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Research Papers: Forced Convection

Pumping Energy Saving Using Nanoparticle Suspensions as Heat Transfer Fluids

[+] Author and Article Information
Massimo Corcione

Mem. ASME
e-mail: massimo.corcione@uniroma1.it

Alessandro Quintino

DIAEE Sezione Fisica Tecnica,
Sapienza Università di Roma,
via Eudossiana 18,
00184 Rome, Italy

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 15, 2011; final manuscript received April 30, 2012; published online October 5, 2012. Assoc. Editor: Robert D. Tzou.

J. Heat Transfer 134(12), 121701 (Oct 05, 2012) (9 pages) doi:10.1115/1.4007314 History: Received December 15, 2011; Revised April 30, 2012

The pumping power diminution consequent to the use of nanoparticle suspensions as heat transfer fluids is analyzed theoretically assuming that nanofluids behave like single-phase fluids. In this hypothesis, all the heat transfer and friction factor correlations originally developed for single-phase flows can be used also for nanoparticle suspensions, provided that the thermophysical properties appearing in them are the nanofluid effective properties calculated at the reference temperature. In this regard, two empirical equations, based on a wide variety of experimental data reported in the literature, are used for the evaluation of the nanofluid effective thermal conductivity and dynamic viscosity. Conversely, the other effective properties are computed by the traditional mixing theory. Both laminar and turbulent flow regimes are investigated, using the operating conditions, the nanoparticle diameter, and the solid–liquid combination as control parameters. The fundamental result obtained is the existence of an optimal particle loading for minimum cost of operation at constant heat transfer rate. A set of empirical dimensional algebraic equations is proposed to determine the optimal particle loading of water-based nanofluids.

Copyright © 2012 by ASME
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References

Figures

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Fig. 1

Distributions of keff/kf versus φ for Al2O3 + H2O, with dp and T as parameters

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Fig. 2

Comparison between the predictions of Eq. (19) for Al2O3 (dp = 45 nm) + H2O at T = 294 K and some available literature correlations/data [46-48]

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Fig. 3

Comparison between the predictions of Eq. (19) for Al2O3 (dp = 38 nm) + H2O at φ = 0.01–0.04 and some available literature correlations [48,49]

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Fig. 4

Distributions of μefff versus φ for water-based nanofluids, with dp as a parameter

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Fig. 5

Comparison between the predictions of Eq. (21) for water-based nanofluids containing nanoparticles with dp = 33 nm and some available literature correlations/data [16,58,58-60]

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Fig. 6

Distributions of δ (%) versus φ with Ref and dp as parameters

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Fig. 7

Distributions of δ (%) versus dp with Ref and φ as parameters

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Fig. 8

Distributions of δ (%) versus φ wit2h Ref and Tm as parameters

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Fig. 9

Distributions of δ (%) versus φ with Ref and L/D as parameters

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Fig. 10

Distributions of δ (%) versus φ with Ref as a parameter

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Fig. 11

Distributions of δ (%) versus Tm with Ref and φ as parameters

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Fig. 12

Distributions of δ (%) versus L/D with Ref and φ as parameters

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Fig. 13

Distributions of δ (%) versus Ref with φ as a parameter

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Fig. 14

Distributions of φopt (%) versus Tm with Ref and dp as parameters

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Fig. 15

Distributions of φopt (%) versus L/D with Ref as a parameter

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Fig. 16

Distributions of δ (%) versus φ for different solid–liquid combinations in laminar flow

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Fig. 17

Distributions of δ (%) versus φ for different solid–liquid combinations in turbulent flow

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Fig. 18

Comparison between Eq. (26) and the theoretical data of φopt (%)

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Fig. 19

Comparison between Eqs. (27)–(28) and the theoretical data of φopt (%)

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