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

被引：0
|
作者：
Fakhri Yousefi
Hajir Karimi
Somayeh Mohammadiyan
机构：
[1] Yasouj University,Department of Chemistry
[2] Yasouj University,Department of Chemical Engineering
来源：
Heat and Mass Transfer | 2016年 / 52卷
关键词：
Principle Component Analysis; Artificial Neural Network; Hide Layer; Base Fluid; Effective Viscosity;
D O I：
暂无
中图分类号：
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
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页码：2345 / 2355
页数：10
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