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Research Papers

Computational Analysis of Nanofluid Cooling of High Concentration Photovoltaic Cells

[+] Author and Article Information
Z. Xu

Department of Mechanical and
Aerospace Engineering,
North Carolina State University,
911 Oval Drive,
Raleigh, NC 27695

C. Kleinstreuer

Department of Mechanical and
Aerospace Engineering,
North Carolina State University,
911 Oval Drive,
Raleigh, NC 27695
e-mail: ck@ncsu.edu

1Corresponding author.

Manuscript received June 5, 2013; final manuscript received December 17, 2013; published online March 17, 2014. Assoc. Editor: S. A. Sherif.

J. Thermal Sci. Eng. Appl 6(3), 031009 (Mar 17, 2014) (9 pages) Paper No: TSEA-13-1123; doi: 10.1115/1.4026355 History: Received June 05, 2013; Revised December 17, 2013

High concentration photovoltaic devices require effective heat rejection to keep the solar cells within a suitable temperature range and to achieve acceptable system efficiencies. Various techniques have been developed to achieve these goals. For example, nanofluids as coolants have remarkable heat transfer characteristics with broad applications; but, little is known of its performance for concentration photovoltaic cooling. Generally, a cooling system should be designed to keep the system within a tolerable temperature range, to minimize energy waste, and to maximize system efficiency. In this paper, the thermal performance of an Al2O3-water cooling system for densely packed photovoltaic cells under high concentration has been computationally investigated. The model features a representative 2D cooling channel with photovoltaic cells, subject to heat conduction and turbulent nanofluid convection. Considering a semi-empirical nanofluid model for the thermal conductivity, the influence of different system design and operational parameters, including required pumping power, on cooling performance and improved system efficiency has been evaluated. Specifically, the varied system parameters include the nanoparticle volume fraction, the inlet Reynolds number, the inlet nanofluid temperature, and different channel heights. Optimal parameter values were found based on minimizing the system's entropy generation. Considering a typical 200-sun concentration, the best performance can be achieved with a channel of 10 mm height and an inlet Reynolds number of around 30,000, yielding a modest system efficiency of 20%. However, higher nanoparticle volume fractions and lower nanofluid inlet temperatures further improve the cell efficiency. For a more complete solar energy use, a combined concentration photovoltaic and thermal heating system are suggested.

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Figures

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Fig. 1

Physical model of the CPV receiver and the cooling system

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Fig. 2

Simplified model of the cooling channel for the concentrator cell module

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Fig. 3

Equivalent thermal circuit of cell, mounting, and cooling system

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Fig. 4

Model validation of dimensionless velocity profile in fully developed region and in the near wall region. y+ = y·v*/ν and u+ = u¯/v* where v* = (τw/ρ)1/2

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Fig. 5

Comparison of axial Nusselt number ratio

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Fig. 6

Comparison of cell efficiencies using water and nanofluid cooling under different inlet Reynolds numbers

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Fig. 7

Cell efficiency versus nanoparticle volume fraction

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Fig. 8

Cell efficiency versus Reynolds number

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Fig. 9

Gross power output and input/output power ratio over Reynolds numbers

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Fig. 10

Cell efficiency versus nanofluid inlet temperature. The two cases with constant pumping powers adopt the reference pumping powers of Re = 4000, Tin = 25 °C case, and Re = 30,000, Tin = 25 °C case, respectively.

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Fig. 11

Pumping power comparison for varying channel heights

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Fig. 12

Thermal and frictional entropy-generation rate versus channel height

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Fig. 13

Waste power and system net power output under different channel heights

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Fig. 14

System entropy generation versus nanoparticle volume fraction

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Fig. 15

System entropy generation versus Reynolds number. h = 10 mm.

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Fig. 16

System entropy generation versus nanofluid inlet temperature

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Fig. 17

Illustration of a concentration photovoltaic/thermal system

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