Heat Transfer Analysis of Different Coolant in the Waist Tubes of a Radiator and Performance Prediction Based on Artificial Neural Network

Abstract

In this study, water, 0.05 Al2O3/water and 0.05 CuO/water nanofluids as a coolant in the waist tubes of a radiator are investigated numerically to evaluate their thermal and flow performance. Results are presented in terms of temperature distribution, heat transfer coefficient for different states. The results indicate that coolant has a significant impact on the heat transfer performance of the radiator, nanofluids increase the heat transfer coefficient. In addition, artificial neural network (ANN) was proposed for temperature difference between inlet and outlet of coolant prediction, ANN shows an extremely high prediction accuracy. The present study can help to understand the heat and flow behaviour of nanofluids in the waist tube.