Design and thermal engineering optimization of ultra-stable metamaterial solar thermal absorbers through machine learning for industrial heating applications

Global warming and energy demand have rapidly increased worldwide, and energy generation depends on fossil fuels, which affect the environment. Renewable energy sources are significant options to fulfil the energy demand, and solar thermal energy is a clean and pollution-free source. This paper shows the Vertical Cylindrical Multiple Ring Resonator Solar Thermal Absorber (VCMRSTA) and a solar thermal absorber that utilizes solar energy and transforms it into heat. The suggested VCMRSTA utilized the Al, 2D MXene, and Graphene materials with a highly thermally stable BaTiO3 ceramic material. MXene and ceramic BaTiO 3 material are used in the VCMRSTA resonator and substrate layer. Graphene material was deposited on the substrate, and Al was used in the VCMRSTA bottom layer. This VCMRSTA trapped the thermal absorption in the UV area more than 98%, in the VIS area more than 96%, in the NIR area more than 93%, and in the NIR/SWIR area more than 93%. This VCMRSTA achieved a 94.07% absorption range of 200 to 2500 nm. Polarization-insensitive VCMRSTA achieved 94.7% absorption under AM 1.5. Investigate the VCMRSTA Transverse Electric (TE) and Magnetic (TM) field with colour-shifting electric field distribution. The VCMRSTA optimized with Machine Learning (ML) got a high result and achieved a high R 2 value of 0.98305. The multiple-ring arrangement of the resonator achieved high thermal absorption and was utilized for renewable thermal energy applications.