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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Robotic Lunar Landers for Science and Exploration

The MSFC/APL Robotic Lunar Landing Project (RLLDP) team has developed lander concepts encompassing a range of mission types and payloads for science, exploration, and technology demonstration missions: (1) Developed experience and expertise in lander systems, (2) incorporated lessons learned from previous efforts to improve the fidelity of mission concepts, analysis tools, and test beds Mature small and medium lander designs concepts have been developed: (1) Share largely a common design architecture. (2) Flexible for a large number of mission and payload options. High risk development areas have been successfully addressed Landers could be selected for a mission with much of the concept formulation phase work already complete

Cohen, Barbara A.↗
NASA NTRS2008

Mission Architecture and Technology Options for a Flagship Class Venus In Situ Mission

Venus, as part of the inner triad with Earth and Mars, represents an important exploration target if we want to learn more about solar system formation and evolution. Comparative planetology could also elucidate the differences between the past, present, and future of these three planets, and can help with the characterization of potential habitable zones in our solar system and, by extension, extrasolar systems. A long lived in situ Venus mission concept, called the Venus Mobile Explorer, was prominently featured in NASA's 2006 SSE Roadmap and supported in the community White Paper by the Venus Exploration Analysis Group (VEXAG). Long-lived in situ missions are expected to belong to the largest (Flagship) mission class, which would require both enabling and enhancing technologies beside mission architecture options. Furthermore, extreme environment mitigation technologies for Venus are considered long lead development items and are expected to require technology development through a dedicated program. To better understand programmatic and technology needs and the motivating science behind them, in this fiscal year (FY08) NASA is funding a Venus Flaghip class mission study, based on key science and technology drivers identified by a NASA appointed Venus Science and Technology Definition Team (STDT). These mission drivers are then assembled around a suitable mission architecture to further refine technology and cost elements. In this paper we will discuss the connection between the final mission architecture and the connected technology drivers from this NASA funded study, which - if funded - could enable a future Flagship class Venus mission and potentially drive a proposed Venus technology development program.

Balint, Tibor S.↗
NASA NTRS2017

EVA Systems Technology Gaps and Priorities 2017

Performance of Extra-Vehicular Activities (EVA) has been and will continue to be a critical capability for human space flight. Human exploration missions beyond LEO will require EVA capability for either contingency or nominal activities to support mission objectives and reduce mission risk. EVA systems encompass a wide array of products across pressure suits, life support systems, EVA tools and unique spacecraft interface hardware (i.e. EVA Translation Paths and EVA Worksites). In a fiscally limited environment with evolving transportation and habitation options, it is paramount that the EVA community's strategic planning and architecture integration products be reviewed and vetted for traceability between the mission needs far into the future to the known technology and knowledge gaps to the current investments across EVA systems. To ascertain EVA technology and knowledge gaps many things need to be brought together, assessed and analyzed. This includes an understanding of the destination environments, various mission concept of operations, current state of the art of EVA systems, EVA operational lessons learned, and reference advanced capabilities. A combined assessment of these inputs should result in well-defined list of gaps. This list can then be prioritized depending on the mission need dates and time scale of the technology or knowledge gap closure plan. This paper will summarize the current state of EVA related technology and knowledge gaps derived from NASA's Exploration EVA Reference Architecture and Operations Concept products. By linking these products and articulating NASA's approach to strategic development for EVA across all credible destinations an EVA could be done in, the identification of these gaps is then used to illustrate the tactical and strategic planning for the EVA technology development portfolio. Finally, this paper illustrates the various "touch points" with other human exploration risk identification areas including human health and performance.

Johnson, Brian J.↗
NASA NTRS2015

Lessons Learned from Optical Payload for Lasercomm Science (OPALS) Mission Operations

This paper provides an overview of Optical Payload for Lasercomm Science (OPALS) activities and lessons learned during mission operations. Activities described cover the periods of commissioning, prime, and extended mission operations, during which primary and secondary mission objectives were achieved for demonstrating space-to-ground optical communications. Lessons learned cover Mission Operations System topics in areas of: architecture verification and validation, staffing, mission support area, workstations, workstation tools, interfaces with support services, supporting ground stations, team training, procedures, flight software upgrades, post-processing tools, and public outreach.

Sindiy, Oleg V.↗
NASA NTRS2019

Aerodynamic Characterization and Improved Testing Methods for the Space Launch System Liftoff and Transition Environment

Low-speed wind tunnel testing for the liftoff and transition environment of the Space Launch System (SLS) was recently completed in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel using 1.75%-scale models of three SLS vehicle configurations. During the liftoff testing, the primary objective was to evaluate the aerodynamic forces and moments on the SLS launch vehicles, as the vehicle, launch tower, and mobile launch platform were subjected to ground winds from all directions at varying heights for the vehicle off the launch pad. Additionally, aerodynamic forces and moments were acquired for all three SLS vehicles during the transition phase from liftoff to ascent that covered a wide range of angles of attack and angles of side slip. Details of the experimental setup including improved testing methods and a stiffer sting-balance system based on lessons learned from previous test entries are presented. Also, a new force measurement technique was applied during the test where two subminiature six-component load cells were installed in each Solid Rocket Booster (SRB) to acquire forces and moments for each SRB separately from the full vehicle forces and moments measured by the main strain gauge balance. Finally, sample results from the experiment are presented including improved overall data coverage, evaluation of the new SRB data, and smoke flow visualization photos.

