Thermal Performance Analysis of Heat Pipe Intercooler Based on Artificial Neural-Networks

Abstract

Since the internal heat transfer is a complicated process, the heat pipe heat exchanger of the engine has not been fully understood yet, which is originated from its extreme complexity. In theoretical studies, the involvement of two-phase flow and phase change processes usually simplifies the processing very much, and the model built differs too much from the actual one, resulting in reduced simulation accuracy. In this study, the prediction model of heat transfer and heat resistance of the heat pipe intercooler is established based on artificial neural networks (ANNs). Then the performance of the heat pipe intercooler from heat transfer and heat resistance aspects is investigated. The average relative error between the heat transfer prediction model and the test value is 3.6%, and the average relative error between the resistance prediction model and the test value is 12.68%, which shows that the prediction model can predict the thermal performance of heat pipe intercooler more accurately. Finally, the proposed model is applied to optimize the structural parameters of the heat pipe intercooler, and the optimal parameters are obtained accordingly. These optimal design parameters can provide the basis for further investigation and development of the heat pipe intercooler in diverse applications.