Results for “passive daytime radiative cooling”

NASA NTRS2022

Mechanically Tunable Radiative Cooling for Adaptive Thermal Control

Passive radiative cooling is currently thefrontier technology in renewable-energy research. Interms of extraterrestrial applications, radiative cooling is a critical component to the thermalmanagement system of a spacecraft, where the extreme environment of space can cause largetemperature variations that can break and damage equipment. For terrestrial applications,nocturnal or daytime radiative cooling is expected to lead to cost-effective passive heatmanagement without the need of inefficient and costly artificial refrigeration technologies.However, most currently available radiative cooling systems cannot be changed dynamically andradiate a constant static amount of thermal power. Dynamically tunable adaptive radiativecooling systems will be a critical development to prolong the lifetime of spacecraft or improvethe efficiency of terrestrial cooling systems. Here we propose stretchable radiative coolingdesigns that can be substantially tuned by using the simple physical mechanism of mechanical strain. When their structure is stretched, the radiated power is significantly reduced. We developa modeling method that can simulate mechanical stretching combined with electromagnetic response to compute the tunable thermal emission of these new adaptive radiative coolingsystems. The presented photonically engineered structures can be used as coatings to achieveefficient adaptive thermal control of various objects in a cost-effective and environmentallyfriendly way. The proposed designs are much simpler to be realized than others found in theliterature and the best design achieves a high thermal emission power with a tunable range on theorder of 132 W/m2.

Andrew Butler↗

NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