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 (HTC) 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
(MLP) neural network, whereas the analogous HTC was selected to be its
output. Via the method of trial-and-error, MLP network with 30 neurons in
the hidden layer was attained as optimal ANN structure. The fact that is was
enabled to predict accurately the HTC. For the training set, the mean
relative absolute error (MRAE) is about 0.68 % and the correlation
coefficient (R) 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 ANN model with experimental data and empirical
correlations in vertical channel under the low flow rate and low quality
shows a good agreement.
Modeling and estimation of thermal performance factor of MgO-water nanofluids flow by artificial neural network using experimental data
