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Research Papers

Modeling Forced Convection Nanofluid Heat Transfer Using an Eulerian–Lagrangian Approach

[+] Author and Article Information
Sandipkumar Sonawane

Department of Mechanical Engineering,
NDMVP's KBT College of Engineering,
Udoji Maratha Boarding Campus,
Gangapur Road,
Nashik 422013, India
e-mail: sbsonawane1980@gmail.com

Upendra Bhandarkar

Department of Mechanical Engineering,
Indian Institute of Technology Bombay,
Powai, Mumbai 400076, India
e-mail: bhandarkar@iitb.ac.in

Bhalchandra Puranik

Department of Mechanical Engineering,
Indian Institute of Technology Bombay,
Powai, Mumbai 400076, India
e-mail: puranik@iitb.ac.in

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received February 25, 2015; final manuscript received October 29, 2015; published online March 22, 2016. Assoc. Editor: Srinath V. Ekkad.

J. Thermal Sci. Eng. Appl 8(3), 031001 (Mar 22, 2016) (8 pages) Paper No: TSEA-15-1055; doi: 10.1115/1.4032734 History: Received February 25, 2015; Revised October 29, 2015

An Eulerian–Lagrangian model is used to simulate turbulent-forced convection heat transfer in internal flow using dilute nanofluids. For comparison, a single-phase model of the nanofluid which describes a nanofluid as a single-phase fluid with appropriately defined thermophysical properties is also implemented. The Eulerian–Lagrangian model, which requires only the properties of the base fluid and nanoparticles separately, is seen to predict the heat transfer characteristics accurately without resort to any models for the thermophysical properties. The simulations with the single-phase model show that it can very well be used to predict the heat transfer behavior of dilute nanofluids as long as the thermophysical properties are directly those measured experimentally or those predicted from a Brownian motion based model. These approaches are particularly useful for engineering estimation of heat transfer performance of equipment where nanofluids are expected to be used.

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References

Figures

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Fig. 1

Schematic of the configuration under investigation

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Fig. 2

Bulk temperature of ATF as a function of developed region length for Re = 9000

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Fig. 3

Local Nusselt number for ATF as a function of developed region length for Re = 9000

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Fig. 4

Comparison of the numerical predictions with the experimental data and with the predictions using the Dittus–Boelter correlation for pure ATF

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Fig. 5

Comparison of numerically predicted pressure drop with the experimentally measured values for ATF–CuO nanofluid at 50 °C mean fluid temperature

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Fig. 6

Comparison of numerically predicted heat transfer coefficients with the experimentally measured values for ATF–CuO nanofluid at 0.3% particle volume concentration

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Fig. 7

Comparison of the numerical predictions with the experimental results, Gnielinski correlation, and Dittus–Boelter equation for ATF–CuO nanofluid

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Fig. 8

Comparison of the numerical predictions with the experimental results, Gnielinski correlation, and Dittus–Boelter equation for ATF–Al2O3 nanofluid

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Fig. 9

Comparison of the numerical predictions with experimental results, Gnielinski correlation, and Dittus–Boelter equation for ATF–TiO2 nanofluid

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Fig. 10

(a) Temperature distribution of ATF–CuO nanofluid at 0.3% particle volume concentration and constant wall temperature boundary conditions and (b) enlarged view close to the wall

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Fig. 11

Numerical prediction comparison with experimental data reported by Kayhani et al. [12] at 0.1% particle volume concentration

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Fig. 12

Numerical prediction comparison with experimental data reported by Kayhani et al. [12] at 0.5% particle volume concentration

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Fig. 13

Effect of Brownian force on heat transfer coefficient of ATF + 0.3% Al2O3 nanofluid in laminar region

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Fig. 14

Effect of Brownian force on Nusselt number of ATF + 0.3% Al2O3 nanofluid in laminar region

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