Theoretical Design of a Multilayer Based Spectrally Selective Solar Absorber Applied Under Ambient Conditions

Abstract

With the increasing of global energy requirements and environmental problems, the use of solar thermal energy has attracted widespread attention. The selective solar absorption coating is the most important part of a solar thermal conversion device. At present, most of the coatings work well in a vacuum at a high temperature, while not stably in the air environment. Based on the high-temperature resistant and infrared-reflective properties of ITO, a multilayer film of SiO2/Si3N4/SiO2/ITO/Cr has been designed as a selective solar absorber. The genetic algorithm is applied to optimize the material and thickness selection for each layer. The results show that the optimized multilayer film could achieve a high solar absorptance up to 90% while keeping a relatively low infrared emittance around 50% for temperature change between 600°C and 900°C. All the materials composing this film have been tested before to be chemically stable at a high temperature up to 900°C in the air environment. It is also adaptive to different incident angles from 0° to 60°. The finite-difference time-domain method was also adopted to plot the energy density distribution for different wavelengths, which provided the underlying mechanism for the selective emission spectrum. The findings in this study would provide valuable guidance to design a low-cost selective solar absorption coating without the need for vacuum generation.

Upper bound efficiency for thermal radiation energy conversion by using spectrally selective electrical, chemical and mechanical work extractors

“…For generality here we keep g l (or e g and x g ) as a parameter. Typical dependence of the emittance on wavelength for spectrally selective coatings may be found, for instance, in figure 2 of [20]. The emittance is small for the range of energy of the photons emitted around the peak of material emission spectrum.…”

A Non-Scheduled Multi-Stage Decision-Making Approach to Control Cascading Failures

“…The proposed model is a non-linear programming problem which is difficult to be solved directly. Therefore, we draw on the idea of the two-layer decomposition optimization scheme proposed by Liu et al (2015), and the genetic algorithm (Shi et al, 2021) is used to solve it. The upper-level model optimizes the outage probability of predicted tripped branch for each cascading stage, and the obtained values are imported into the lower-level model.…”