Design and performance evaluation of polarization stable solar thermal absorber for ultrawideband renewable energy solutions using machine learning

In this research, we designed a solar thermal absorber to improve the efficiency using advanced materials and machine learning algorithms. The developed solar absorber using novel metamaterials shows an optimized absorption efficiency of 91.11%, with near unity absorptivity across several spectral peaks. The structural parameters, namely, T1, T2, T3 and Wb, have been fine-tuned to be optimized for maximum performance. The comparative analysis of slotted and solid resonator designs in tabular form points out the efficiency of both, thereby showing the possibility of downsizing absorber structures without losing their effectiveness. This suggests that compact absorber layouts remain functionally robust, offering the potential for size reduction while maintaining strong absorption capabilities. The absorber maintains optimal performance across a wide range of incident angles, from 0° to 40°. This robustness under varying illumination conditions enhances its suitability for integration into scalable and sustainable renewable thermal energy systems, ensuring consistent energy absorption and efficiency regardless of orientation or environmental fluctuations. This work, therefore, highlights the transformative potential of metamaterial-based solar absorbers in renewable energy generation.