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

被引：85

作者：

Hemmat Esfe, Mohammad ^{[1
]}

Rostamian, Hadi ^{[1
]}

Afrand, Masoud ^{[1
]}

Karimipour, Arash ^{[1
]}

Hassani, Mohsen ^{[1
]}

机构：

[1] Islamic Azad Univ, Najafabad Branch, Dept Mech Engn, Najafabad, Iran

来源：

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER | 2015年 / 68卷

关键词：

Thermal conductivity; Experimental data; Correlation; Artificial neural network; Nanofluid; Solid volume fraction; MIXED-CONVECTION FLOW; HEAT-TRANSFER; THERMOPHYSICAL PROPERTIES; DYNAMIC VISCOSITY; INCLINED CAVITY; ETHYLENE-GLYCOL; PRESSURE-DROP; PARTICLE-SIZE; NANOFLUID; TEMPERATURE;

D O I：

10.1016/j.icheatmasstransfer.2015.08.015

中图分类号：

O414.1 [热力学];

学科分类号：

070201 [理论物理];

摘要：

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 degrees 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. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：98 / 103

页数：6

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