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Two-phase nanofluid condensation and heat transfer modeling using least square method (LSM) for industrial applications

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Abstract

In this paper, two-phase Nanofluid condensation and heat transfer analysis over a vertical plate under gravity and between two parallel plates under magnetic force are investigated respectively using Least Square Method (LSM) and numerical method. After presenting the governing equations and solving them by LSM, the accuracy of results is examined by fourth order Runge–Kutta numerical method. Modeling results show that the condensate film thickness after condensation is reduced and therefore, the rate of heat transfer is enhanced by the addition of nanoparticles to the regular fluid. Effect of different nanoparticles and constant numbers on the temperature/velocity/concentration profiles as well as Nusselt number and boundary layer thickness, are also investigated. For instance, it was found that TiO2 and Ag have maximum boundary layer thicknesses and Nusselt number, respectively. By considering the magnetic field effect, it is also found that nanoparticles concentration can be controlled by changing the Hartmann number which, in turn, leads to different condensation and heat transfer properties.

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Abbreviations

\(A\) :

Ratio of thermophoretic effects to Brownian diffusion effects of nanofluid

\(C\) :

Concentration of nanofluid

\(C_{p}\) :

Specific heat

\(D_{T} ,\,D_{B}\) :

Parameters in the Eqs. (8–10)

\(F\left( \varphi \right)\) :

Function of nanoparticle volume fraction

\(g\) :

Gravity acceleration

\(k\) :

Thermal conductivity

\(k_{nf}\) :

Thermal conductivity of nanofluid

\(k_{f}\) :

Thermal conductivity of pure fluid

\(k_{s}\) :

Thermal conductivity of nanoparticle

\(kr\) :

Rotation parameter

\(M\) :

Magnetic parameter

\(Nb\) :

Brownian motion parameter

\(Nt\) :

Thermophoresis parameter

\(Nu\) :

Nusselt number

P :

Pressure

Pr:

Prandtl number

\(\delta\) :

Condensate film thickness

\(\varphi\) :

Nanoparticle volume fraction

\(\eta ,\,\xi\) :

Dimensionless distance

\(\mu_{nf}\) :

Viscosity of nanofluid

\(\theta\) :

Dimensionless temperature

\(\rho\) :

Density

\(\rho_{nf}\) :

Density of nanofluid

\(\rho_{f}\) :

Density of pure fluid

\(\rho_{s}\) :

Density of nanoparticle

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Acknowledgements

The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (Nos. 51422604, 21276206) and the National 863 Program of China (No. 2013AA050402). This work was also supported by the China Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. International Research Center for Renewable Energy, State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    M. Hatami & D. Jing

  2. Department of Mechanical Engineering, Esfarayen University of Technology, Esfarayen, North Khorasan, Iran

    M. Hatami

  3. Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    S. Mosayebidorcheh

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  1. M. Hatami
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Correspondence to D. Jing.

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Hatami, M., Mosayebidorcheh, S. & Jing, D. Two-phase nanofluid condensation and heat transfer modeling using least square method (LSM) for industrial applications. Heat Mass Transfer 53, 2061–2072 (2017). https://doi.org/10.1007/s00231-016-1964-5

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