Heat Transfer Prediction for Helical Baffle Heat Exchangers with Experimental Data by Radial Basis Function Neural Networks

Abstract

In this paper an artificial neural network (ANN) is used to correlate experimentally determined heat transfer rate of non-continuous helical baffle heat exchangers. First the heat exchangers with three helical angles were experimentally investigated under different inlet volumetric flow rate and temperature. The commonly implemented radial-basis function (RBF) neural network is applied to develop a prediction model based on the limited experimental data. Compared with correlations, the RBF network exhibits superiority in accuracy. The satisfactory results suggest the RBF network might be used to predict the thermal performance of shell-and-tube heat exchangers with helical baffles.