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

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Abstract

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.

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Authors and Affiliations

  1. Department of Chemistry, Yasouj University, Yasouj, 75914-353, Iran

    Fakhri Yousefi & Somayeh Mohammadiyan

  2. Department of Chemical Engineering, Yasouj University, Yasouj, 75914-353, Iran

    Hajir Karimi

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  1. Fakhri Yousefi
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Correspondence to Fakhri Yousefi.

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Yousefi, F., Karimi, H. & Mohammadiyan, S. Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis. Heat Mass Transfer 52, 2345–2355 (2016). https://doi.org/10.1007/s00231-015-1745-6

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  • DOI: https://doi.org/10.1007/s00231-015-1745-6

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