Affiliation:
1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Algeria
2. Nuclear Research Center of Birine, Algeria
Abstract
The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. Orientation,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron neural network, whereas the analogous heat transfer coefficient was selected to be its output. Via the method of trial-and-error, multiple layer perceptron network with 30 neurons in the hidden layer was attained as optimal arteficial neural network structure. The fact that is was enabled to predict accurately the heat transfer coefficient. For the training set, the mean relative absolute error is about 0.68 % and the correlation coefficient, is about 0.9997. As for the testing and validation set they are, respectively, about 0.60 % and 0.9998 and about 0.79 % and 0.9996.
Publisher
National Library of Serbia
Subject
Renewable Energy, Sustainability and the Environment
Cited by
3 articles.
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