The Radiative Cooling Compatible Infrared Stealth Multilayered Films Design Based on Deep Neural Network
The infrared radiative signature of the military target is determined by the emissivity of the infrared atmospheric window and the temperature level.Conventional infrared stealth coatings exhibit low emissivity across the whole infrared band but lack effectively radiative cooling through non-atmospheric window.This work designs a set of infrared stealth multilayered films structure based on deep neural network,incorporating germanium,platinum,and silicon arranged in order for compatible radiative cooling.The analysis reveals that the structure achieves a low average emissivity of 0.20/0.23 within the infrared atmospheric window detection bands of 3～5 μm and 8～14 μm,while maintaining a high average emissivity of 0.87 within the non-atmospheric window band of 5～8 μm,thus facilitating efficient radiative cooling.Furthermore,the designed structure shows strong robustness regarding the polarization and incidence angle of the incoming electromagnetic wave.The spectral selectivity of the structure is attributed to the selective transmission of the germanium layer,the Fabry-Perot resonance generated by the Pt-Si-Titanium alloy TC4,as well as the intrinsic absorption of the Pt layer and TC4 substrate.
modulation of radiative propertiesdeep neural networkmultilayered films structure
NETL
NSTL
万方数据
