Prediction of heat transfer coefficient of pool boiling using back propagation neural network

Abstract EN

Artificial neural network (ANN), in comparison with empirical correlations, has recently received more attention.

The present paper includes predictive modeling of heat transfer coefficient for binary mixtures in pool boiling for hydrocarbon compounds, using Back propagation techniques through Multilayer Perceptron, one of the types of the artificial neural networks.

To train and learn the system, predictive neural network was found, which is capable of understanding and predicting the preset output which is heat transfer coefficient.

The principle operation of such neural networks is based on the experimental data collected from some researchers [1-4].

A new ANN model is proposed using five inputs (mole fraction, temperature difference, heat flux, density and viscosity) to predict the heat transfer coefficient.

The prediction using ANN shows 0.0026 AARE (Absolute Average Relative Error) with most widely known correlations namely those of Calus, Fujita and Thome which have given 0.086, 0.066 and 0.038 respectively.