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Öğe Analysis of the rejuvenation performance of hybrid blankets by using uranium fuels (UN, UC, UO2, U3Si2) and different coolants for various volume fraction(Pergamon-Elsevier Science Ltd, 2000) Yapici, H; Ipek, O; Ozceyhan, V; Erisen, AThe possibility of nuclear fuel rejuvenation in fusion reactors is investigated for different fuels and coolants. Neutronic performances of the deuterium-tritium (D-T) driven hybrid blankets, fuelled with UN, UC, UO2 and U3Si2, in four different cases, are investigated under first wall load of the 5 MW/m(2.) The fissile fuel zone considered to be cooled with four coolants: air, flibe (Li2BeF4), natural lithium and eutectic lithium (Li17Pb83) With volume fraction ratio of 29.5, 45.5 and 62.56%. The behaviour of the fuels mentioned above are observed during 48 months for discrete time intervals of Delta t = 15 days and by a plant factor (PF) of 75%. At the end of the operation time, calculations have shown that cumulative fissile fuel enrichment (CFFE) values have varied between 3.80 and 8.1% depending on the fuel, volume fraction and coolant type. The best enrichment performance is obtained in flibe (Li2BeF4) coolant blankets, followed by Eutectic lithium (Li17Pb83), air whereas natural lithium coolant shows a poor rejuvenation performance in all fuels. CFFE reach maximum value (8.1%) in UO2 fuelled blanket (in Row #1) and Li2BeF4 coolant that volume fraction is 62.5% after 48 months. The lowest CFFE value (3.80%) is in U3Si2 fuelled blanket (in Rows #6 and 7) and natural lithium coolant that volume fraction is 62.56% at the end of the operation period.The enrichment would be sufficient for LWR reactor. The best tritium breeding ratio (TBR) is obtained in U3Si2 fuelled blanket with natural lithium coolant, and followed by UC, UO2. UN with the same coolant. At the beginning of the operation, TBR values were 1.459, 1.502 and 1.554 in U3Si2 fuelled blanket with natural Lithium coolant 1.414, 1.474 and 1.547 in UC fuelled blanket with natural lithium coolant for volume fraction of 29.5, 45.5 and 62.56%, respectively. At the end of the operation, TBR reach 1.511. 1.559 and 1.613 in U3Si2 fuelled blanket and 1.467, 1.532 and 1.609 in UC fuelled blanket for volume fraction of 29.5, 45.5 and 62.56%, respectively. TBR values are higher than unity. Therefore, investigated hybrid blanket is self-sufficient for all fuel mixture and coolants. The isotopic percentage of Pu-240 is higher than 5% in all modes with flibe coolant, so that the plutonium component in these modes can never reach a nuclear weapon grade quality during the operation period. This is a very important safety factor. The isotopic percentage of Pu-240 is lower than 5% in all blanket with air, natural lithium, and eutectic lithium coolant. In these modes, operation period must be increased for safety. (C) 2000 Elsevier Science Ltd. All rights reserved.Öğe Artificial neural network analysis of heat pumps using refrigerant mixtures(Pergamon-Elsevier Science Ltd, 2004) Arcaklioglu, E; Erisen, A; Yilmaz, RIn this study, we have investigated the performance of a vapor compression heat pump with different ratios of R12/R22 refrigerant mixtures using artificial neural networks (ANN). Experimental studies were completed to obtain training and test data. Mixing ratio, evaporator inlet temperature and condenser pressure were used as input layer, while the outputs are coefficient of performance (COP) and rational efficiency (RE). The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. It is shown that the R-2 values are about 0.9999 and the RMS errors are smaller than 0.006. With these results, we believe that the ANN can be used for prediction of COP and RE as an accurate method in a heat pump. (C) 2003 Elsevier Ltd. All rights reserved.Öğe Performance prediction of a vapour-compression heat-pump(Elsevier Sci Ltd, 2004) Sozen, A; Arcaklioglu, E; Erisen, A; Akcayol, MAThe performance of the heat pump was predicted using a fuzzy-logic controller under various working-conditions and mixing ratios of R12/R22 refrigerant mixtures, instead of requiring an expensive and time consuming experimental study [Int. J. Ref. 13 (1990) 163]. Fuzzy-logic's linguistic terms provide a feasible method for defining the performance of the heat pump. Input data for the fuzzy logic are mixing ratio, evaporator-inlet temperature and condenser pressure. In the comparison of performance, results obtained via analytic equations and by means of the fuzzy-logic controller, the coefficient of performance (COP), and rational efficiency (RE) for all working situations differ by less than 1.5% and 1%, respectively. The statistical coefficient of multiple determinations (R-2-value) equals to 0.9988 for both the COP and the RE. With these results, we believe that fuzzy logic can be used for the accurate prediction of the COP and the RE of the heat pump. (C) 2004 Elsevier Ltd. All rights reserved.Öğe Thermodynamic analyses of refrigerant mixtures using artificial neural networks(Elsevier Sci Ltd, 2004) Arcaklioglu, E; Cavusoglu, A; Erisen, AThe aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated,as above, were the outputs. The network has yielded R-2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively. (C) 2003 Elsevier Ltd. All rights reserved.
