Mathematical and artificial brain structure-based modeling of heat conductivity of water based nanofluid enriched by double wall carbon nanotubes

被引：14
作者：
Hemmat Esfe, Mohammad [1 ]
Afrand, Masoud [2 ]
机构：
[1] Islamic Azad Univ, Khomeinishahr Branch, Young Researchers & Elite Club, Khomeinishahr, Iran
[2] Islamic Azad Univ, Dept Mech Engn, Najafabad Branch, Najafabad, Iran
来源：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2020年 / 540卷
关键词：
Artificial neural network; DWCNTs; Empirical correlation; Thermal conductivity; Nanofluid; MAGNETO-NATURAL CONVECTION; THERMAL-CONDUCTIVITY; RHEOLOGICAL BEHAVIOR; NEURAL-NETWORK; HYBRID NANOFLUID; ETHYLENE-GLYCOL; ENTROPY GENERATION; ZNO NANOPARTICLES; MIXED CONVECTION; VISCOSITY;
D O I：
10.1016/j.physa.2019.04.002
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Mathematical modeling based on response surface method and also an artificial brain structure-based model was applied to predict thermal properties of a water-based nanofluid earned by a set of laboratory results. Functionalized DWCNTs were used to enrich the conventional fluid (Water in the present study). Using response surface method a two-variable experimental based correlation proposed as a function of concentration and temperature. Also, artificial neural network employed as a brain structure-based approach with two inputs (temperature and concentrations) and thermal conductivity ratio as the desired output. Using mathematical and artificial based methods help to improve the economic efficiency of experimental studies. (C) 2019 Published by Elsevier B.V.
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页数：9
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