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NASA NTRS2006

Linking Yellowstone Research to Mars Exploration

Yellowstone's hydrothermal features and their associated communities of thermophiles are studied by scientists who are searching for evidence of life on other planets. The connection is extreme environments. If life originated in the extreme conditions thought to have been widespread on ancient Earth, it may well have developed on other planets and it might still exist today. The chemosynthetic microbes that thrive in some of Yellowstone s hot springs do so by metabolizing inorganic chemicals, a source of energy that does not require sunlight. Such chemical energy sources provide the most likely habitable niches for life on Mars or on the moons of Jupiter-Ganymede, Europa, and Callisto-where uninhabitable surface conditions preclude photosynthesis. Chemical energy sources, along with extensive groundwater systems (such as on Mars) or oceans beneath icy crusts (such as Jupiter's moons) could provide habitats for life. The study of stromatolites on Earth may also be applied to the search for life on other planets. If stromatolites are eventually found in the rocks of Mars or on other planets, we will have proven that life once existed elsewhere in the universe. Yellowstone National Park will continue to be an important site for studies at the physical and chemical limits of survival. These studies will give scientists a better understanding of the conditions that give rise to and support life, and they will learn how to recognize signatures of life in ancient rocks and on distant planets.

DesMarais, David J.↗

NASA NTRS2005

NASA Hydrogen Peroxide Propellant Hazards Technical Manual

The Fire, Explosion, Compatibility and Safety Hazards of Hydrogen Peroxide NASA technical manual was developed at the NASA Johnson Space Center White Sands Test Facility. NASA Technical Memorandum TM-2004-213151 covers topics concerning high concentration hydrogen peroxide including fire and explosion hazards, material and fluid reactivity, materials selection information, personnel and environmental hazards, physical and chemical properties, analytical spectroscopy, specifications, analytical methods, and material compatibility data. A summary of hydrogen peroxide-related accidents, incidents, dose calls, mishaps and lessons learned is included. The manual draws from art extensive literature base and includes recent applicable regulatory compliance documentation. The manual may be obtained by United States government agencies from NASA Johnson Space Center and used as a reference source for hazards and safe handling of hydrogen peroxide.

Baker, David L.↗

NASA NTRS2009

Medical Challenges of the First Canadian Long-Duration Space Mission: Lessons Learned

In 2008, Dr. Thirsk was assigned to the crew of Expedition 20/21. This Expedition represented a milestone for the Canadian Space Program since it was the first time that a Canadian would take part in a long-duration mission. Robert Thirsk had the privilege of expanding the boundaries of space exploration by living and working on board the International Space Station for six months. The launch took place on May 27, 2009 aboard a Soyuz rocket from the Cosmodrome in Baikonur, Kazakhstan. This abstract was written before Dr. Thirsk returned to Kazakhstan. Objective: To gather all medically relevant data needed to support the first Canadian long-duration mission in space, and process it to derive lessons learned for presentation and for public disclosure. Methods: Sources of data used for analysis for Expedition 20 on International Space Station included flight selection data, maintenance annual physicals, Flight Medicine Clinic visits, parabolic flight experiments, preflight exams and baseline data collections, daily in-flight exercise countermeasure and science payloads, weekly periodic fitness, nutrition, radiation and payload assessments, postflight medical exams, rehabilitation, and science activities.

Hamilton, Douglas R.↗

NASA NTRS2010

Orion Flight Performance Design Trades

A significant portion of the Orion pre-PDR design effort has focused on balancing mass with performance. High level performance metrics include abort success rates, lunar surface coverage, landing accuracy and touchdown loads. These metrics may be converted to parameters that affect mass, such as ballast for stabilizing the abort vehicle, propellant to achieve increased lunar coverage or extended missions, or ballast to increase the lift-to-drag ratio to improve entry and landing performance. The Orion Flight Dynamics team was tasked to perform analyses to evaluate many of these trades. These analyses not only provide insight into the physics of each particular trade but, in aggregate, they illustrate the processes used by Orion to balance performance and mass margins, and thereby make design decisions. Lessons learned can be gleaned from a review of these studies which will be useful to other spacecraft system designers. These lessons fall into several categories, including: appropriate application of Monte Carlo analysis in design trades, managing margin in a highly mass-constrained environment, and the use of requirements to balance margin between subsystems and components. This paper provides a review of some of the trades and analyses conducted by the Flight Dynamics team, as well as systems engineering lessons learned.

