Yazar "Arcaklioglu, E." seçeneğine göre listele

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    A new approach to thermodynamic analysis of ejector-absorption cycle: artificial neural networks
    (Pergamon-Elsevier Science Ltd, 2003) Sözen, A.; Arcaklioglu, E.; Özalp, M.
    Thermodynamic analysis of absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations. To simplify this complex process, the use of artificial neural networks (ANNs) has been proposed for the analysis of ejector-absorption refrigeration systems (EARSs). ANNs approach was used to determine the properties of liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiBr), which does not cause ozone depletion for EARS. The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function was used in the network. In addition, this paper presents a. comparative performance study of the. EARS using both analytic functions and prediction of ANN for properties of the fluid couple. After training, it was found that average error is less than 1.3% and R-2 values are about 0.9999. Additionally, when the results of analytic equations obtained by using experimental data. and by means of ANN were compared, deviations in coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F) for all working temperatures were found to be less than 1.8%, 4%, 0.2%, respectively. Deviations for COP, ECOP and F at a generator temperature of similar to90 degreesC for which the COP of the system is maximum are 1%, 2%, 0.1%; respectively, for other working temperatures. As seen from the results obtained, the calculated thermodynamic properties-are obviously within acceptable uncertainties. (C) 2003 Elsevier Science Ltd. All rights reserved.
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    Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks
    (Pergamon-Elsevier Science Ltd, 2004) Sözen, A.; Arcaklioglu, E.; Özalp, M.
    Thermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (1) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption beat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three 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. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R-2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COPr), exergetic coefficient of performance of the system for cooling (ECOPr) and circulation ratio (F) for all working temperatures were found to be less than 1.7%, 5.1%, and 1.9%, respectively. Deviations for COPr, ECOPr and F at a generator temperature of similar to90 degreesC (cut off temperature) at which the coefficient of performance of the system is maximum are 0.9%, 1.8%, and 0.1%, respectively, for other working temperatures. When this system was used for heating, similar deviations were obtained. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties. The results showed that the use of ANNs for determination of thermodynamic properties is acceptable in design of the EAHP. (C) 2003 Elsevier Ltd. All rights reserved.
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    A study for estimating solar resources in Turkey using artificial neural networks
    (Taylor & Francis Inc, 2004) Sözen, A.; Özalp, M.; Arcaklioglu, E.; Kanit, E.G.
    Turkey has sufficient solar radiation and radiation period for solar thermal applications since it lies in a sunny belt between 36degrees and 42degreesN latitudes. The yearly average solar radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 h. This study investigates the estimation of solar resources in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. These cities selected can give a general idea about Turkey. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used in input layer of network. Solar radiation is in output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R-2 values to be about 99.8937% for the testing stations. However, these values were found to be 2.41% and 99.99658% for the training stations. The results indicate that the ANN model seems promising for evaluating solar resource possibilities at the places where there are no monitoring stations in Turkey. The results on the testing stations indicate a relatively good agreement between the observed and the predicted values.