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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Responsible AI Framework for Air Traffic Management

Future system will require increased levels of automation to address increased diversity, density, environmental considerations resulting in higher complexity. Will automation be able to manage off-nominal, non-normal, unexpected, contingency situations?

artificial intelligence↗
NASA NTRSDate not supplied

Responsible AI Framework for Air Traffic Management

Future system will require increased levels of automation to address increased diversity, density, environmental considerations resulting in higher complexity. Will automation be able to manage off-nominal, non-normal, unexpected, contingency situations?

artificial intelligence↗
NASA NTRSDate not supplied

Using Artificial Intelligence (AI) and Machine Learning (ML) to conduct Space Missions Solid Waste Management Survey

The National Aeronautics and Space Administration (NASA) Solid Waste Management team has been focusing on technologies that can operate in microgravity. NASA aims to conduct both short and long-term transit and planetary missions on the lunar and Mars surfaces. Therefore, an updated waste survey is needed to explore technologies for operation in microgravity for transit missions and partial gravity for planetary missions. This paper will utilize Artificial Intelligence and Machine Learning techniques to conduct the survey and generate knowledge graphs for Spacecraft Waste Management.

Artificial Intelligence↗
NASA NTRSDate not supplied

Automation and AI in Space Drilling

Future planetary surface sampling missions, such as delving past the near-surface ice layers on Mars in search of organics and possibly signs of past/extant life, will require lightweight, low-mass planetary drilling and sample handling. Unlike terrestrial drills, these exploration drills must work dry (without drilling muds or gas), blind (no prior local or regional seismic or other surveys), and light (very low downward force or weight on bit, and perhaps 100 W available from solar power or batteries). Given the lightspeed transmission delays to Mars and outward, an exploratory planetary drill cannot be controlled directly from Earth. Drills that penetrate deeper than a few centimeters are likely to get stuck if operated open-loop (the MSL drill only penetrates 5 cm, and the MER Rock Abrasion Tools 5 mm by comparison), so some form of local drill control is required. In the relatively near-term, human crews cannot be presumed to be available for surface instrument teleoperation. Therefore highly automated drill and sample-transfer operations will be required, to explore the subsurface with the ability to safe robotic drilling systems and recover and continue on from the most probable fault conditions. Current automation, scheduling and diagnostic approaches will be discussed that roughly track the actions and roles of humans in terrestrial manual drilling operations.

drill automation↗
NASA NTRSDate not supplied

End-User Assessment of the NASA SPoRT Lightning AI Product

The NASA Short-term Prediction Research and Transition (SPoRT) Center has begun to develop products to address operational challenges and enhance safety during the lifecycle of lightning activity, and conduct evaluations in concentrated R2O/O2R efforts. The product evaluated for this study is Lightning Artificial Intelligence (A.I.), which predicts the probability of lightning out to 15 minutes in advance and spatially maps it using filled color contours. Lightning A.I. uses reflectivity, differential reflectivity, and correlation coefficient data from a subset of radars within the NEXRAD network to generate lightning probabilities at approximately 2 km resolution. The domains, spanning 145 x 145 km are centered over NASA-affiliated centers across the CONUS and use radar data which are closest in proximity. Lightning A.I. was developed with intended use by emergency managers at NASA centers. However, National Weather Service (NWS) offices are also often tasked with monitoring and forecasting the threat for lightning within their County Warning Areas for various impact-based decision support services. These forecasts are typically provided for aviation operations and large-scale, outdoor events, which may have varying safety requirements for decision-making based on lightning proximity and recency. The NASA SPoRT center conducted an assessment of the operational uses of Lightning A.I by various collaborative NWS Offices from late July into early September. This assessment included feedback from participants on both the product itself as well as its accessibility within the new, interactive NASA SPoRT Lightning Viewer. This presentation will include background information about Lightning A.I. and highlight results from this assessment. The feedback from the assessment will be used to inform research on any necessary modifications to this and future lightning products to assist end users.

Kelley Murphy↗
NASA NTRSDate not supplied

AI-enabled Autonomous Systems: Space Power Applications

Advances in artificial intelligence (A.I.) over the last decade opens new opportunities for NASA to advance the state of the art in autonomous missions beyond low-Earth-orbit. Still, many challenges remain before these technologies can be deployed on manned spacecraft. This presentation summarizes many of these challenges, provides recommendations on how to address these problems, and provides an example of NASA’s Autonomous Power Controller's A.I.-based fault detection system.

Artificial Intelligence↗