Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

作     者：WANG Xuehui YAN Xiaona GAO Neng CHEN Guangming WANG Xuehui;YAN Xiaona;GAO Neng;CHEN Guangming

作者机构：Fluids and Thermal Engineering Research GroupFaculty of EngineeringUniversity of NottinghamNG72RDUK School of Landscape ArchitectureZhejiang A&F UniversityHangzhou 311300China Ningbo Institute of TechnologyZhejiang UniversityNingbo 315100China 

出 版 物：《Journal of Thermal Science》 (热科学学报（英文版）)

年 卷 期：2020年第29卷第6期

页      面：1504-1512页

核心收录：

学科分类：12[管理学] 080701[工学-工程热物理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：financially sponsored by the National Natural Science Foundation of China(No.51706060) Innovate UK Project(ACeDrive No.113167) 

主　　题：thermal conductivity nanofluids ANN model heat transfer 

摘      要：The nanofluid has been widely used in many heat transfer areas due to its significant enhancement effect on the thermal ***,the methods that can accurately predict their thermal conductivities are very important to evaluate and analyze the heat transfer *** this paper,a novel artificial neural network(ANN)model was proposed to predict the thermal conductivity of nanofluids with ethylene glycol and could be used in a wide range with excellent accuracy.A total of 391 experimental data with a wide range of temperatures(4℃ to 90℃),nanoparticles(metal,metal oxide,etc.),volume concentrations(0.05%to 10%),and particle sizes(2 nm to 282 nm)were *** build the ANN model,the temperature,thermal conductivities of the base fluid and nanoparticles,the size and volume concentration of the nanoparticles were selected and used as the input *** were 5 nodes,10 nodes and 1 node in input layer,hidden layer and output layer,*** predicted results of the ANN model coincided with the experimental data very well with the correlation coefficient and mean square error(MSE)were 0.9863 and 3.01×10–5,*** relative deviations of 99.74%data were within±5%.The model was expected to be a good practical method to predict the thermal conductivity of nanofluids with ethylene glycol.