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

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• 作者：Mohammad Hemmat Esfe ; ^{M.hemmatesfe@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Hadi Rostamian ; Masoud Afrand ; Arash Karimipour ; ^{arashkarimipour@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Mohsen Hassani
• 关键词：Thermal conductivity ; Experimental data ; Correlation ; Artificial neural network ; Nanofluid ; Solid volume fraction
• 刊名：International Communications in Heat and Mass Transfer
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：68
• 期：Complete
• 页码：98-103
• 全文大小：662 K

文摘

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.