Study on heat transfer performance of steam-cooled ribbed channel using neural networks and genetic algorithms

This work aims to optimize the steam-cooled ribbed channels to achieve the best heat transfer performance. The combined effects of channel aspect ratio (W/H m 0.25-4), rib angle (alpha - 30-90 degrees) and Reynolds number (Re -10,000-100,000) on the heat transfer characteristics of steam-cooled ribbed channels were analyzed. The semi-empirical heat transfer correlation related to W/H, alpha and Re was developed. The back propagation neural network (BPNN) combined with genetic algorithm (GA) was used to predict the heat transfer coefficients and optimize the structural parameters of steam-cooled ribbed channels based on 90 groups experimental data, and an excellent BPNN model with a maximum prediction error of 1.9% was obtained. Flow fields in the steam-cooled ribbed channels were numerically calculated to explore the heat transfer enhancement mechanism of optimized channels. The results show that the average heat transfer coefficients of steam-cooled ribbed channels increase at first and then decrease with the increase of W/H and alpha. The optimized neural network has better prediction accuracy than that of the fitted empirical correlation. Reynolds number has a great influence on the optimal aspect ratio and rib angle of the steam-cooled ribbed channel. The optimal W/H and the optimal a increase from 2.23 to 3.35 and 41.12 degrees to 60.89 degrees, respectively, with the increase of Re. Large alpha within the range of 41.12-60.89 degrees should be selected for cooling channels with relatively larger W/H in the range of 0.25-4. The enhancement of longitudinal secondary flows and the suppression of main secondary flows result in the heat transfer enhancement of the optimized channels. (C) 2018 Elsevier Ltd. All rights reserved.