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Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid

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An Erratum to this article was published on 16 April 2015

Abstract

In this study, experiments were performed by six different volume fractions of Al2O3 nanoparticles in distilled water. Then, actual nanofluid Nusslet number compared by Adaptive neuro fuzzy inference system (ANFIS) predicted number in square cross-section duct in laminar flow under uniform heat flux condition. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 0.99 for all cases.

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Abbreviations

ARE:

Average relative error

AARE:

Absolute average relative error

MSE:

Mean square error

RMSE:

Root Mean square error

ANFIS:

Adaptive neuro fuzzy inference system

FIS:

Fuzzy inference system

CHF:

Critical heat flux

X, Y, and Z:

Linguistic variables

ai, bi, and ci:

Parameter set

μA(x), and μB(x):

Membership function

A:

Surface area of the square cross-section duct (m2)

Cp:

Specific heat (kJ kg−1 K−1)

Dh :

Hydraulic diameter (m)

\( \overline{\text{h}}_{\text{nf}} (\exp ) \) :

Experimental average heat transfer coefficient of Nanofluid (W m−2 K−1)

K:

Thermal conductivity (W m−1 K−1)

L:

Duct length (m)

Nu (exp):

Experimental average Nusselt number of Nanofluid

Nu (th):

Nanofluid Nusselt number calculated from Seider–Tate equation

Pe:

Peclet number

Pr:

Prandtl number

Q:

Heat flux (W)

Re:

Reynolds number

Tb :

Bulk temperature (K)

Tw :

Duct wall temperature (K)

\( \overline{\text{U}} \) :

Average fluid velocity (m s−1)

μ:

Viscosity (Pa s)

μwnf :

Nanofluid viscosity at duct wall temperature (Pa s)

\( \phi \) :

Nanoparticle volume fraction (%)

ρ:

Density (kg m−3)

nf:

Nanofluid

s:

Solid nanoparticles

w:

Water

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, Heat Pipe and Nanofluid Research Center, Ferdowsi University of Mashhad, Mashhad, Iran

    H. Salehi & S. Zeinali-Heris

  2. Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    M. Esfandyari

  3. Petroleum Department, National Iranian South Oil Field Company, Ahwaz, Iran

    M. Koolivand

Authors
  1. H. Salehi
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  2. S. Zeinali-Heris
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  3. M. Esfandyari
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  4. M. Koolivand
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Corresponding author

Correspondence to H. Salehi.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s00231-015-1566-7.

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Salehi, H., Zeinali-Heris, S., Esfandyari, M. et al. Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid. Heat Mass Transfer 49, 575–583 (2013). https://doi.org/10.1007/s00231-012-1104-9

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