Patel Shobhit K, Parmar Juveriya, Katkar Vijay
Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, India.
Department of Electronics and Communication Engineering, Marwadi University, Rajkot, Gujarat, India.
Sci Rep. 2022 Jun 17;12(1):10166. doi: 10.1038/s41598-022-14509-y.
Energy utilization is increasing day by day and there is a need for highly efficient renewable energy sources. Solar absorbers with high efficiency can be used to meet these growing energy demands by transforming solar energy into thermal energy. Solar absorber design with highly efficient and Ultra-broadband response covering visible, ultraviolet, and near-infrared spectrum is proposed in this paper. The absorption response is observed for three metamaterial designs (plus-shape slotted design, plus-shape design, and square-shape design) and one optimized design is used for solar absorber design based on its high efficiency. The design results are compared with AM 1.5 spectral irradiance response. The electric field response of the plus-shape slotted metamaterial design is also presented which matches well with the absorption results of different solar spectrum regions. The results proved that the attained absorption response showing wide angle of incidence. Machine learning is also used to examine the design data in order to forecast absorption for various substrate thickness, metasurface thickness, and incidence angles. Regression and forecasting simulations based on machine learning are used to try to anticipate absorber behaviour at forthcoming and intermediate wavelengths. Simulation results prove that Machine Learning based methods can lessen the obligatory simulation resources, time and can be used as an effective tool while designing the absorber. The proposed highly efficient, wide-angle, ultra-broadband solar absorber design with its behavior prediction capability using machine learning can be utilized for solar thermal energy harvesting applications.
能源利用日益增加，因此需要高效的可再生能源。高效太阳能吸收器可通过将太阳能转化为热能来满足这些不断增长的能源需求。本文提出了具有高效和超宽带响应的太阳能吸收器设计，其响应覆盖可见光、紫外线和近红外光谱。观察了三种超材料设计（十字形开槽设计、十字形设计和方形设计）的吸收响应，并基于其高效率将一种优化设计用于太阳能吸收器设计。将设计结果与AM 1.5光谱辐照度响应进行了比较。还给出了十字形开槽超材料设计的电场响应，其与不同太阳光谱区域的吸收结果匹配良好。结果证明，所获得的吸收响应具有宽入射角。机器学习也用于检查设计数据，以便预测各种基板厚度、超表面厚度和入射角下的吸收情况。基于机器学习的回归和预测模拟用于尝试预测吸收器在未来和中间波长下的行为。模拟结果证明，基于机器学习的方法可以减少所需的模拟资源和时间，并且在设计吸收器时可作为一种有效工具。所提出的具有利用机器学习进行行为预测能力的高效、广角、超宽带太阳能吸收器设计可用于太阳能热能收集应用。