Chan, David T.↗
NASA NTRSDate not supplied

Runtime Monitoring for Unmanned Aerospace Systems with Neural Network Components

AI components (e.g., Deep Neural Networks) are increasingly used in unmanned Aerospace systems for safety-relevant applications. Rigorous Verification and Validation methods for such components are still in their infancy and thus, monitoring of the AI's behavior during runtime is essential. In this paper, we will present a runtime-monitoring architecture, which combines the advanced statistical analysis framework SYSAI (System Analysis using Statistical AI) with temporal and probabilistic runtime monitoring carried out by R2U2 (Realizable, Responsive, and Unobtrusive Unit). Learned statistical models of complex systems with AI components are produced by the SYSAI framework and provide detailed information to enable the R2U2 runtime monitor to efficiently perform advanced safety and performance checks in nominal and off-nominal conditions. We will present initial results of our tool set and architecture on a case study, a DNN-based autonomous centerline tracking system (ACT).

Yuning He↗
NASA NTRSDate not supplied

Expressing Functionality Using Design Patterns, A Real-World Example

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting tacit knowledge from existing systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. This study enables such a technique by demonstrating the use of architectural theory in expressing tacit knowledge from existing systems and presenting that knowledge according to the model-based view offered by the Functional Architecture of Systems methodology. By validating the functional information expressed by the design pattern, this work solidifies design patterns as a critical component of a technical architecture and proposes a functional architecture viewpoint for describing mined knowledge in a usable, model-based, way suitable for archival in a modern knowledge management system.

Systems Engineering↗
NASA NTRSDate not supplied

Heritage to Flight: The Test Program that Brought an Inflation System Back to Life for LOFTID

Following the success of the Inflatable Reentry Vehicle Experiment-3 (IRVE-3) project, the Terrestrial Hypersonic Inflatable Aerodynamic Decelerator (HIAD) Orbital Reentry (THOR) project was stood up with a “built to print” Inflation System utilizing the IRVE-3 flight spares. When THOR was canceled, this Inflation System had already been tested and assembled, ready for use. Thus, when the Low Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) was spun up, the THOR system seemed like a good candidate to salvage flight ready hardware. There were, however, some key differences in the IRVE/THOR system and the LOFTID one. LOFTID’s HIAD was two times the size of IRVE for starters and would require much higher flows to accommodate inflating such an article within a specific timeframe. With new requirements and a tight schedule, the LOFTID Inflation team set to work to verify flight readiness under LOFTID conditions and eliminate the need to requalify new hardware. LOFTID, utilizing an Inflation System built primarily hardware from IRVE-3 spares, successfully flew in November of 2022. This paper will outline the mission differences, the test campaign used to prepare LOFTID’s Inflation System for flight, and some lessons learned in repurposing older hardware.

Inflation System↗
NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗
NASA NTRS2024

Physiological and Subjective Responses of Pilots during Advanced Air Mobility Flight Testing with Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA’s Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This paper investigates into pilots’ physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The study provides insights into pilots’ workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this study enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

Kevin J. Monk↗
NASA NTRSDate not supplied

Physiological and Subjective Responses of Pilots During Advanced Air Mobility Flight Testing With Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA's Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This presentation focuses on results of pilots' physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The data provides insights into pilots' workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this endeavor enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

air mobility↗
NASA NTRS2024

ENLIGHTEN: Electrical Network Line Inspection Guided by High-Speed Technology & Electromagnetic Navigation

Natural disasters are a major cause of power outages, primarily due to their damage to power line infrastructure. As a result, the current state of power maintenance, with a heavy reliance on manual labor, is in trouble. Its lack of speed and autonomy must be addressed to ensure power security for households and institutions worldwide. This innovative approach explores how High-Speed Unmanned Aerial Vehicle (HSUAV) technology can inspect power line infrastructure rapidly in response to natural disaster scenarios. The HSUAV technology will be accompanied by enhanced sensory equipment including light detection and ranging (LiDAR), thermal imaging, and electromagnetic field (EMF) navigation. It would also be supplemented by innovative forms of machine learning models and recharging systems to efficiently detect anomalies in power line infrastructure. The proposed system, ENLIGHTEN, can help restore power in affected communities after a devastating natural disaster, greatly improving relief efforts. In the future, ENLIGHTEN can be expanded beyond the United States and shared worldwide, effectively mitigating the consequences on a larger scale.

UAV systems, Power line management, Natural disast↗