Volume 25, Issue 5
Computational Optimal Design of Random Rough Surfaces in Thin-Film Solar Cells

Gang Bao, Yanzhao Cao, Junshan Lin & Hans Werner Van Wyk

Commun. Comput. Phys., 25 (2019), pp. 1591-1612.

Published online: 2019-01

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  • Abstract

Random rough textures can increase the absorbing efficiency of solar cells by trapping the optical light and increasing the optical path of photons. In this paper, we are concerned with optimal design of random rough surfaces in thin-film solar cells. We formulate the design problem as a random PDE constrained optimization problem and employ gradient-based methods for solving the problem numerically. To evaluate the gradient of the objective function, the Monte-Carlo method is used for sampling the probability space and the adjoint state method is employed to calculate the gradient at each sample. Numerical examples are shown to test the efficiency of the proposed algorithm. It is demonstrated that optimally obtained random textures yield an enormous absorption enhancement and a higher photon absorptance than that of existing random textures.

  • Keywords

Optimal design, random rough surface, solar cell, Helmholtz equation.

  • AMS Subject Headings

35J05, 35Q60, 49Q10, 65C05, 65C30

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-25-1591, author = {}, title = {Computational Optimal Design of Random Rough Surfaces in Thin-Film Solar Cells}, journal = {Communications in Computational Physics}, year = {2019}, volume = {25}, number = {5}, pages = {1591--1612}, abstract = {

Random rough textures can increase the absorbing efficiency of solar cells by trapping the optical light and increasing the optical path of photons. In this paper, we are concerned with optimal design of random rough surfaces in thin-film solar cells. We formulate the design problem as a random PDE constrained optimization problem and employ gradient-based methods for solving the problem numerically. To evaluate the gradient of the objective function, the Monte-Carlo method is used for sampling the probability space and the adjoint state method is employed to calculate the gradient at each sample. Numerical examples are shown to test the efficiency of the proposed algorithm. It is demonstrated that optimally obtained random textures yield an enormous absorption enhancement and a higher photon absorptance than that of existing random textures.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-0013}, url = {http://global-sci.org/intro/article_detail/cicp/12963.html} }
TY - JOUR T1 - Computational Optimal Design of Random Rough Surfaces in Thin-Film Solar Cells JO - Communications in Computational Physics VL - 5 SP - 1591 EP - 1612 PY - 2019 DA - 2019/01 SN - 25 DO - http://doi.org/10.4208/cicp.OA-2018-0013 UR - https://global-sci.org/intro/article_detail/cicp/12963.html KW - Optimal design, random rough surface, solar cell, Helmholtz equation. AB -

Random rough textures can increase the absorbing efficiency of solar cells by trapping the optical light and increasing the optical path of photons. In this paper, we are concerned with optimal design of random rough surfaces in thin-film solar cells. We formulate the design problem as a random PDE constrained optimization problem and employ gradient-based methods for solving the problem numerically. To evaluate the gradient of the objective function, the Monte-Carlo method is used for sampling the probability space and the adjoint state method is employed to calculate the gradient at each sample. Numerical examples are shown to test the efficiency of the proposed algorithm. It is demonstrated that optimally obtained random textures yield an enormous absorption enhancement and a higher photon absorptance than that of existing random textures.

Gang Bao, Yanzhao Cao, Junshan Lin & Hans Werner Van Wyk. (2020). Computational Optimal Design of Random Rough Surfaces in Thin-Film Solar Cells. Communications in Computational Physics. 25 (5). 1591-1612. doi:10.4208/cicp.OA-2018-0013
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