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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Active In-Flight Load Redistribution Utilizing Fiber-Optic Shape Sensing and Multiple Control Surfaces

Morphing wing technologies have gained research interest in recent years as technological advancements pave the way for such innovations. A key benefit of such a morphing wing concept is the ability of the wing to transition into an optimal configuration at multiple flight conditions. Such a morphing wing will have applications not only in drag reduction but also in flutter suppression and gust alleviation. By manipulating the wing geometry to match a given flight profile it is likely that the wing will yield increases in not just aerodynamic efficiency but also structural efficiency. These structurally efficient designs will likely rely on some type of structural sensing system which will ensure the wing maintains positive margins throughout its flight profile.

FOSS↗
NASA NTRS2017

NASA Facilities for Airframe and Engine Icing

This presentation is intended for a technical audience. It describes NASA Glenn's two icing-capable facilities, the Icing Research Tunnel (IRT) and the Propulsion Systems Lab (PSL). The presentation contains stock images publicly available from NASA and other sources.

Icing Research Tunnel (IRT)↗
NASA NTRS2020

Onboard Automated Scheduling for the Mars 2020 Rover

The Mars 2020 Mission, scheduled to land on Mars February 18, 2021, has developed an onboard scheduling system [1]. The rationale for the onboard scheduler is to enable the Perseverance rover to adjust its activities in response to activities taking longer or shorter than planned, or using more or less resources than expected, as effectively using these resources could significantly improve rover productivity [2]. If deployed, the onboard scheduler would be an unprecedented use of Artificial Intelligence/Autonomy onboard software in a key role for a major mission.

Biehl, J.↗
NASA NTRSDate not supplied

Collecting Samples from the Máaz Formation of Jezero Crater with the Mars 2020 Perseverance Rover

Collection of samples that could be returned to Earth from the floor of Jezero,a Noachian crater characterized by a delta–lake system with high potential for habitability, is a major goal of the Mars 2020 mission. The Mars 2020 Perseverancerover iscurrently exploringthe Máaz and Séítahformationsto the southeastof the delta. Here wefocus on thecrater-retainingMáaz formation, a widespread, rough and fractured terrain with lobatemarginsmapped in orbital images, e.g.[1]. Outcrop morphology and texture, as well as the appearance, composition and mineralogy of abraded rock surfaces observed by Perseverance suggest that theMáazformation consists of asequence of maficigneous units, likely lavas flows. These rocks have experienced variable interaction with aqueous fluids. Type localities of the lower Roubion and the more resistant Rochette members of the Máazformation have been targeted and their abraded surfaces characterized prior to sample collection. In thispresentation we will summarize these sampling activities and potential future sampling of theheavily crateredupperCh’ałmember that is indicative of the Máazformationfrom orbit.

Justin Ibrahim Simon↗
NASA NTRSDate not supplied

AAM NC ATI TechTalk - Aerograph Architecture v1

Aerograph is NASA’s data management system for Advanced Air Mobility. Its mission is to support AAM research by providing a reliable and secure system that collects, stores, protects, and shares AAM data. Its vision is to provide a system that AAM research scientists, aerospace engineers, data scientists, and analysts trust for obtaining NC data and performing key analyses. The types of data Aerograph manages involves data related to flight test events, including: Aircraft Performance and Characterization (e.g., position reports) Airspace (e.g., operation intent, waypoints, and constraints) Environment (e.g., surface and wind weather) Infrastructure (e.g., surveillance coverage) Derivative Analytical Artifacts (e.g., glide path performance chart, 3D position chart, Integrated Data Product)

Aerograph↗