Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation. (November 2015)

• Record Type: Journal Article
• Authors: Hemmat Esfe, Mohammad; Rostamian, Hadi; Afrand, Masoud; Karimipour, Arash; Hassani, Mohsen
• Is Part Of: International communications in heat and mass transfer. Volume 68(2015:Nov.)
• Page Start: 98
• Page End: 103
• Publication Date: 2015-11
• Subjects: Thermal conductivity -- Experimental data -- Correlation -- Artificial neural network -- Nanofluid -- Solid volume fraction; 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.2015.08.015
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: In this article, artificial neural network (ANN) model has been used to study the thermal conductivity of MgO–water/EG (60:40) nanofluids based on experimental data. MgO nanoparticles in a binary mixture of water/EG (60:40) were scattered to make the above-mentioned nanofluid in two stages. The properties of the nanofluid were measured in different concentrations (0.1, 0.2, 0.5, 0.75, 1, 2, and 3%) and temperatures of 20 to 50 °C. Afterwards, two correlations were suggested for predicting the thermal conductivity of the nanofluids. The results of this study show that the ANN model can predict thermal conductivity to a great degree and is in agreement with the experimental results.