Jackson, Mark C.↗

NASA NTRS1976

NASA Tech Briefs, Summer 1976

Topics covered include: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Life Sciences; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences. Also included are; NASA TU Services: Technology Utilization services that can assist you in learning about and applying NASA technology; and New Product Ideas: A summary of selected innovations of value to manufacturers for the development of new products.

Source record↗

NASA NTRS2021

Exploration Mission Tasks: A Technical Manual

This technical manual is an abridgement of the Generalizable Skills and Knowledge for Exploration Missions (NASA/CR-2018-22045) report (Stuster et al., 2019), describing research conducted under Cooperative Agreement 80NSSC18K0042 for the Human Factors and Behavioral Performance Element, Human Research Program, located at the National Aeronautics and Space Administration’s (NASA) Johnson Space Center. The research identified tasks that will be conducted by human crew during an expedition to Mars, and the abilities, skills, and knowledge that will be required of crew members. The 3-year study uses research methods that were developed to analyze the work performed by a variety of civilian and military occupational specialties and is consistent with Human Factors methods. The work began by developing a comprehensive inventory of 1,125 tasks that are likely to be performed during the 12 phases of the first human expeditions to Mars, from launch to landing 30 months later. Sixty subject matter experts (SMEs) rated expedition tasks in terms of (likely) frequency of performance, difficulty to learn, and importance to mission success; a fourth metric (criticality), was derived by summing the mean ratings of the three dimensions. Seventy-two SMEs placed the physical, cognitive, and social abilities necessary to perform the tasks in order of importance for specialist domains identified by the task analysis. The research team then identified: 1) Abilities, skills, and knowledge that can be retained and generalized across tasks and 2) Implications for crew size and composition. Study results also led to recommendations concerning equipment, habitats, and procedures for exploration-class space missions. Note: The full-mission task inventory was developed during a comprehensive review of documentation and concepts of operations. It was understood by the study team that the tasks were based on currently available information, and that the tools, equipment, propulsion methods, and/or other aspects of actual human expeditions to Mars might be different from those described here, as a consequence of technological development and evolving Mars Design Reference Missions. The purpose and scope of this technical manual is to present the core, actionable information that resulted from this research. The abridged format is intended to address the needs of development and research teams to quickly access, discern, and use the information in the course of their exploration-related work.

Brandin Munson↗

NASA NTRS1988

Lessons learned in ground processing scientific and applications payloads at Kennedy Space Center

The payload ground processing of experiments which flew on the Shuttle is examined, including studies of atmosphere and earth observation, life science, advance technology, materials science, plasma physics, astrophysics and solar physics, astronomy, navigation and communication, and fluid mechanics. The process of designing and development payloads, the integration of instruments, carrier interface verification, and late servicing at the launch pad are discussed. The problems of cost, red tape, contamination, safety, and propriety protection are considered.

Elfrey, Priscilla↗

NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗

NASA NTRS1992

Tests characterizing bioprocessor hardware for analytical modeling

The tests outlined in this paper were used to characterize the hardware components of the Salad Machine, a small NASA-developed bioprocessor. The data from these tests are presented, and the methods by which this data can be integrated into system mathematical models are briefly discussed. The subsystems and physical processes discussed include the lighting system, the air loop (condensing heat exchanger and the blower), heat transfer to the surroundings, and leakage. Through this effort it was learned that in the development of a test protocol, care should be taken to order the tests such that environmental parameters, particularly humidity, require as few large adjustments as possible. Sensor calibration and installation take a substantial amount of time, which should be built into the test schedule. Two properties were particularly hard to quantify: the air flow rate and the energy from the lighting system entering into the growth volume. Flow rate can be measured using the appropriate device for the system configuration and airflow. Lighting system radiation level was measured using three methods. The results of these methods varied substantially, putting off conclusive quantification of this value.

