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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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NASA NTRSDate not supplied

Precision Fermentation: Relieving the Crabtree Effect in S. cerevisiae through Genetic Engineering

Nutrient degradation in the current NASA pre-packaged food system poses significant challenges to crew health in missions beyond low earth orbit as certain vitamins and nutrients have been shown to degrade during extended storage, and resupply and fresh food items cannot be readily provided. BioNutrients(BN) uses synthetic biology in an effort to supplement the NASA food system by delivering in situ production of nutrients and therapeutics produced by genetically engineered microbes. One yeast S. cerevisiae strain utilized in BN is genetically modified to produce two types of antioxidants: zeaxanthin and β-carotene. Zeaxanthin is a non-provitamin A carotenoid and has been implicated in eye health and vision performance, and β-carotene is a provitamin A carotenoid. Microbial production of high-value nutrients can aid our understanding and address the issue of nutrient degradation and loss in the NASA food system. Although S. cerevisiae is an excellent candidate for a long shelf life, on-demand nutrient production system, its space applications are limited by the Crabtree effect where respiration is shut down in favor of fermentative metabolism when glucose is present in high concentrations, causing the release of unwanted ethanol. Current and likely future life support systems are negatively impacted by ethanol and ethanol release is strictly limited.To improve feasibility of using S. cerevisiae, we explored ways to reduce the Crabtree effect, thereby limiting its ethanol production. Specifically, we used CRISPR-cas9 to genetically modify various genes in this pathway

E Zaroff↗
DOE OSTI2026

MATEY: multiscale adaptive transformer models for spatiotemporal physical systems

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.

adaptive tokenization↗
DOE OSTI2022

The Net Zero World Initiative’s Preliminary Analysis of Decarbonization Pathways for Five Countries

Under the Net Zero World Initiative, the United States is mobilizing the capabilities of nine U.S. government agencies, led by the U.S. Department of Energy (DOE), to partner with philanthropies and multiple countries to cocreate and implement tailored technical and investment pathways to accelerate the decarbonization of global energy systems. In addition, 10 of the DOE national laboratories have built a consortium housed in the Net Zero World Action Center to implement this vision by providing the deep analysis and modeling required to carry out the vision. As a whole-of-government program, the Net Zero World Initiative partners with countries committed to raising their climate ambitions by creating and implementing highly tailored, actionable technical and investment strategies that put a net-zero world within reach. The initiative enables country partners to harness the convening power and technical expertise of U.S. agencies and laboratories, international industry, and technical institutions while providing the United States an opportunity to learn from and deepen U.S. technical cooperation with key countries. This report is the first of a series, with future Phase II work being informed by ongoing consultations with the partner countries to address country pathway analysis priorities. This future work will likely include evaluating detailed technological, policy, and investment options for key sectors and for energy systems holistically. This analysis may examine in greater detail the economic and social benefits of net-zero energy transitions, including quality jobs and health outcomes, the impacts of price and supply volatility on energy investments and decisions, the risk of stranded assets, and related issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Sensorium: commissioning abundant sensors with augmented reality and QR codes

In the future, it will be possible to build high-quality models of building interiors based on data from a dense fleet of sensors reporting on air volumes much smaller than a room or zone. To enable such models, we are creating technologies that allow a fleet of sensors to be commissioned quickly at low cost. Our sensor commissioning process builds a 3D model of each building interior that includes sensor positions and sensor networking information such as sensor MAC addresses. It employs multiple technologies, including augmented reality, LiDAR, QR codes, and computer vision. Sensors can be commissioned at more than 10x the speed and at less than one tenth the cost of traditional approaches.

Bier, Eric A.↗
NASA NTRS2000

A strategic vision for telemedicine and medical informatics in space flight

This Workshop was designed to assist in the ongoing development and application of telemedicine and medical informatics to support extended space flight. Participants included specialists in telemedicine and medical/health informatics (terrestrial and space) medicine from NASA, federal agencies, academic centers, and research and development institutions located in the United States and several other countries. The participants in the working groups developed vision statements, requirements, approaches, and recommendations pertaining to developing and implementing a strategy pertaining to telemedicine and medical informatics. Although some of the conclusions and recommendations reflect ongoing work at NASA, others provided new insight and direction that may require a reprioritization of current NASA efforts in telemedicine and medical informatics. This, however, was the goal of the Workshop. NASA is seeking other perspectives and views from leading practitioners in the fields of telemedicine and medical informatics to invigorate an essential and high-priority component of the International Space Station and future extended exploration missions. Subsequent workshops will further define and refine the general findings and recommendations achieved here. NASA's ultimate aim is to build a sound telemedicine and medical informatics operational system to provide the best medical care available for astronauts going to Mars and beyond.

