensured.Purpose: Temperature rise in computers is an undesirable situation that occurs depending on the processor load. Due to excessive temperature rise in the Central Processing Unit (CPU), computers shut down and system damage occurs over time. In this study, a new thermoelectric cooling system is designed to reduce the temperature in the CPU. In addition, 3 different artificial intelligence models were created for the dynamic control of the system and their successes were compared.Theory and Methods: The new cooling system is designed using a thermoelectric module. It is to remove the excess heat by conduction and convection by taking advantage of the temperature difference between the thermoelectric cooler and the CPU we add to the system. Since the temperature of the thermoelectric cooler will always be lower than the CPU temperature, effective cooling will be provided. A special electronic circuit and software have been developed for the control of the cooling unit. Three different artificial intelligence models (artificial neural network, random forest, and k-nearest neighbor) were created to dynamically control the additional cooling system and their successes were compared. Artificial intelligence determines the power and fan speed of the thermoelectric cooling system. It performs this control by evaluating all parameters (different values such as CPU frequency, voltage, number of processes) instead of a specific CPU load or a specific temperature value.Results: While the CPU temperature was 41 & DEG;C at maximum load, this temperature was reduced to 310C thanks to the designed thermoelectric cooling system. All methods provided a high classification success in training. However, the classification success of the artificial neural network method (97.973%) is higher than the random forest (97.297%) and the k-nearest neighbor (96.306%). Conclusion: In the standard CPU fan, the CPU temperature at maximum load was 41 & DEG;C and the maximum energy consumed by the fan for cooling was 8 Watts. Thanks to the developed thermoelectric cooler system, the CPU temperature was reduced to 31 & DEG;C and the energy difference for this process was maximum 12 Watts, at maximum load.Öğe Performance assessment of a phase change charging mode in a vertical thermal energy storage system(Wiley, 2022) Asker, Mustafa; Akal, Dincer; Ezan, Mehmet AkifIn this work, the performance of a charging mode of a thermal energy storage system is investigated numerically, and results are assessed considering the first law and second law perspectives. The storage unit comprises parallel plates positioned vertically, and the heat transfer fluid flows within the spacing between the plates. The melting process of phase change material inside a rectangular enclosure is simulated considering the natural convection using an in-house model codded in C++. The proposed model is validated with the numerical and experimental research from the literature. A parametric analysis is carried out to explore the influence of heat transfer inlet temperature and aspect ratio on the unit's first law and second law performance indicators. Analyses are also conducted by disregarding the natural convection to assess the convective mode of heat transfer on the time-wise variation of the storage effectiveness and stored exergy. The results revealed that for the highest inlet temperature of the heat transfer fluid, the stored energy value increases from 133.85 to 250 kJ then drops to 240 kJ by varying the aspect ratio from 0.25 to 0.50 and from 0.50 to 1.00, respectively, for the natural convection dominated melting. On the other hand, regarding effectiveness, both with and without natural convection modes show the same aspect ratio variations trend. The effectiveness reduces from 0.9 to 0.40 by increasing the aspect ratio from 0.25 to 1.00 for the natural convection-dominated melting mode. The effectiveness drops from 0.62 to 0.16 for the same variation in the aspect ratio for the conduction-dominated melting mode. Besides, it is found that the highest stored exergy is observed in Case 9 w/NC situation with a stored exergy value of 13.3 kJ. The exergy efficiency changes approximately between 65% and 81% for all cases.Öğe A review of hydrogen usage in internal combustion engines (gasoline-Lpg-diesel) from combustion performance aspect(Pergamon-Elsevier Science Ltd, 2020) Akal, Dincer; Oztuna, Semiha; Buyukakin, Mustafa KemalettinDemand for fossil fuels is increasing day by day with the increase in industrialization and energy demand in the world. For this reason, many countries are looking for alternative energy sources against this increasing energy demand. Hydrogen is an alternative fuel with high efficiency and superior properties. The development of hydrogen-powered vehicles in the transport sector is expected to reduce fuel consumption and air pollution from exhaust emissions. In this study, the use of hydrogen as a fuel in vehicles and the current experimental studies in the literature are examined and the results of using hydrogen as an additional fuel are investigated. The effects of hydrogen usage on engine performance and exhaust emissions as an additional fuel to internal combustion gasoline, diesel and LPG engines are explained. Depending on the amount of hydrogen added to the fuel system, the engine power and torque are increased at most on petrol engines, while they are decreased on LPG and diesel engines. In terms of chemical products, the emissions of harmful exhaust gases in gasoline and LPG engines are reduced, while some diesel engines increase nitrogen oxide levels. In addition, it is understood that there will be a positive effect on the environment, due to hydrogen usage in all engine types. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Simulation of Drying Behavior of Cotton Bobbins by a Simultaneous Heat and Mass Transfer Model(Trans Tech Publications Ltd, 2011) Akyol, Ugur; Kahveci, Kamil; Cihan, Ahmet; Akal, DincerIn this study, the drying process of cotton bobbins for different drying air temperatures has been simulated by a simultaneous heat and mass transfer model. In the model, the mass transfer is assumed to be controlled by diffusion. In order to make the simulation, firstly, drying behavior of cotton bobbins for different drying air temperatures has been determined on an experimental bobbin dryer setup which was designed and manufactured based on hot-air bobbin dryers used in textile industry. In the experimental setup, temperatures of different points in cotton bobbins were measured by thermocouples placed inside the bobbins, and weights of the bobbins during the drying period were determined by means of a load cell. Then, moisture ratio and temperature values of the model have been fitted to the experimental ones. The fit was performed by selecting the values for the diffusion coefficient and the thermal diffusivity in the model in such a way that these values make the sum of the squared differences between the experimental and the model results for moisture ratio and temperature minimum. Results show that there is a good agreement between the model results and the experimental measurements. The results also show that temperature has a significant effect on mass transfer and the temperature dependence of the diffusion coefficient may be expressed by an Arrhenius type relation.Öğe Using Artificial Intelligence Methods for Power Estimation in Photovoltaic Panels(Univ Namik Kemal, 2022) Akal, Dincer; Umut, IlhanThe limited reserves of fossil resources, the fluctuations in their prices and the damage they cause to the environment have led countries to seek alternatives to primary energy resources. Solar energy, which is an unlimited and environmentally friendly resource, is a powerful alternative to other energy sources. The majority of the European Union countries offer various opportunities to consumers in electricity generation from solar energy with many incentive mechanisms and ensure their widespread use. In many parts of the world, interest in renewable energy sources such as solar, wind, hydrogen and geothermal is also growing. In addition to all these, researches are continuing to use alternative energy sources and to make energy production more efficient. The radiation value required to obtain electricity from solar energy varies according to the weather conditions during the day and seasonal characteristics. The climatic conditions in the area where solar power plants are installed directly affect the output power and energy cost to be obtained from photovoltaic panels. Estimating the output power produced from photovoltaic panels according to environmental conditions, guiding companies in the installation of solar energy systems, obtaining maximum energy, energy management and efficient operation of the system are of great importance. In this study, feedforward back propagation artificial neural networks and KNN (K-Nearest Neighbors) methods were used to estimate power values using the data (Temperature, Humidity, Pressure, Radiation) obtained from the installed photovoltaic panels. Thus, the panel values obtained under real field conditions were trained with both methods and the results were compared. As a result, the power values of the panel were classified using the artificial neural network model developed with the highest accuracy of 98.7945%. It has been seen that the machine learning models used for solar energy estimation developed within the scope of this study have high performance and can produce results very close to the real values. In addition, it was concluded that both artificial intelligence models developed in locations with different characteristics according to the determined load demand can be used.
