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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Lunar Ice Cube: ongoing development of first generation deep space cubesat mission with compact broadband IR spectrometer

Lunar Ice Cube (LIC) is one of 13 6U cubesats that will be deployed by EM1 in cislunar space. LIC along with Lunar Flashlight and LunaH-Map, all focused on the search for volatiles but with very different payloads, will be the first deep space cubesats designed to address goals for both demonstrating new technologies and collecting scientific data. Effectively, as their developments are occurring in parallel, they are acting as prototypes for future deep space cubesats missions. One useful outcome of this experiment is to evolve a working paradigm for the development and operation of compact, cost-capped, standardized (supporting subsystems) spacecraft to serve the needs of diverse user communities.

Mason, Paul↗
NASA NTRS2021

Measurement Uncertainty Analysis of 6x9 Icing Research Tunnel (IRT)

This paper presents the results of the measurement uncertainty analysis that was performed on the aero-thermal characteristics of the 6- by 9-Foot Icing Research Tunnel (IRT) at the NASA Glenn Research Center. The engineering judgments and statistical methods used to determine the elemental uncertainties are described. Instrumentation uncertainty was quanti ed using MANTUS (Measurement Analysis Tool for Uncertainty in Systems) and instrument speci cation sheets. The Monte Carlo method was used to propagate systematic components of uncertainty in order to quantify the uncertainty of the Variables of Interest (VOI). A detailed description of the Monte Carlo method and the MANTUS tool can be found in the Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel report [1]. Detailed uncertainty results for test section airspeed and temperature as well as other variables of interest are described within this document.

6x9 Icing Research Tunnel↗
NASA NTRS2022

Evidence Report: Risk of Injury and Compromised Performance due to EVA Operations

During future missions to the Moon and Mars, each crewmember will most likely perform up to 24 hours a week of extravehicular activity (EVA) in support of exploration, science, construction, and maintenance tasks. Achieving mission objectives will require EVA systems and operations concepts that maximize human performance and efficiency while minimizing health and safety risks for crewmembers. Currently, over 450 EVAs have been performed in microgravity using the Extravehicular Mobility Unit (EMU). The EMU space suit has enabled the successful assembly and maintenance of the International Space Station (ISS) for over 20 years, as well as deployment of payload experiments, solar arrays, satellite launches and repairs. This EVA work was accomplished at a slower cadence than is expected for Moon and Mars, with ISS crewmembers performing no more than 7 EVAs during a single mission and without any back-to-back EVAs. Despite their success, ISS EVAs have resulted in more injury to EVA crewmembers than may be acceptable for long-duration exploration missions. The Apollo astronauts completed EVA tasks in suits that were designed for their short-duration lunar missions, although suit mobility problems were evident. The more frequent EVAs and more varied EVA tasks that are anticipated during the future longer-duration exploration missions will require EVA suits and systems that are better oriented to human health and performance than those used during the Apollo Program. Many of the problems that were encountered with the Apollo EVA suits (e.g., limited mobility and dexterity, high and aft center of gravity, and other features requiring significant crew compensation) will need to be corrected or mitigated to optimize EVA objectives of exploration missions. It is critical that we understand how EVA system design variables such as suit pressure, weight/mass, center-of-gravity location, joint ranges of motion, and biomedical monitoring, affects the ability of astronauts to perform safe, efficient, and effective EVAs. To achieve this understanding, EVA researchers will need to develop and execute an integrated human testing program across multiple environments. The research will provide objective data that will enable informed design decisions and crewmember standards, thereby ensuring EVA systems that optimize crewmember health, safety, efficiency, and performance. This report describes the risks to crew health, safety, performance, and efficiency caused by EVA operations, and it provides the evidence base to substantiate the importance of the risk.

EVA↗
NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗
NASA NTRSDate not supplied

PPE Electric Propulsion Advancing to CDR: 12kW String

String level testing is a vital part of electric propulsion development. With the Power and Propulsion Element (PPE) spacecraft flying two new models of Hall Thrusters, this work is even more critical. PPE will have both 6kW and 12kW thruster strings onboard, all powered by Maxar power processing units (PPU). Both strings have recently undergone end-to-end string level hot fire testing to verify the ability of all components to work together. This entails operating the thruster with the Xenon Flow Controller (XFC) and PPUs under both nominal and off-nominal conditions to stress the system and determine weak points. Such testing has long been a standard Maxar practice since our first 1.5kW electric propulsion system in 2004. This paper will focus on Phase 1 of the 12kW testing recently completed at NASA Glenn. The primary conclusion of the test effort was that the 12kW string operated nominally and that the PPU and XFC could operate the thruster within specifications.

