Robust optimization of a tandem grating solar thermal absorber

Choi, Jong-In; Kim, Mingeon; Kang, Kyeonghwan; Lee, Ikjin; Lee, Bong Jae

doi:10.1016/j.jqsrt.2018.01.028

Cited by 14 publications

(7 citation statements)

References 33 publications

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“…Because the solar spectrum covers from 280 nm to 4,000 nm, a broadband absorber is necessary to fully utilize solar energy. Although complicated nanostructures have been proposed as efficient solar thermal absorbers 2,18,19 , a simple fishnet design is proposed here to simplify the fabrication process while maintaining high solar absorption performance. As an absorbing material, we chose Cr because it has stronger intrinsic absorption than many novel metals and can also support plasmonic resonance in the visible spectrum 20 .…”

Section: Modelingmentioning

confidence: 99%

“…In addition to the deterministic optimization, we also conducted robust optimization 17 to find an absorber design that was relatively insensitive to fabrication errors. A multi-objective genetic algorithm 16 was used for the robust optimization 19 ; that is, the one objective function was to maximize the solar absorptance, and the other was to minimize the variation of the solar absorptance (i.e., standard deviation σ sol ) arising because of fabrication errors. In this work, the fabrication errors were assumed to follow a Gaussian distribution within the following uncertainty ranges 27 : 50 nm for Λ, 25 nm for w , 5 nm for d g , and 10 nm for both d uf and d bf .…”

Section: Modelingmentioning

confidence: 99%

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Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance

Seo

Jung

Kim

et al. 2019

Sci Rep

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In using nanostructures to design solar thermal absorbers, computational methods, such as rigorous coupled-wave analysis and the finite-difference time-domain method, are often employed to simulate light-structure interactions in the solar spectrum. However, those methods require heavy computational resources and CPU time. In this study, using a state-of-the-art modeling technique, i.e., deep learning, we demonstrate significant reduction of computational costs during the optimization processes. To minimize the number of samples obtained by actual simulation, only regulated amounts are prepared and used as a data set to train the deep neural network (DNN) model. Convergence of the constructed DNN model is carefully examined. Moreover, several analyses utilizing an evolutionary algorithm, which require a remarkable number of performance calculations, are performed using the trained DNN model. We show that deep learning effectively reduces the actual simulation counts compared to the case of a design process without a neural network model. Finally, the proposed solar thermal absorber is fabricated and its absorption performance is characterized.

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Section: Modelingmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance

Seo

Jung

Kim

et al. 2019

Sci Rep

Self Cite

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“…Both quality of merit [23] and Taguchi method [4,17] were utilized to develop absorbers when design objectives were quantitatively defined. On the other hand, the genetic algorithm was capable of tuning all design factors provided computational resources were sufficient [6].…”

Section: Introductionmentioning

confidence: 99%

Optimizing effectiveness and robustness for solar heat absorbers composed of a periodically nano-structured surface

Chen

2019

SN Appl. Sci.

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This work studies solar heat absorbers having periodic and rectangular nano-pillars on their surfaces. Design factors are periods, filling ratios, and pillar height. They are tailored using the design of experiments for absorption effectiveness and robustness. The former requires enlarging absorption within the solar spectrum as well as diminishing re-emission loss at long wavelengths. The latter refers to independence of absorption effectiveness from noise interference. Studied noise factors include dimensional deviations and variation of sunlight orientation. Effects from individual design factor and interactions between two design factors are all investigated. The selected absorbers exhibit appealing wavelengthselective absorptance spectra. Absorption effectiveness and robustness of these absorbers are quantitatively illustrated by a large response variable Q and its small variation S, respectively.

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“…They showed the influence of improvement steps on the accuracy of the metamodel and predicted the robust optimum. Choi et al (2018) implemented a robust optimization method for designing a tandem grating solar absorber. A Kriging method was used to build a metamodel in a five-dimensional input space and uncertainty propagation was evaluated using a genetic algorithm method.…”

Section: Introductionmentioning

confidence: 99%

Robust optimization based on analytical evaluation of uncertainty propagation

Nejadseyfi

Geijselaers

Boogaard

2018

Engineering Optimization

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Optimization under uncertainty requires proper handling of those input parameters that contain scatter. Scatter in input parameters propagates through the process and causes scatter in the output. Stochastic methods (e.g. Monte Carlo) are very popular for assessing uncertainty propagation using black-box function metamodels. However, they are expensive. Therefore, in this article a direct method of calculating uncertainty propagation has been employed based on the analytical integration of a metamodel of a process. Analytical handling of noise variables not only improves the accuracy of the results but also provides the gradients of the output with respect to input variables. This is advantageous in the case of gradientbased optimization. Additionally, it is shown that the analytical approach can be applied during sequential improvement of the metamodel to obtain a more accurate representative model of the black-box function and to enhance the search for the robust optimum.

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Robust optimization of a tandem grating solar thermal absorber

Cited by 14 publications

References 33 publications

Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance

Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance

Optimizing effectiveness and robustness for solar heat absorbers composed of a periodically nano-structured surface

Robust optimization based on analytical evaluation of uncertainty propagation

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