USING ARTIFICIAL NEURAL NETWORK FOR PREDICTING HEAT TRANSFER COEFFICIENT DURING FLOW BOILING IN AN INCLINED CHANNEL

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. The comparison of the developed arteficial neural network model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.

Automated summary

The study used boiling water data in an inclined channel to develop an artificial neural network model, achieving high accuracy in predicting heat transfer coefficient. The comparison with experimental data and empirical correlations showed good agreement.