Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH)2–EG using experimental data. (October 2015)

• Record Type: Journal Article
• Authors: Hemmat Esfe, Mohammad; Afrand, Masoud; Wongwises, Somchai; Naderi, Ali; Asadi, Amin; Rostami, Sara; Akbari, Mohammad
• Is Part Of: International communications in heat and mass transfer. Volume 67(2015:Oct.)
• Page Start: 46
• Page End: 50
• Publication Date: 2015-10
• Subjects: Nanofluids -- Artificial neural network -- Thermal conductivity; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2015.06.015
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: This paper presents an investigation on the thermal conductivity of nanofluids using experimental data, neural networks, and correlation for modeling thermal conductivity. The thermal conductivity of Mg(OH)2 nanoparticles with mean diameter of 10 nm dispersed in ethylene glycol was determined by using a KD2-pro thermal analyzer. Based on the experimental data at different solid volume fractions and temperatures, an experimental correlation is proposed in terms of volume fraction and temperature. Then, the model of relative thermal conductivity as a function of volume fraction and temperature was developed via neural network based on the measured data. A network with two hidden layers and 5 neurons in each layer has the lowest error and highest fitting coefficient. By comparing the performance of the neural network model and the correlation derived from empirical data, it was revealed that the neural network can more accurately predict the Mg(OH)2 –EG nanofluids' thermal conductivity.