Abstract
Radiative cooling is an energy-efficient technology without consuming power. Depending on their use, radiative coolers (RCs) can be designed to be either solar-transparent or solar-opaque, which requires complex spectral characteristics. Our research introduces a novel deep learning-based inverse design methodology for creating thin-film type RCs. Our deep learning algorithm determines the optimal optical constants, material volume ratios, and particle size distributions for oxide/nitride nanoparticle-embedded polyethylene films. It achieves the desired optical properties for both types of RCs through Mie Scattering and effective medium theory. We also assess the optical and thermal performance of each RCs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
