Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis

被引：15
|
作者：
Yousefi, Fakhri [1 ]
Karimi, Hajir [2 ]
Mohammadiyan, Somayeh [1 ]
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 / 11期
关键词：
EQUATION-OF-STATE; HEAT-TRANSFER ENHANCEMENT; THERMAL-CONDUCTIVITY; VOLUMETRIC PROPERTIES; BROWNIAN-MOTION; POLYMER MELTS; NANOFLUIDS; TEMPERATURE; PREDICTION; MIXTURE;
D O I：
10.1007/s00231-015-1745-6
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN-PCA model have good agreement with the experimental data.
引用
收藏
页码：2345 / 2355
页数：11
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