Investigation of plus-shaped half-circular ring metasurface-based solar thermal absorber design optimized utilizing machine learning for renewable energy applications

The rising thermal energy needs of advanced industrial systems, coupled with the pressing necessity to curb fossil fuel dependence, are driving rapid growth in renewable thermal energy adoption. Sustainable energy technologies are pivotal in lowering ecological degradation, improving environmental sustainability, and addressing the global challenges associated with accelerating climate change. Among these, solar thermal energy offers a simple and effective solution for sustainable electricity generation, especially for thermal applications. In this work, a broadband multilayer solar thermal absorber is proposed, achieving high thermal absorption rates exceeding 90% across a broad spectral range up to 2800 nm. The thermal absorber demonstrates near-perfect absorption above 98% at 700 nm, making it highly suitable for practical energy harvesting. Additionally, machine learning techniques are employed to analyze the parametric variations, comparing actual and predicted results for enhanced design optimization. Electromagnetic and magnetic field distributions are analyzed to validate the absorption performance, while polarization insensitivity under varying incident angles is also demonstrated. With its multifunctional capabilities, the proposed solar absorber can be effectively applied in thermal systems such as solar cookers, swimming pool heating, space heating, agricultural processes, and various industrial thermal applications.