Gustavino, S.↗

NASA NTRS2007

The X-43A Flight Research Program: Lessons Learned on the Road to Mach 10

During an aerospace engineer's undergraduate studies, he or she will attend classes in aerodynamics, thermodynamics, structures, stability and control, dynamics, design, propulsion, and computer science, along with the related courses in mathematics, physics, statistics, and chemistry required to understand the material. Upon graduation, the new engineer will have acquired a basic knowledge of how to build an aerospace vehicle. What only comes through experience, however, is the understanding of the inevitable imperfect process through which an aerospace vehicle is built. This is the adventure of turning a basic concept into functional hardware. Engineers working on a project must often deal with ambiguous situations. They are routinely asked by management to provide risk assessments of a project, yet even after careful analysis uncertainties remain. The project must be accomplished within finite limits of time and money. The question an engineer answers is whether the solution to potential problem is worth the cost and schedule delay, or if the solution might actually be worse than the problem it is meant to solve. Review protocols are established to ensure that an unknown has not been overlooked. But these cannot protect against an unknown unknown. Examples of these situations can be found in the history of the X-43A Hyper-X (Hypersonic Experiment) program. In this NASA project, a supersonic combustion ramjet (scramjet) engine was flight tested on a subscale vehicle. The X-43A Hyper-X Research Vehicle (HXRV) was launched from a B-52B mothership, then boosted to the test speed by a modified Pegasus rocket first stage, called the Hyper-X Launch Vehicle (HXLV). Once at the proper speed and altitude, the X-43A separated from the booster, stabilized itself, and then the engine test began. Although wind-tunnel scramjet engine tests had begun in the late 1950s, before the Hyper-X program there had never been an actual in-flight test of such an engine integrated with an appropriate airframe. Thus, while the scramjet had successfully operated in the artificial airflow of wind tunnels, the concept had yet to be proven in real air. These conditions meant changes in density and temperature, as well as changes in angle of attack and sideslip of a free-flying vehicle. A wind tunnel is limited in its ability to simulate these subtle factures, which have a major impact on almost any vehicle, but especially that of a scramjet's performance. The Hyper-X project was to provide a real-world benchmark of the ground test data. The full scale X-43A engine would be operated in the wind tunnel, and then flown, and the data from its operation would then be compared with projections. If these matched, the wind tunnel data would be considered a reliable design tool for future scramjet. If there were significant differences, the reasons for these would have to be identified. Until such information was available, scramjets would lack the technological maturity to be considered for future space launch or high-speed atmospheric flight vehicles.

Peebles, Curtis↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Recession tracking↗

NASA NTRS2024

An On-Board Off-Board Framework for Online Replanning: Applied to UAVs in Urban Environments

Autonomous systems are being used in a multitude of areas at an increasing rate and require a high level of adaptivity and intelligence to operate safely, especially under faulty conditions. This paper introduces a novel genetic algorithm tailored for UAV trajectory replanning, with an improved execution time via search space reduction based on the operating conditions of the UAV and its remaining mission. A unique characteristic of the replanning agent is its fast-start and adaptive properties, pre-seeding candidates with partial solutions and dynamically tuning elitism, crossover, and mutation rates in correspondence to the average fitness and diversity of the population. A population restart mechanism and early stopping mechanism are evaluated as well to assess their effect on solution quality and runtime. Previous work on genetic algorithms for UAV replanning were conducted with short trajectories in a small state space. Our UAV operates in a 56,000 square meter simulated urban environment, with static obstacles and a total of 53 possible waypoints. The agent increases the safety and reliability of UAV autonomy when operating under faulty conditions and when replanning is required.

Machine Learning↗

NASA NTRSDate not supplied

Surrogate Modeling of Subgrid Turbulent Transport Based on 3D Radiative Hydrodynamic Simulations of the Quiet Sun

Turbulent Transport: Plays a critical role in astrophysical plasmas, such as the solar interior, spanning multiple scales and challenging traditional modeling approaches. Objective: Develop machine learning (ML) models—MLP and CNN—to predict subgrid Reynolds stress tensors from StellarBox 3D simulations of the solar atmosphere. Benchmarking: Compare ML-driven models against physics-based Gradient and Smagorinsky approaches.

