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    AN EXPERIMENTAL INVESTIGATION OF THE EFFECT OF REFRIGERANT CHARGE LEVEL ON AN AUTOMOTIVE AIR CONDITIONING SYSTEM
    (Turkish Soc Thermal Sciences Technology, 2011) Atik, Kemal; Aktas, Abdurrazzak
    During the last 25 years automotive air conditioning (AAC) systems have significant development introduced by the industry and research institutes in the world to minimize the global warming threat to the environment. This paper reports the results of a study on the performance of an AAC system with measuring the compressor driving speed and the refrigerant leakage. For this purpose an experimental set up is designed and constructed to investigate the system performance. Although, the manufacturer's recommended amount for the tests with R-134a as refrigerant was 750 g, the experiments were also carried out by selecting different amount of the same refrigerant charges to analyse the coefficient of performance (COP), the cooling capacity and the compressor power change with respect to the rotating speed of the compressor. The evaluation of experimental data revealed that the best cooling capacity was achieved at 500 g refrigerant charge. Although, while the charge level decreased 40% below or increased 20% above the 500g of the charge amount, cooling capacity loss increased up to 25% when optimum value of 500 g of the cooling refrigerant was utilized. The test results proved in each case that increasing the compressor driving speed cause almost a linear change in the corresponding power level. The test results also shown that COP of the cooling system was decreased effectively when the revolution speed increased for any specified charge amount of the refrigerant.
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    Investigation of using regression analysis and artificial neural network methods in estimate of solar radiation
    (Turkish Soc Thermal Sciences Technology, 2007) Deniz, Emrah; Atik, Kemal
    In this study, the more effective one of two methods, artificial neural network and regression analysis, was tried to be determined when they were used in estimation of the solar radiation intensity. For this purpose, wind velocity, air temperature, soil temperature, declination angle, humidity, the ratio of solar irradiation to daytime length, and monthly average of extraterrestrial solar radiation data between 1995 and 2004 belonging to Zonguldak city was obtained from Turkish State Meteorological Service (TSMS). Models were developed by regression analysis and artificial neural network (ANN) with the data obtained. Using these models, mountly average values of total solar irradiation between January/2005 and December/2005 were calculated and these calculated results were compared to measured results of the same period. It was determined that there are mean relative errors of 1.28 % and 3.25 % when the estimation was made by regression analysis and artificial neural network respectively.
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    Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network
    (Pergamon-Elsevier Science Ltd, 2007) Yilmaz, Sezayi; Atik, Kemal
    Capacity modification in mechanical cooling systems can be performed by various methods. Changing condenser temperature also changes the capacity of the cooling system. In this study, a series of experiments were performed in order to determine the effects of changing cooling water flow rate (changing condenser temperature) in a mechanical heat pump experimental setup on the cooling capacity of the system. Power consumption, thermal efficiency, coefficient of performance (COP) of the system in various cooling capacities were estimated theoretically by using the data acquired from the experiments performed. Performance values obtained were used for training Artificial neural network (ANN) whose structure was designed for this operation. The Network, which has three layers as input, output, and hidden layer, has one input and four output cells. Six cells were used in hidden layers. Training was continued until the square error became (E < 0.005) in this ANN, for which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area. (c) 2007 Elsevier Ltd. All rights reserved.
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    A NUMERICAL INVESTIGATION OF EFFECT OF MAGNETIC FIELD ON HEAT CONVECTION WITH VARIABLE PHYSICAL PROPERTIES
    (Pamukkale Univ, 2008) Recebli, Ziyaddin; Atik, Kemal; Sekmen, Perihan
    In some studies, the effect of magnetic field on heat convection has been investigated given that physical properties are constant regardless of temperature. The effect of magnetic field on heat convection and fluids whose physical properties change by temperature has been investigated in this study as physical properties of fluids change by the effect of temperature. Momentum, continuity and energy equations including electromagnetic force affecting the fluid were used in the solution. Temperatures at axial and radial directions and Nusselt numbers were calculated depending on magnetic field intensity and other physical properties of fluid by solving the equation system written in cylindrical coordinates system by means of one of the numerical methods which is finite difference method. According to results, velocity and temperature of the cooled fluid decreased following an increase in the intensity of magnetic field placed vertically to flow direction. As determined in the previous one, this study also indicated that the increase in Reynolds number increases Nusselt number, and increasing the effect of magnetic field decreases Nusselt number. The theoretical results of the present study are in conformity with the results of our previous one.
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    Performance parameters estimation of MAC by using artificial neural network
    (Pergamon-Elsevier Science Ltd, 2010) Atik, Kemal; Aktas, Abdurrazzak; Deniz, Emrah
    In this study, a modeling of a mobile air conditioner system in different amounts of refrigerant and in different compressor revolution speeds was carried out via artificial neural network (ANN). The three-layered ANN that is utilized in order to be able to model this system has 2 cells in its input layer and 3 cells in its output layer. The least problem-yielding ANN structure was analyzed by examining the number of cells in hidden layer from 6 to 19. The best result was obtained in the ANN with 10 hidden cells. A 0.945 was obtained in estimating coefficient of correlation cooling capacity, 0.985 was found in the power consumed in compressor, and 0.994 was established in COP estimation. The obtained correlation coefficients showed that ANNs can be used with a high precision in guessing the performance parameters of mobile air conditioner (MAC) systems. (C) 2010 Elsevier Ltd. All rights reserved.
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    Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network
    (Elsevier France-Editions Scientifiques Medicales Elsevier, 2008) Kurt, Hueseyin; Atik, Kemal; Ozkaymak, Mehmet; Recebli, Ziyaddin
    Work to date has shown that Artificial Neural Network (ANN) has not been used for predicting thermal performance parameters of a solar cooker. The objective of this study is to predict thermal performance parameters such as absorber plate, enclosure air and pot water temperatures of the experimentally investigated box type solar cooker by using the ANN. Data set is obtained from the box type solar cooker which was tested under various experimental conditions. A feed-forward neural network based on back propagation algorithm was developed to predict the thermal performance of solar cooker with and without reflector. Mathematical formulations derived from the ANN model are presented for each predicting temperatures. The experimental data set consists of 126 values. These were divided into two groups, of which the 96 values were used for training/learning of the network and the rest of the data (30 values) for testing/validation of the network performance. The performance of the ANN predictions was evaluated by comparing the prediction results with the experimental results. The results showed a good regression analysis with the correlation coefficients in the range of 0.9950-0.9987 and mean relative errors (MREs) in the range of 3.92516-7.040% for the test data set. The regression coefficients indicated that the ANN model can successfully be used for the prediction of the thermal performance parameters of a box type solar cooker with a high degree of accuracy. (c) 2007 Elsevier Masson SAS. All rights reserved.