Aerospace Medicine/organization & administration↗
NASA NTRS2019

Persistent Assets in Zero-G and on Planetary Surfaces: Enabled by Modular Technology and Robotic Operations

Space operations are on the cusp of a revolutionary new operational paradigm that leverages modular systems and recurring robotic visits to “Persistent Assets” enabling asset maintenance, repair, and enhancement. A “Persistent Asset” is defined here as any zero-g or planetary surface system that benefits from in-space assembly (ISA) or multiple visits for servicing, repairs, and upgrades. This term is an extension of the term “Persistent Platform” used by Ms. Pam Melroy at the Defense Advanced Research Projects Agency to describe the vision of the Agency for a geosynchronous Earth orbiting platform. In this paper, the term “Persistent Asset” is introduced to encompass not only zero-g systems; such as telecommunication platforms, Earth observing science platform, Department of Defense platforms, and scientific telescope systems, but also planetary surface systems that support missions such as human outposts, science stations, and in-situ resource utilization systems. In contrast to the current state of the art, where space systems are typically launched as a single unit and operated without any further physical intervention after launch; future systems will be maintained, enhanced and reconfigured in-situ as new technology becomes available or mission needs change. Visits to the persistent asset can be regularly scheduled or dictated by funding constraints enabling a pay-as-you-go approach, which is largely independent of time-constraints and able to exploit launches of opportunity. In this paper, historical in-space assembly activities which relied heavily on astronaut extra-vehicular activity will be reviewed as well as early robotic assembly activities. These approaches will be contrasted with emerging modular approaches supporting realization of a new Persistent Asset operational paradigm. The paper will define attributes of the Persistent Asset paradigm and illustrate advantages by applying the paradigm to two relevant applications: 1) a large space telescope backing structure and 2) the backbone structure for a solar electric transport vehicle. Finally, recently developed unique Persistent Asset elements (modules, and interface approaches) will be described.

Doggett, William R.↗
NASA NTRS2018

Persistent Assets in Zero-G and on Planetary Surfaces: Enabled by Modular Technology and Robotic Operations

Space operations are on the cusp of a revolutionary new operational paradigm that leverages modular systems and recurring robotic visits to “Persistent Assets” enabling asset maintenance, repair, and enhancement. A “Persistent Asset” is defined here as any zero-g or planetary surface system that benefits from in-space assembly (ISA) or multiple visits for servicing, repairs, and upgrades. This term is an extension of the term “Persistent Platform” used by Ms. Pam Melroy at the Defense Advanced Research Projects Agency to describe the vision of the Agency for a geosynchronous Earth orbiting platform. In this paper, the term “Persistent Asset” is introduced to encompass not only zero-g systems; such as telecommunication platforms, Earth observing science platform, Department of Defense platforms, and scientific telescope systems, but also planetary surface systems that support missions such as human outposts, science stations, and in-situ resource utilization systems. In contrast to the current state of the art, where space systems are typically launched as a single unit and operated without any further physical intervention after launch; future systems will be maintained, enhanced and reconfigured in-situ as new technology becomes available or mission needs change. Visits to the persistent asset can be regularly scheduled or dictated by funding constraints enabling a pay-as-you-go approach, which is largely independent of time-constraints and able to exploit launches of opportunity. In this paper, historical in-space assembly activities which relied heavily on astronaut extra-vehicular activity will be reviewed as well as early robotic assembly activities. These approaches will be contrasted with emerging modular approaches supporting realization of a new Persistent Asset operational paradigm. The paper will define attributes of the Persistent Asset paradigm and illustrate advantages by applying the paradigm to two relevant applications: 1) a large space telescope backing structure and 2) the backbone structure for a solar electric transport vehicle. Finally, recently developed unique Persistent Asset elements (modules, and interface approaches) will be described.

