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Öğe Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network(Pergamon-Elsevier Science Ltd, 2007) Sözen, Adnan; Özalp, Mehmet; Arcaklioğlu, ErolThis study proposes a alternative approach based on artificial neural networks (ANNs) to determine the thermodynamic properties - specific volume, enthalpy and entropy - of an alternative refrigerant (R508b) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. In the ANN, the back-propagation learning algorithm with two different variants, namely scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM), and Logistic Sigmoid transfer function were used to determine the best approach. The most suitable algorithm and with appropriate number of neurons (i.e. 7) in the hidden layer is found to be the LM algorithm which has provided the minimum error. For wet vapor region, R-2 values - which are errors known as absolute fraction of variance - are 0.983495, 0.969027, 0.999984, 0.999963, 0.999981, and 0.999975, for specific volume, enthalpy and entropy for training and testing, respectively. Similarly, for superheated vapor, they are: 0.995346, 0.996947, 0.999996, 0.999997, 0.999974, and 0.999975, for training and testing, respectively. According to the regression analysis results, R-2 values are 0.9312, 0.9708, 0.9428, 0.9343, 0.967 and 0.9546 for specific volume, enthalpy and entropy for wet vapor region and superheated vapor, respectively. The comparisons of the results suggest that, ANN provided results comfortably within the acceptable range. This study, deals with the potential application of the ANNs to represent PVTx (pressure-specific volume-temperature-vapor quality) data. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol-gasoline blend fuel(Pergamon-Elsevier Science Ltd, 2007) Serdar Yücesu, H.; Sözen, Adnan; Topgul, Tolga; Arcaklioğlu, ErolThis study consists of two cases: (i) The experimental analysis: Ethanol obtained from biomass can be used as a fuel in spark ignition engines. As renewable energy source ethanol, due to the high octane number, low emissions and high engine performance is preferred alternative fuel. First stage of this study, ethanol-unleaded gasoline blends (E10, E20, E40 and E60) were tested in a single cylinder, four-stroke spark ignition and fuel injection engine. The tests were performed by varying the ignition timing, relative air-fuel ratio (RAFR) and compression ratio at a constant speed of 2000 rpm and at wide open throttle (WOT). Effect of ethanol-unleaded gasoline blends and tests variables on engine torque and specific fuel consumption were examined experimentally. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the engine torque and specific fuel consumption based on the ignition timing, RAFR and compression ratio at a constant speed of 2000 rpm and at WOT for different fuel densities using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained Levenberg-Marquardt (LM) algorithm with five neurons in the hidden layer, which made it possible to the engine torque and specific fuel consumption with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the RZ values are 0.999996 and 0.999991 for, the engine torque and specific fuel consumption, respectively. Similarly, these values for testing data are 0.999977 and 0.999915, respectively. As seen from the results of mathematical modeling, the calculated engine torque and specific fuel consumption are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach(Pergamon-Elsevier Science Ltd, 2007) Sözen, Adnan; Arcaklioğlu, ErolThis paper proposes artificial neural networks (ANNs) technique as a new approach to determine the exergy losses of an ejector-absorption heat transformer (EAHT). Thermodynamic analysis of the EAHT is too complex due to complex differential equations and complex simulations programs. ANN technique facilitates these complicated situations. This study is considered to be helpful in predicting the exergetic performance of components of an EAHT prior to its setting up in a thermal system where the working temperatures are known. The best approach was investigated using different algorithms with developed software. The best statistical coefficient of multiple determinations (R-2-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively. Similarly these values for testing data are 0.999774, 0.994039, 0.999613 and 0.99938, respectively. The results show that this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround, which is especially important during iterative design phases, and easy of design by operators with little technical experience. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Modelling of Turkey's net energy consumption using artificial neural network(2005) Sözen, Adnan; Arcaklioğlu, Erol; Özkaymak, MehmetThe main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models were used in order to train the neural network: (i) Population, gross generation, installed capacity and years are used in input layer of network (Model 1). (ii) Energy sources are used in input layer of network (Model 2). The NEC is in output layer for two models. R2 values for training data are equal to 0.99944 and 0.99913, for Model 1 and Model 2, respectively. Similarly, R2 values for testing data are equal to 0.997386 and 0.999558 for Model 1 and Model 2, respectively. According to the results, the NEC prediction using ANN technique will be helpful in developing highly applicable and productive planning for energy policies. Copyright © 2005 Inderscience Enterprises Ltd.Öğe Performance maps of a diesel engine(Elsevier Sci Ltd, 2005) Çelik, Veli; Arcaklioğlu, ErolThis paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R-2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data. (c) 2004 Elsevier Ltd. All rights reserved.Öğe Performance parameters of an ejector-absorption heat transformer(Elsevier Sci Ltd, 2005) Sözen, Adnan; Arcaklioğlu, Erol; Özalp, Mehmet; Yücesu, SerdarEjector-absorption heat transformers (EAHTs) are attractive for increasing a solar-pond's temperature and for recovering low-level waste-heat. Thermodynamic analysis of the performance of an EAHT is complicated due to the associated complex differential equations and simulation programs. This paper proposes the use of artificial neural-networks (ANNs) as a new approach to determine the performance parameters, as functions of only the working temperatures of the EAHT, which is used to increase the solar pond's temperature under various working conditions. Thus, this study is helpful in predicting the performance of an EAHT where the temperatures are known. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was investigated for performance parameters with developed software using various algorithms. The best statistical coefficients of multiple determinations (R-2-values) equal 0.99995, 0.99997 and 0.99995 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively obtained by the LM algorithm with seven neurons. In the comparison of performances, results obtained via analytic equations and by means of the ANN, the COP, ECOP and F for all working situations differ by less than 1.05%, 0.7% and 3.07%, respectively. These accuracies are acceptable in the design of the EAHT. The ANN approach greatly reduces the time required by design engineers to find the optimum solution. Apart from reducing the time required, it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users. Also, this approach has the advantages of high computational speed, low cost for feasibility, rapid turn-around, which is especially important during iterative design phases, and ease of design by operators with little technical experience. (C) 2004 Elsevier Ltd. All rights reserved.
