Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/822
Title: Artificial neural networks for predicting the local concentration ratio of parabolic trough collectors
Authors: Kalogirou, Soteris A. 
Keywords: Neural networks
LCR
Issue Date: 1996
Source: Proceedings of the EuroSun'96 Conference, Freiburg, Germany, Vol. 1, pp. 470-475.
Abstract: The concentrated radiant flux on receiver surfaces of parabolic trough collectors is not uniform but exhibits a “bell” type shape as presented in this paper. This flux is given in terms of the local concentration ratio (LCR) being in effect the ratio of the incoming to the concentrated value of solar radiation at the periphery of the receiver. Knowledge of the LCR is required not only at normal conditions but also at various incident angles. This is useful to a designer who wants to calculate the intercept factor and the optical efficiency of the concentrator at those angles. In the work presented here actual values of LCRs, at ten degree intervals around the receiver, for two different incident angles (15° and 60°) are used to train an artificial neural network. Subsequently the network is used to predict the LCR values at other incident angles.The matching of the data for the 15° and 60° is very good with an R2- value equal to 0.9997. For the unknown data at incident angles of 0°, 30° and 45° the R2-values are 0.9966, 0,9867 and 0.9765 respectively which indicates that the estimation is performed with adequate accuracy. By using the predicted LCR values, the intercept factor is estimated with a maximum deviation of 3.2% from the value estimated with the actual LCR values which is very adequate.
URI: http://ktisis.cut.ac.cy/handle/10488/822
Rights: EuroSun
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Files in This Item:
File Description SizeFormat 
C13-EUROSUN.pdf64.15 kBAdobe PDFView/Open
Show full item record

Page view(s) 50

12
checked on Apr 29, 2017

Download(s) 50

2
checked on Apr 29, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.