William R Doggett↗
DOE OSTI2024

Soybean genomics research community strategic plan: A vision for 2024–2028

Abstract This strategic plan summarizes the major accomplishments achieved in the last quinquennial by the soybean [Glycine max(L.) Merr.] genetics and genomics research community and outlines key priorities for the next 5 years (2024–2028). This work is the result of deliberations among over 50 soybean researchers during a 2‐day workshop in St Louis, MO, USA, at the end of 2022. The plan is divided into seven traditional areas/disciplines: Breeding, Biotic Interactions, Physiology and Abiotic Stress, Functional Genomics, Biotechnology, Genomic Resources and Datasets, and Computational Resources. One additional section was added, Training the Next Generation of Soybean Researchers, when it was identified as a pressing issue during the workshop. This installment of the soybean genomics strategic plan provides a snapshot of recent progress while looking at future goals that will improve resources and enable innovation among the community of basic and applied soybean researchers. We hope that this work will inform our community and increase support for soybean research.

Genetics & Heredity↗
NASA NTRS2012

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

Wolf, Michael↗
NASA NTRS2008

Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS)

One of the strategic goals of the U.S. National Aeronautics and Space Administration (NASA) is to "Develop a balanced overall program of science, exploration, and aeronautics consistent with the redirection of the human spaceflight program to focus on exploration". An important sub-goal of this goal is to "Study Earth from space to advance scientific understanding and meet societal needs." NASA meets this subgoal in partnership with other U.S. agencies and international organizations through its Earth science program. A major component of NASA s Earth science program is the Earth Observing System (EOS). The EOS program was started in 1990 with the primary purpose of modeling global climate change. This program consists of a set of space-borne instruments, science teams, and a data system. The instruments are designed to obtain highly accurate, frequent and global measurements of geophysical properties of land, oceans and atmosphere. The science teams are responsible for designing the instruments as well as scientific algorithms to derive information from the instrument measurements. The data system, called the EOS Data and Information System (EOSDIS), produces data products using those algorithms as well as archives and distributes such products. The first of the EOS instruments were launched in November 1997 on the Japanese satellite called the Tropical Rainfall Measuring Mission (TRMM) and the last, on the U.S. satellite Aura, were launched in July 2004. The instrument science teams have been active since the inception of the program in 1990 and have participation from Brazil, Canada, France, Japan, Netherlands, United Kingdom and U.S. The development of EOSDIS was initiated in 1990, and this data system has been serving the user community since 1994. The purpose of this chapter is to discuss the history and evolution of EOSDIS since its beginnings to the present and indicate how it continues to evolve into the future. this chapter is organized as follows. Sect. 7.2 provides a discussion of EOSDIS, its elements and their functions. Sect. 7.3 provides details regarding the move towards more distributed systems for supporting both the core and community needs to be served by NASA Earth science data systems. Sect. 7.4 discusses the use of standards and interfaces and their importance in EOSDIS. Sect. 7.5 provides details about the EOSDIS Evolution Study. Sect. 7.6 presents the implementation of the EOSDIS Evolution plan. Sect. 7.7 briefly outlines the progress that the implementation has made towards the 2015 Vision, followed by a summary in Sect. 7.8.

Ramapriyan, Hampapuram K.↗
NASA NTRS2007

Jet Propulsion Laboratory: Annual Report 2006

Nothing is as gratifying in space exploration as when we are surprised by the unexpected. Much of our work progresses in an orderly way, from concept to plan to creation to finding. But now and then we are caught off-guard by something startlingly new, and it is these moments that make our hearts race and leave us with many of our most compelling memories. And 2006 was an exceptional year for the unforeseen. One of our orbiters shocked many with stark proof that liquid water, the seemingly long-gone force that reshaped so much of the scenery of Mars, still flows there today,at least in occasional bursts. Another spacecraft caught us by surprise with photos of Yellowstone-like geysers on one of Saturn's seemingly nondescript moons, Enceladus. A spaceborne observatory created to plumb the life histories of stars and galaxies showed off a completely unexpected talent when it revealed the day and night faces of a fire and ice planet far beyond our solar system 40 light-years away. A newly launched Earth observer revealed that the clouds that decorate our own planet are not what we thought them to be in many ways. Of course, not all of the high points of the year arrived on our doorstep in such unexpected ways. There was also great drama when missions came off exactly as planned, such as when Stardust's sample return capsule made a flawless landing in the Utah desert, bringing home samples of cometary and interstellar dust. Mars Reconnaissance Orbiter slipped into orbit around the red planet exactly as planned. Numerous other missions and technology programs likewise made great achievements during the year. In all, 17 spacecraft and six instruments were stationed across the solar system, studying our own world, other planets, comets and the deeper universe. All of these achievements were enabled by many teams and systems at the Laboratory. The Deep Space Network of communications complexes across three continents supported all of NASA's solar system missions, and several from our international partners, while looking ahead to reinvent itself for the future. Technologists were at work creating innovations both for NASA missions and terrestrial uses. JPL's community of scientific researchers was equally busy coordinating the science activities of our missions and pursuing independent investigations. None of this would be possible without the support of business and administrative teams dedicated to making the Laboratory's institutional environment as world-class as its technical face, or the public engagement specialists who bring the experience of space to the country's diverse publics. All of our missions in one way or another support our nation's Vision for Space Exploration, which envisages a gradually widening robotic and human presence across the solar system in the years ahead.

