Investigation of graphene based disk-square integration resonator for enhanced solar absorption using machine learning for solar heaters

Abstract

As worries about climate change and energy security grow, the need for clean and sustainable energy sources becomes increasingly critical. Among the possible alternatives, solar thermal technology provides a dependable and adaptable method for harnessing the sun's energy as heat. At the core of this technique is the solar thermal absorber, a critical component that converts sunlight into useful thermal energy. The Graphene Based Disk-Square Integration Resonator Solar Absorber (GBDSIRSA) structure is examined in this work between 200 and 2500 nm in wavelength. The GBDSIRSA has remarkable performance throughout a wide variety of spectral ranges, underscoring its adaptability and effectiveness. GBDSIRSA has exceptional efficiency in absorbing light over the full spectrum, from UV to MIR wavelengths, with an average absorptance of 97.73%. The absorptance rates are particularly high, achieving 94.02% in the UV, 96.36% in the VIS, 97.12% in the NIR, and an astounding 98.94% in the mid-infrared (MIR) region. The GBDSIRSA is independent of polarization effect and also an incident angle independent up to 80 degrees. The machine learning (Local Regression) approach used with test size of 0.2 and 1.8780 ×10 -5 mean squared error for absorptance prediction has good prediction accuracy R 2 of more than 95% which is applied to simulation resource reduction. Because of its wide absorptance spectrum, polarization and incident angle independence, the GBDSIRSA is a very promising technology for a variety of applications, including thermal imaging and renewable energy. It can efficiently harvest solar energy. All things considered, GBDSIRSA's remarkable absorptance properties highlight its promise as a top choice for cutting-edge optical and solar energy applications.