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Radiative Heat Transfer

Specification of Micro-Nanoscale Radiative Patterns Using Inverse Analysis for Increasing Solar Panel Efficiency

[+] Author and Article Information
Shima Hajimirza

Department of Mechanical Engineering,  The University of Texas at Austin, Austin, TX 78712Shima@ices.utexas.edu

Georges El Hitti

Center for Energy and Processes, Mines ParisTech, Paris 75006, Francegeorges.el-hitti@mines-paristech.fr

Alex Heltzel

PC Krause and Associates, Inc., West Lafayette, IN 47906heltzel@pcka.com

John Howell

Department of Mechanical Engineering,  The University of Texas at Austin, Austin, TX 78712jhowell@mail.utexas.edu

These values of fixed parameters are based on the empirical results of Ref. [1] and are known to be nearly optimal.

In addition, this scale of thickness is difficult to model due to an enhancement factor dependent on an enhanced localized near-field decaying in a few tens of nanometers.

J. Heat Transfer 134(10), 102702 (Aug 07, 2012) (8 pages) doi:10.1115/1.4006209 History: Received July 30, 2011; Revised January 30, 2012; Published August 06, 2012; Online August 07, 2012

This work proposes a comprehensive and efficient optimization approach for designing surface patterning for increasing solar panel absorption efficiency using near-field radiation effects. Global and local optimization methods, such as the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton (BFGS-QN) and simulated annealing (SA), are employed for solving the inverse near-field radiation problem. In particular, a thin amorphous silicon (a-Si) solar panel with periodic silver nanowire patterning is considered. The design of the silver patterned solar panel is optimized to yield maximum enhancement in photon absorption. The optimization methods reproduce results found in the previous literature but with reduced computational expense. Additional geometric parameters, which are not discussed in previous work, are included in the optimization analysis, further allowing for increased absorption enhancement. Both the BFGS-QN and the SA methods give efficient results, providing designs with enhanced absorption.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Model of solar cell used for near-field radiation calculation

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Figure 2

Comparison between FMM (from Rockstuhl [13]) and FDTD for silicon thickness of 90 nm

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Figure 3

Enhancement factor as a function of nanowire period and thickness of silicon

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Figure 4

Inverse cost function (enhancement factor) per iteration for a 2-p SA simulation

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Figure 5

Inverse cost function (enhancement factor) per iteration for a 2-p QN simulation

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Figure 6

Minimum required number of trials (on average) versus enhancement factor for 100 SA and QN simulations

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Figure 7

Percentage of success versus enhancement factor for 100 SA and QN simulations

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Figure 8

Average inverse cost function per iteration for 100 2-p SA simulation

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Figure 9

Average inverse cost function per iteration for 100 2-p QN simulation

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Figure 10

Inverse cost function (enhancement factor) per iteration for a 4-p SA simulation

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Figure 11

Inverse cost function (enhancement factor) per iteration for a 4-p QN simulation

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Figure 12

Inverse cost function (enhancement factor) per iteration for a 4-p SA simulation

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Figure 13

Inverse cost function (enhancement factor) per iteration for a 4-p QN simulation

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Figure 14

Changes in the EF of the geometry obtained by 4-p inverse optimization as a function of incident angle

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