Robotics & Machine Learning Daily News2024,Issue(Jun.4) :55-56.

Studies from Purdue University in the Area of Machine Learning Reported (Machine Learning-based Design Optimization of Aperiodic Multilayer Coatings for Enhance d Solar Reflection)

普渡大学在机器学习领域的研究报告(基于机器学习的非周期多层膜增强三维太阳反射设计优化)

Robotics & Machine Learning Daily News2024,Issue(Jun.4) :55-56.

Studies from Purdue University in the Area of Machine Learning Reported (Machine Learning-based Design Optimization of Aperiodic Multilayer Coatings for Enhance d Solar Reflection)

普渡大学在机器学习领域的研究报告(基于机器学习的非周期多层膜增强三维太阳反射设计优化)

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摘要

机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。据NewsRx记者从印第安纳州西拉斐特发回的新闻报道,研究表明:“多层涂层在半导体、光学反射镜和能量收集技术方面有着广阔的应用前景和成功的前景。其中,光学反射镜是被动辐射冷却的关键。”这项研究的财政支持来自国家科学基金会(NSF)。我们的新闻编辑引用了普渡大学的一篇研究,“基于在蜗牛中观察到的多层辐射冷却系统,并借鉴前人的研究成果,本研究展示了机器学习算法在优化和深入了解多层结构方面的有效性。由于生物发现的方解石晶体中天空窗口发射率低的限制,”关注太阳反射率对于最大限度地利用蜗牛体内的生物光子至关重要。人工搜索具有气隙的方解石周期性多层设计SPA CE表明,20μm涂层厚度170 nm处的最大太阳反射率为-89%。然后我们采用了基于机器学习的进化优化方法-遗传算法。优化后的非周期涂层在20μm的涂层中,太阳反射率明显提高到-99.8%。有趣的是,优化后的非周期涂层在20μm的涂层中,太阳反射率明显提高到-99.8%。在20μm周期和非周期方解石多层膜中,170 nm的平均层厚度提供了最大的太阳反射率。光谱反射率的研究表明,层厚度对调节太阳反射率至关重要。对于较小的涂层,优先考虑太阳强度较高的波长。增加涂层厚度可以包含较厚的Er层,以反射较长的波长。对辐射制冷材料的进一步研究表明,方解石和硫酸钡由于折射率的差异,对日光的反射明显优于二氧化硅。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from West Lafay ette, Indiana, by NewsRx correspondents, research stated, “Multilayered coatings are promising and successful for applications in semiconductors, optical mirror s, and energy harvesting technologies. Amongst these, optical mirrors are essent ial for passive radiative cooling.” Financial support for this research came from National Science Foundation (NSF). Our news editors obtained a quote from the research from Purdue University, “Bui lding upon the multilayer radiative cooling systems observed in snails and drawi ng from previous research, this study showcases the efficacy of machine learning algorithms in optimizing and gaining insights into multilayer structures. Due t o the constraint of low sky window emissivity in biologically found calcite shel ls, focusing on solar reflectance becomes crucial to maximize the biological phe nomenon found in snails. The manual search of the periodic multilayer design spa ce for calcite with air gaps points to the maximum solar reflectance of -89% at 170 nm layer thickness for 20 mu m coating. To unlock the full potential of t hese multilayers, we then employ machine learning -based evolutionary optimizati on method - a genetic algorithm. The optimized aperiodic coating shows a signifi cant enhancement of solar reflectance to -99.8% for a 20 mu m coat ing. Interestingly, the same average layer thickness of 170 nm provides maximum solar reflectance in 20 mu m periodic and aperiodic calcite multilayer. Investig ation of the spectral reflectance shows that layer thickness is crucial in tunin g the solar reflectance. For small coatings, wavelengths with higher solar inten sity are prioritized. Increasing the coating thickness allows inclusion of thick er layers to reflect longer wavelengths, leading to increasing trend of average calcite layer thickness. Further work exploring radiative cooling materials show s that calcite and barium sulfate reflect sunlight significantly better than sil icon dioxide due to their refractive index contrast.”

Key words

West Lafayette/Indiana/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Pur due University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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