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Technical Brief

A Computational Study of Mixed Convection Heat Transfer From a Continuously Moving Isothermal Vertical Plate to Alumina–Water Nanofluid as in Hot Extrusion

[+] Author and Article Information
Arijit Mahapatra

Department of Mechanical Engineering,
Indian Institute of Technology Kanpur,
Kanpur 208016, Uttar Pradesh, India

P. S. Ghoshdastidar

Mem. ASME
Department of Mechanical Engineering,
Indian Institute of Technology Kanpur,
Kanpur 208016, Uttar Pradesh, India
e-mail: psg@iitk.ac.in

1Corresponding author.

Manuscript received December 16, 2016; final manuscript received July 22, 2017; published online September 13, 2017. Assoc. Editor: Yannis Korkolis.

J. Manuf. Sci. Eng 139(11), 114501 (Sep 13, 2017) (8 pages) Paper No: MANU-16-1654; doi: 10.1115/1.4037422 History: Received December 16, 2016; Revised July 22, 2017

The paper presents a computational study of steady, laminar, two-dimensional (2D) mixed convection heat transfer from a continuously moving isothermal vertical plate to alumina–water nanofluid as in hot extrusion. The simulation is based on a heterogeneous flow model which takes into account Brownian diffusion and thermophoresis of nanoparticles. The finite difference method is used to discretize the governing equations. The SIMPLE algorithm has been applied to obtain flow, thermal, and nanoparticle concentration fields. The numerical results have been validated satisfactorily with the published results for pure fluids. A detailed parametric study reveals that in the mixed convection regime, the enhancement factor (EF) (defined as the ratio of average heat transfer coefficient in nanofluid to that in base fluid) increases with nanoparticle concentration. The enhancement is more at lower Richardson number (Gr/Re2), that is, closer to forced convection regime. In the regime close to free convection, the EF is found to be very small. Larger plate velocity (that is, higher Reynolds number) has a positive effect on heat transfer enhancement but higher plate-fluid temperature difference results in lower EF. An enhancement in heat transfer coefficient as high as 22% is realized at the plate velocity of 0.4 m/s. The effectiveness (defined as the ratio of average heat transfer coefficient in nanofluid to the power required to pull the plate), in general, falls with higher volume fraction of nanoparticles and plate velocity and escalates with a rise in Richardson number and plate-fluid temperature difference.

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Figures

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Fig. 1

The physical problem and the computational domain

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Fig. 2

The boundary conditions and the grid

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Fig. 3

Comparison of streamlines in base fluid and nanofluidfor ϕ∞=0.01 and ϕ∞=0.05, L = 1 m, Ts − T = 30 K and u0=0.05 m/s

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Fig. 4

Temperature profiles at x/L = 0.5 for u0 = 0.05 m/s, L = 1 m, Ts−T∞=30 K and different nanoparticle volume fractions

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Fig. 5

Distribution of volume fraction of nanoparticles normal to the plate at x/L = 0.5 for u0  = 0.05 m/s, L = 1 m, Ts−T∞=30 K and different ϕ∞

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Fig. 6

Percent enhancement in heat transfer coefficient with nanoparticle volume fraction for Ts−T∞=30 K and L = 1 m and different values of plate velocity

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Fig. 7

Variation of effectiveness with nanoparticle volume fraction for Ts−T∞=30 K and L = 1 m and different values of plate velocity

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Fig. 8

Variation of percent enhancement in heat transfer coefficient with Richardson number for L = 0.1 m, Ts−T∞=30 K and different ϕ∞

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Fig. 9

Variation of percent enhancement in heat transfer coefficient with plate velocity for L = 1 m, Ts−T∞=30 K and different ϕ∞

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