Abstract
Since solar thermal stations, domestic hot water and heat-process systems all are being to use solar energy by converting the sun’s rays into heat. In order for these processes to be effective, they must absorb as much solar radiation as possible while limiting the loss radiative heat from the absorbent surface which has to have selectivity in the wavelengths of the solar spectrum. This selectivity allows it to have a maximum of absorbance with a minimum of thermal radiation emitted. This article deals with the optimization based on artificial neural networks of the solar absorption and infrared emission of the absorbing surface, in terms of absorbance and emissivity, study the possibility of integrating multilayer films deposited on aluminum and cover substrates to describe the properties of a surface. More than 300 different coatings and surface treatments for selective absorption of solar energy have been reported in the scientific and technical literature. Only a few of these have been subjected to detailed theoretical analyses [1]. In our multilayer, we used two materials having, respectively, a high and a low refractive index. We studied two cases: silicone black-pigmented coating/Polyurethane black-pigmented coating and Polyurethane black-pigmented coating/SiO2. The thin films were deposited by Dip-coating technique. Spectroscopic ellipsometry was used to determine the optical constants and the thicknesses of every individual multilayer.
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Haddad, F., Hatti, M., Zaamoum, R., Rahmoun, K. (2018). Photo-Thermal Study of the Optical Properties of Multi-layer Coatings Based on Black Pigmented Coatings/SiO2 Applied for Solar Absorber. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_14
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DOI: https://doi.org/10.1007/978-3-319-73192-6_14
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