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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Current Status and Future Plans for Electric Motors and Drives at NASA

Our work advances propulsion for aircraft while reducing energy consumption, noise, emissions and the cost of air travel. We are committed to investigating the use of alternative energy sources and improving the safety and expediency of flight. Electrified Aircraft Propulsion (EAP) is the use of propulsors (propellers or fans) driven by electric motors to propel aircraft ranging from air taxis to subsonic transports. NASA is developing technology, aircraft concepts, test aircraft, and ground test facilities to turn this idea from science fiction to- reality. Specifically, motors and/or generators (Electric machines) are needed on all electrified aircraft. And NASA is sponsoring or performing work to achieve power densities 2-3 times the state of the art for machines in the MW or larger class. Three major machine types are being developed: permanent magnet, induction, and wound field. Power converters are also an essential component in most EAP aircraft concepts, as they are used to convert from ac to dc power, or vice versa. Silicon carbide and gallium nitride convertors are being developed with conventional cooling as well as a cryogenically cooled converter. Future plans include demonstrating integrated flight-ready systems in both ground and flight tests; collaborating with and leveraging ARPA-E EAP programs; and supporting FAA standards development for market acceptance and safety.

Aircraft↗
NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗
NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