Artificial-neural-networks-based correlating heat transfer and friction of three kinds of heat exchangers having large tube-diameter and large tube-row

This work uses an artificial neural network (ANN) to correlate experimentally determined Nusselt numbers and friction factors for three kinds of fin-and-tube heat exchangers having plain fin, slit fin and fin with longitudinal winglet vortex generators with large tube-diameter and large tube-row. First, the relatively limited experimental data was picked up from the database of nine samples with Reynolds number being between 4,000 and 10,000. The Back Propagation (BP) algorithm was used to train the networks. Compared with correlations for prediction using conventional power-law regressions, the performance of developed ANN based prediction exhibits ANN superiority. Different network configurations were assessed to find the best architecture for correlating heat transfer and flow friction. The deviation between the predictions and experimental data was less than 4%. Then the ANN training database was expanded to include the experimental data and numerical data of some similar geometries by Computational Fluid Dynamics (CFD), which in turn indicated that the predictions agree well with the combined database. The satisfactory results suggest that the ANN model is generalized to correlate the heat transfer and fluid flow of such three kinds of heat exchangers with large tube-diameter and large tube-row. It is recommended that ANNs might be used to predict the performance of thermal systems in engineering applications.