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深度学习助力选择性太阳能吸收器的设计。

Deep learning empowering design for selective solar absorber.

作者信息

Ma Wenzhuang, Chen Wei, Li Degui, Liu Yue, Yin Juhang, Tu Chunzhi, Xia Yunlong, Shen Gefei, Zhou Peiheng, Deng Longjiang, Zhang Li

机构信息

National Engineering Research Center of Electromagnetic Radiation Control Materials, Key Laboratory of Multi-spectral Absorbing Materials and Structures of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen University Xiamen, Fujian 361005, China.

出版信息

Nanophotonics. 2023 Aug 11;12(18):3589-3601. doi: 10.1515/nanoph-2023-0291. eCollection 2023 Sep.

DOI:10.1515/nanoph-2023-0291
PMID:39635349
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11502052/
Abstract

The selective broadband absorption of solar radiation plays a crucial role in applying solar energy. However, despite being a decade-old technology, the rapid and precise designs of selective absorbers spanning from the solar spectrum to the infrared region remain a significant challenge. This work develops a high-performance design paradigm that combines deep learning and multi-objective double annealing algorithms to optimize multilayer nanostructures for maximizing solar spectral absorption and minimum infrared radiation. Based on deep learning design, we experimentally fabricate the designed absorber and demonstrate its photothermal effect under sunlight. The absorber exhibits exceptional absorption in the solar spectrum (calculated/measured = 0.98/0.94) and low average emissivity in the infrared region (calculated/measured = 0.08/0.19). This absorber has the potential to result in annual energy savings of up to 1743 kW h/m in areas with abundant solar radiation resources. Our study opens a powerful design method to study solar-thermal energy harvesting and manipulation, which will facilitate for their broad applications in other engineering applications.

摘要

太阳能辐射的选择性宽带吸收在太阳能应用中起着至关重要的作用。然而,尽管这是一项已有十年历史的技术,但从太阳光谱到红外区域的选择性吸收器的快速精确设计仍然是一项重大挑战。这项工作开发了一种高性能设计范式,将深度学习和多目标双重退火算法相结合,以优化多层纳米结构,从而实现太阳能光谱吸收最大化和红外辐射最小化。基于深度学习设计,我们通过实验制造了所设计的吸收器,并展示了其在阳光下的光热效应。该吸收器在太阳光谱中表现出优异的吸收率(计算值/测量值 = 0.98/0.94),在红外区域具有较低的平均发射率(计算值/测量值 = 0.08/0.19)。在太阳能辐射资源丰富的地区,这种吸收器有可能实现每年每平方米高达1743千瓦时的节能效果。我们的研究开启了一种强大的设计方法,用于研究太阳能热收集和操控,这将促进其在其他工程应用中的广泛应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/b587f1722b30/j_nanoph-2023-0291_fig_006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/feca3697aa9d/j_nanoph-2023-0291_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/bacb583d94f8/j_nanoph-2023-0291_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/f71e33929a32/j_nanoph-2023-0291_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/9183a37d1e12/j_nanoph-2023-0291_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/ea205b0d5488/j_nanoph-2023-0291_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/b587f1722b30/j_nanoph-2023-0291_fig_006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/feca3697aa9d/j_nanoph-2023-0291_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/bacb583d94f8/j_nanoph-2023-0291_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/f71e33929a32/j_nanoph-2023-0291_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/9183a37d1e12/j_nanoph-2023-0291_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/ea205b0d5488/j_nanoph-2023-0291_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fda0/11502052/b587f1722b30/j_nanoph-2023-0291_fig_006.jpg

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