Electric propulsion↗
NASA NTRSDate not supplied

Soyuz Landing Reconstructions

Introduction: Recent data show a landing-attributed injury rate among Soyuz crew members to be higher than predicted in original certification analysis. New vehicles being developed for NASA are being certified using analytical tools to quantify injury risk. This assessment will provide data to allow the Agency to evaluate whether these tools are able to capture true injury risk and allow a comparison between Soyuz landings and upcoming vehicles. Methods: A finite element model (FEM) of the Soyuz descent module (DM) and its landing environment were developed to recreate Soyuz landing conditions. Occupant breakout models composed of the Soyuz seat and Sokol helmet were then developed to evaluate occupant response. These models included both anthropomorphic test device (ATD) and human body model (HBM) surrogates in three anthropometric sizes. A generalized distribution analysis of the Soyuz landing environment was performed using landing variable distributions from available literature. Results were used to identify trends in vehicle landing acceleration with landing conditions and to compare landing model injury risk predictions with those estimated in vehicle certification analysis. Next, historic Soyuz landings identified as inducing occupant injury during were reconstructed using the developed landing model and estimated landing conditions. Results and Discussion: This assessment demonstrated the capability of certification metrics, which include a combination of vehicle- and occupant-level injury risk analysis, to identify elevated injury risk landing conditions. However, the developed landing load predictions were limited by available vehicle and landing condition data. Additional data, particularly landing acceleration data specific to historic Soyuz landing conditions, would greatly enhance this assessment’s findings.

Preston Greenhalgh↗
NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

A new Exploration Extravehicular Activity Suit (xEVAS) is being designed to replace the current Extravehicular Mobility Unit (EMU) for the National Aeronautics and Space Administration’s (NASA’s) Artemis program to return astronauts to the lunar surface. This new suit will allow for increased range of motion compared the current EMU and Apollo era suits and will have additional features that will enhance the health and safety of exploration. With the design of lunar missions and the xEVAS progressing, it is important to consider possible injuries and injury mechanisms that could occur in the suit. To address these concerns, the suited Injury Modes and Effects Analysis (IMEA) was developed to outline suited injury scenarios and rank them based on risk score. The IMEA documents possible scenarios and underlying mechanisms of injury while wearing an extravehicular activity (EVA) suit. Tasks during lunar surface EVA as well as training events to prepare for lunar missions were considered as history has shown that more suit injuries occur during training than in flight. Each scenario is ranked with a consequence and likelihood scoring based on our current understanding of the suit and Artemis design reference missions to identify high-risk cases that will drive further work in suited injury. Injuries, mechanisms of injury, and mitigation strategies are evaluated within each scenario. The Suited Injury Summit was held on January 5, 2022, to vet the IMEA with external experts. This was an all-day virtual meeting with the suited injury team, ergonomists, suit engineers, safety engineers, the flight operations directorate, flight doctors, astronauts, astronaut strength, conditioning, and rehabilitation specialists (ASCRS), and external subject matter experts (SMEs). External SMEs consisted of surgeons with varying specialties. The intent of this meeting was to walk through the top injury risks identified in the analysis, identify any gaps that were not captured, and discuss mitigations. With participation from all groups, countless lessons-learned came from the Summit meeting. Using the lessons-learned and discussion from the Summit, the top 10 risks have been identified: neutral buoyancy laboratory training, hand/glove injuries, poor suit fit, field training, specific EVA tasks/design of task, boots/ankle injuries, falls from heights, background radiation, repetitive contact, and ambulation/long-distance ambulation. Mitigation steps have also been determined for each of the top risks. The IMEA and documentation of top risks is a living document. Yearly meetings are planned to update the analysis and reevaluate top risks and mitigations. The IMEA is being used to drive work in suited injury, and this work will continue to evolve with IMEA and lunar mission updates.

Tessa Reiber↗