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Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network

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Abstract

Heat transfer of Cu–water nanofluid over a stretching cylinder in the presence of magnetic field has been investigated. The group method of data handling (GMDH) type neural networks (NNs) is used to calculate Nusselt number formulation. Results indicate that GMDH-type NN in comparison with fourth-order Runge–Kutta integration scheme provides an effective means of efficiently recognizing the patterns in data and accurately predicting a performance. The effects of nanoparticle volume fraction, magnetic parameter and Reynolds number on Nusselt number are studied by sensitivity analyses. The results show that Nusselt number is an increasing function of Reynolds number and volume fraction of nanoparticles while it is a decreasing function of magnetic parameter. As volume fraction of nanoparticles increases, the effect of this parameter on Nusselt number also increases, but opposite behavior is obtained for magnetic parameter and Reynolds number.

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Abbreviations

A 1, A 2, A 3, A 4 :

Constant parameters

a :

Radius of cylinder

a i :

Coefficients of the quadratic polynomial equation

c :

Positive constant

C f :

Skin friction coefficient

f :

Dimensionless stream function

k :

Thermal conductivity

M :

Magnetic parameter

MS:

Mean square error

MAD:

Mean absolute deviation

Nu :

Nusselt number

Pr :

Prandtl number

q w :

Heat transfer from the cylinder surface

Re :

Reynolds number

R 2 :

Absolute fraction of variance

T :

Temperature of the nanofluid

u, v :

Velocity components along the x and y directions, respectively

x, y :

Cartesian coordinates along x and y axes, respectively

α :

Thermal diffusivity

η :

Similarity variable

θ :

Similarity function for temperature

ρ :

Density

ϕ :

Nanoparticle volume fraction

μ :

Dynamic viscosity

υ :

Kinematic viscosity

τ w :

Wall shear stress

ψ :

Stream function

σ :

Electrical conductivity

w:

Condition at the surface

∞ :

Far field

nf:

Nanofluid

f:

Base fluid

s:

Nano-solid-particles

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Authors and Affiliations

  1. Department of Mechanical Engineering, Babol University of Technology, Babol, Iran

    M. Sheikholeslami, D. D. Ganji & Houman B. Rokni

  2. Department of Chemical Engineering, Babol University of Technology, Babol, Iran

    F. Bani Sheykholeslami & S. Khoshhal

  3. Department of Civil Engineering, Babol University of Technology, Babol, Iran

    H. Mola-Abasia

  4. Mechanical and Materials Engineering, University of Denver, 2390 S York St., Denver, CO, 80208, USA

    Houman B. Rokni

Authors
  1. M. Sheikholeslami
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  2. F. Bani Sheykholeslami
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  3. S. Khoshhal
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  4. H. Mola-Abasia
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  5. D. D. Ganji
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  6. Houman B. Rokni
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Corresponding authors

Correspondence to M. Sheikholeslami or Houman B. Rokni.

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Sheikholeslami, M., Bani Sheykholeslami, F., Khoshhal, S. et al. Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network. Neural Comput & Applic 25, 171–178 (2014). https://doi.org/10.1007/s00521-013-1459-y

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  • DOI: https://doi.org/10.1007/s00521-013-1459-y

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