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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗
NASA NTRSDate not supplied

Developing a Finite Element Soyuz Seat Environment and Investigating Occupant Injury Metrics for Accelerative Loading Across Spaceflight Vehicle Designs

Landings in the Soyuz vehicle have generated more injuries than models have predicted, and the reason for this remains unknown. Presently, 3 new capsule-type spacecraft—the Orion, the SpaceX Dragon2, and the Boeing CST-100—are in various stages of development. To better understand the injury risk to Soyuz crewmembers, and to mitigate risk of future injuries onboard the new vehicles, we conducted a finite element (FE) modeling effort. At the outset of this study, previously developed FE models were available for the seats in the 3 new vehicles, whereas the Soyuz environment had not been modeled in an extensible manner. Thus, a model of the Soyuz seat was necessary to facilitate comparisons of all seat types.

D. A. Jones↗
NASA NTRS2021

Comparison of SAR and CYNSS surface water extent metrics over the Yucatan Lake wetland site

Wetlands have a major role in the carbon cycle, outgassing large quantities of carbon dioxide and methane through processes that are directly and strongly influenced by the duration and timing of inundation. Therefore, understanding the seasonal pattern of inundation can be a component for regional to global scale carbon models. Measurement of inundation extent also establishes a benchmark for the current status of wetland areas, useful in assessing the future impacts of climate change. The incorporation of frequent measurements of inundation extent into large-scale hydrological models would permit the evaluation of more detailed seasonal and longer-term floodplain dynamics and their associated management implications.The Cyclone GNSS (CYGNSS) constellation of satellites launched in 2016, and carries receivers capable of receiving data from L-band GNSS reflections. Delay Doppler maps (DDM) are generated on board and telemetered to the ground, along with a small number of raw data takes that can be used for special processing on the ground for evaluation purposes. It has been previously shown that these data can be sensitive to inundation.The NASA ISRO Synthetic Aperture Radar, currently planned for launch in January 2023, has both an L-band and S-band SAR for earth imaging. The L-band SAR, which will image the Earth's land mass twice every 12 days, has a requirement for measuring wetland inundation extent at the 1 ha scale. One of the sites that will be used to validate this requirement is Yucatan Lake, Louisiana. This oxbow lake and surrounding area located adjacent to the Mississippi river experiences periodic and extensive flooding in the surrounding forest areas.In 2019, NASA's UAVSAR fully polarimetric airborne L-band SAR conducted a flight campaign to image a dozen sites in the SE USA at approximately 12-day intervals and both in the morning and evening, to simulate the type of data NISAR will obtain. One site imaged during this campaign was the Yucatan Lake area, spanning water stages from low to high flood conditions.It has been demonstrated previously that GNSS reflectometry such as that measured by CYGNSS may be used to characterize surface inundation. It has also been known for decades that L-band SAR may be used to characterize not only the presence of open water, but also the presence of subcanopy inundation in forested areas. In this paper we will present results comparing data from these two types of instruments.

Lavalle, Marco↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry, Descent, and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC): A Proposed Mission to the Lunar South Pole-Aitken Basin

The global Lunar Magma Ocean (LMO) hypothesis is based on analyses of ferroan anorthosites (FAN), magnesian plutonic rocks (Mg-suite), and KREEPy (Potassium - K; Rare Earth Elements - REE; Phosphorous - P) material present at the Apollo landing sites and the assumed global distribution of these lithologies. However, orbital spacecraft data over the last two decades have highlighted that the location of the Apollo missions within and near the Procellarum KREEP Terrane (PKT) is compositionally anomalous and not representative of the entire lunar surface and suggests that Apollo samples provide a biased view of the Moon. Further, meteorites, which originate from random areas on the lunar surface, including those not visited by Apollo, Luna, or Chang’E-5, provide complementary datasets that reveal a more complex lunar formation and evolution than the simple LMO onion skin model. While many recent investigations support and refine the LMO model, other recent studies have questioned whether this mechanism alone is responsible for the primordial differentiation of the Moon. Thus, relating the distinct and asymmetrically distributed geochemical terranes on the Moon, especially KREEP which is a predicted global LMO product, to lunar formation and differentiation remains a fundamental goal of lunar science. The South Pole-Aitken (SPA) impact carved a two-and-a-half thousand-kilometer basin into the Moon’s ancient crust, excavating material from as deep as the upper mantle. Orbital remote sensing of the basin indicates compositions with elevated thorium (Th) contents and unique mineralogies that provide a window into early lunar structure and LMO processes. We propose to land a powerful characterization laboratory at Birkeland crater on the far side of the Moon–where Th-rich SPA ejecta have been re-exposed near the surface–in order to address outstanding questions about the evolution of the Moon.

K M Cannon↗