SMD↗

NASA NTRS2011

Mission X: Train Like an Astronaut. International Fitness Challenge

The Mission X, Train like an Astronaut, pilot project was a 2-year effort directed by the International Life Science Working Group. The pilot was funded by the Human Research Program and was lead by the Human Research Program Education and Outreach (HRPEO) project and supported by a group of space agencies providing in-kind resources. The aim was to identify an international educational outreach concept that would promote a life science topic utilizing the education and outreach expertise of the various space agencies working on the utilization of the International Space Station. This in turn serves as an inspiration for the younger generation to aspire to go further in school, and provides insight into the capability of a participating country to ensure the effort provided value for their communities and children. The pilot project developed the necessary tools to promote communications between the partners and to use materials and expertise from all the countries? space agencies. The Mission X Website (trainlikeanastronaut.org) provided a single repository for the educational activities as well as a place for the Challenge Teams to provide their progress in the international fitness challenge. It also added to the International flavor as different countries were able to share and learn about what was happening with all those involved in the 6-week challenge period. A point system was utilized to promote constructive, cooperative competition in which 4164 students participated. The points were used to help FitKid, Astro Charlie, "Walk-To-The-Moon". The 18 physical and educational Mission X activities were made available on the Mission X website in seven languages. The Mission X pilot project was considered a success in 1) the design, development, and implementation of the multi-language website, 2) the expansion of healthy lifestyle awareness, and 3) the concept for drawing an international educational community together to highlight global topics in association with human space exploration. Metrics (70 post-event surveys) and lessons learned were collated and assessed. Key Lessons Learned were 1) foster and strengthen partnerships at all levels, 2) enhance and standardize MX Training, and 3) further expansion of activities and methods to teach the importance of fitness and nutrition. Methods used to teach the importance of nutrition and fitness are provided in the report.

Lloyd, Charles↗

NASA NTRS2005

Challenges in Predicting Planetary Granular Mechanics

Through the course of human history, our needs in agriculture, habitat construction, and resource extraction have driven us to gain more experience working with the granular materials of planet Earth than with any other type of substance in nature, with the possible exception being water. Furthermore, throughout the past two centuries we have seen a dramatic and ever growing interest among scientists and engineers to understand and predict both its static and rheological properties. Ironically, however, despite this wealth of experience we still do not have a fundamental understanding of the complex physical phenomena that emerge even as just ordinary sand is shaken, squeezed or poured. As humanity is now reaching outward through the solar system, not only robotic ally but also with our immediate human presence, the need to understand and predict granular mechanics has taken on a new dimension. We must learn to farm, build and mine the regoliths of other planets where the environmental conditions are different than on Earth, and we are rapidly discovering that the effects of these environmental conditions are not trivial. Some of the relevant environmental features include the regolith formation processes throughout a planet's geologic and hydrologic history, the unknown mixtures of volatiles residing within the soil, the relative strength of gravitation, ~d the atm9spheric pressure and its seasonal variations. The need to work with soils outside our terrestrial experience base provides us with both a challenge and an opportunity. The challenge is to learn how to extrapolate our experience into these new planetary conditions, enabling the engineering decisions that are needed right now as we take the next few steps in solar system exploration. The opportunity is to use these new planetary environments as laboratories that will help us to see granular mechanics in new ways, to challenge our assumptions, and to help us finally unravel the elusive physics that lie behind complex granular phenomena. Toward these goals, a workshop was held recently at NASA's John F. Kennedy Space Center, attracting over a hundred scientists and engineers from around the world and from a broad crosssection of scientific and engineering disciplines. This talk will provide an out-briefing from that workshop, communicating some of its early findings in regard to lunar and Martian exploration: (1) the requirements for working with granular materials, (2) the challenges that granular materials will pose, (3) the environmental conditions that affect granular mechanics, (4) instruments and measurements that are needed on the Moon and Mars to support granular material research, and (5) some of the possible research avenues that should be pursued.

Metzger, Philip T.↗

NASA NTRS2018

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc.

Keller, Christoph A.↗