Prediction of convection heat transfer in converging–diverging tube for laminar air flowing using back-propagation neural network

The ability of an artificial neural network (ANN) model for heat transfer analysis in a converging–diverging tube is studied. Back propagation learning algorithm, the most common method for ANNs, was used in training and testing/validation the network. It is trained with selected values of the Reynolds numbers (Re), Prandtl numbers (Pr), half taper angle (θ), aspect ratio (Lcyc/Dmax), and Nusselt number (Nu). The trained network is the used to make predictions of the Nusselt numbers. The accuracy between selected data and ANNs results was achieved with a mean absolute relative error less than 1.5%. This shows that well trained neural network model provided fast, accurate and consistent results.