Predicting the thermal performance of double pipe heat exchanger using the generalized regression neural network model

Abstract

A heat transfer device in which heat exchange takes place between two fluids at different temperatures. The performance of any heat exchanger is evaluated by its heat transfer rate. Such heat transfer rate directly depends on the difference in temperature between two fluids and the mass flow rate. In this study, an attempt has been made to predict the rate of heat transfer between two fluids and the exit temperature using the generalized regression neural network (GRNN) model. For this, two GRNN models are designed using 15 real-time experimental datasets among which the GRNN-1 model is effectively designed to predict the exit temperature of both the hot and the cold fluids flowing through the heat exchanger with an accuracy of 97.48%. Similarly, the GRNN-2 model is designed to predict the rate of heat transfer between the fluids with an accuracy of 94.04%.