Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods

被引：109

作者：

Ahmadi, Mohammad Hossein ^{[1
]}

Nazari, Mohammad Alhuyi ^{[2
]}

Ghasempour, Roghayeh ^{[2
]}

Madah, Heydar ^{[3
]}

Shafii, Mohammad Behshad ^{[4
]}

Ahmadi, Mohammad Ali ^{[5
]}

机构：

[1] Shahrood Univ Technol, Fac Mech Engn, Shahrood, Iran

[2] Univ Tehran, Renewable Energy & Environm Engn Dept, Tehran, Iran

[3] Payame Noor Univ, Dept Chem Engn, POB 19395-3697, Tehran, Iran

[4] Sharif Univ Technol, Dept Mech Engn, Tehran, Iran

[5] Petr Univ Technol, Ahwaz Fac Petr Engn, Dept Petr Engn, Ahvaz, Iran

来源：

COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS | 2018年 / 541卷

关键词：

Thermal conductivity ratio; Nanofluid; Artificial neural network; LSSVM; Correlation coefficient; CONVECTIVE HEAT-TRANSFER; SUPPORT VECTOR MACHINES; NEURAL-NETWORK; PARTICLE-SIZE; RHEOLOGICAL PROPERTIES; DYNAMIC VISCOSITY; HYBRID NANOFLUIDS; TEMPERATURE; WATER; ENHANCEMENT;

D O I：

10.1016/j.colsurfa.2018.01.030

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070305 [高分子化学与物理];

摘要：

Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The correlation coefficient values are very favorable and equal to 0.88125 and 0.87575 and 0.89999 by applying SOM, LM-BP algorithms and LSSVM, respectively.

引用

页码：154 / 164

页数：11

共 84 条

[1]

Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data [J].