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

页码：2345 / 2355

页数：11

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