Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using...

Area of Science:

• Materials Science
• Renewable Energy Engineering
• Nanotechnology

Background:

• Increasing global energy demand necessitates efficient renewable energy solutions.
• Solar absorbers convert solar energy to thermal energy, crucial for solar thermal applications.
• Current designs often lack ultra-broadband absorption and wide-angle performance.

Purpose of the Study:

• To propose and analyze a highly efficient, ultra-broadband solar absorber design.
• To investigate the absorption characteristics across visible, ultraviolet, and near-infrared spectra.
• To utilize machine learning for predicting absorber performance and optimizing design parameters.

Main Methods:

• Fabrication and simulation of three metamaterial designs: plus-shape slotted, plus-shape, and square-shape.
• Optimization of a selected design for enhanced solar absorption efficiency.
• Analysis of absorption response against the AM 1.5 spectral irradiance.
• Electric field response analysis for metamaterial designs.
• Application of machine learning (regression and forecasting) for performance prediction.

Main Results:

• The optimized solar absorber design demonstrates high efficiency and ultra-broadband absorption.
• The absorber exhibits excellent performance across a wide range of incidence angles.
• Metamaterial designs show distinct absorption characteristics matching solar spectrum regions.
• Machine learning effectively predicts absorber behavior, reducing simulation overhead.

Conclusions:

• The proposed solar absorber design is highly efficient, ultra-broadband, and wide-angle.
• Machine learning serves as an effective tool for optimizing solar absorber design and reducing simulation efforts.
• This technology holds significant potential for advancing solar thermal energy harvesting applications.