文献类型：期刊文献

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

作者：Liu, Yi Ma, Zongpeng Huang, Ying

第一作者：刘毅

通信作者：Huang, Y[1]

机构：[1]Guizhou Inst Technol, Sch Big Data, Key Lab Elect Power Big Data Guizhou Prov, Guiyang 55003, Peoples R China;[2]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China.

年份：2022

卷号：40

期号：1

起止页码：267-272

外文期刊名：INTERNATIONAL JOURNAL OF HEAT AND TECHNOLOGY

收录：EI(收录号：20221611984985);Scopus(收录号：2-s2.0-85128270694);WOS:【ESCI(收录号:WOS:000804373600031)】；

语种：英文

外文关键词：numerical; nanofluid; waist tube; heat transfer; artificial neural network (ANN)

摘要：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.