Application of artificial neural network and PCA to predict the thermal conductivities of nanofluids

被引：23
|
作者：
Yousefi, Fakhri [1 ]
Mohammadiyan, Somayeh [1 ]
Karimi, Hajir [2 ]
机构：
[1] Univ Yasuj, Dept Chem, Yasuj 75914353, Iran
[2] Univ Yasuj, Dept Chem Engn, Yasuj 75914353, Iran
来源：
HEAT AND MASS TRANSFER | 2016年 / 52卷 / 10期
关键词：
EQUATION-OF-STATE; HEAT-TRANSFER; ETHYLENE-GLYCOL; VOLUMETRIC PROPERTIES; PARTICLE-SIZE; ENHANCEMENT; TRANSPORT; MIXTURE; TEMPERATURE; SUSPENSIONS;
D O I：
10.1007/s00231-015-1730-0
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
This paper applies a model including back-propagation network (BPN) and principal component analysis (PCA) to compute the effective thermal conductivities of nanofluids such as Al2O3/(60:40)EG:H2O, Al2O3/W, Al2O3/(20:80)EG:W, Al2O3/(50:50)EG:W, ZnO/(60:40) EG:W, CuO/(60:40)EG:W, CuO/W, CuO/(50:50)EG:W, TiO2/W, TiO2/(20:80)EG:W, Fe3O4/(20:80) EG:W, Fe3O4/(60:40) EG:W, Fe3O4/(40:60) EG:W and Fe3O4/W, as a function of the temperature, thermal conductivity of nano particle, volume fraction of nanoparticle, diameter of nanoparticle and the thermal conductivity of base fluids. The obtained results by BPN-PCA model have good agreement with the experimental data with absolute average deviation and high correlation coefficients 1.47 % and 0.9942, respectively.
引用
收藏
页码：2141 / 2154
页数：14
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