National Aeronautics and Space Administration (NAS↗
NASA NTRS2009

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Ivanov, Tonislav↗
NASA NTRS2004

Investigation of Injector Slot Geometry on Curved-Diffuser Aerodynamic Performance

The Compressor Branch vision is to be recognized as world-class leaders in research for fluid mechanics of compressors. Its mission is to conduct research and develop technology to advance the state of the art of compressors and transfer new technology to U.S. industries. Maintain partnerships with U.S. industries, universities, and other government organizations. Maintain a balance between customers focused and long range research. Flow control comprises enabling technologies to meet compression system performance requirements driven by emissions and fuel reduction goals (e.g., in UEET), missions (e.g., access-to-space), aerodynamically aggressive vehicle configurations (e.g., UAV and future blended wing body configurations with highly distorted inlets), and cost goals (e.g., in VAATE). The compression system requirements include increased efficiency, power-to-weight, and adaptability (i.e., robustness in terms of wide operability, distortion tolerance, and engine system health and reliability). The compressor flow control task comprises efforts to develop, demonstrate, and transfer adaptive flow control technology to industry to increase aerodynamic loading at current blade row loss levels, to enable adaptive1 y wide operability, and to develop plant models for adaptive compression systems. In this context, flow control is the controlled modification of a flow field by a deliberate means beyond the natural (uncontrolled) shaping of the solid surfaces that define the principal flow path. The objective of the compressor flow control task is to develop and apply techniques that control circulation, aerodynamic blockage, and entropy production in order to enhance the performance and operability of compression systems for advanced aero-propulsion applications. This summer I would be working with a curved-diffuser because it simulates what happens with flow in the stator blades in the compressor. With this experiment I will be doing some data analysis and parametric study of the injector slot geometries to get the best aerodynamic performance of it. This includes some data reduction, redesign and fast prototyping of the injector nozzle.

Silva, Odlanier↗
NASA NTRS2006

NASA Stennis Space Center Integrated System Health Management Test Bed and Development Capabilities

Integrated System Health Management (ISHM) is a capability that focuses on determining the condition (health) of every element in a complex System (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK)-not just data-to control systems for safe and effective operation. This capability is currently done by large teams of people, primarily from ground, but needs to be embedded on-board systems to a higher degree to enable NASA's new Exploration Mission (long term travel and stay in space), while increasing safety and decreasing life cycle costs of spacecraft (vehicles; platforms; bases or outposts; and ground test, launch, and processing operations). The topics related to this capability include: 1) ISHM Related News Articles; 2) ISHM Vision For Exploration; 3) Layers Representing How ISHM is Currently Performed; 4) ISHM Testbeds & Prototypes at NASA SSC; 5) ISHM Functional Capability Level (FCL); 6) ISHM Functional Capability Level (FCL) and Technology Readiness Level (TRL); 7) Core Elements: Capabilities Needed; 8) Core Elements; 9) Open Systems Architecture for Condition-Based Maintenance (OSA-CBM); 10) Core Elements: Architecture, taxonomy, and ontology (ATO) for DIaK management; 11) Core Elements: ATO for DIaK Management; 12) ISHM Architecture Physical Implementation; 13) Core Elements: Standards; 14) Systematic Implementation; 15) Sketch of Work Phasing; 16) Interrelationship Between Traditional Avionics Systems, Time Critical ISHM and Advanced ISHM; 17) Testbeds and On-Board ISHM; 18) Testbed Requirements: RETS AND ISS; 19) Sustainable Development and Validation Process; 20) Development of on-board ISHM; 21) Taxonomy/Ontology of Object Oriented Implementation; 22) ISHM Capability on the E1 Test Stand Hydraulic System; 23) Define Relationships to Embed Intelligence; 24) Intelligent Elements Physical and Virtual; 25) ISHM Testbeds and Prototypes at SSC Current Implementations; 26) Trailer-Mounted RETS; 27) Modeling and Simulation; 28) Summary ISHM Testbed Environments; 29) Data Mining - ARC; 30) Transitioning ISHM to Support NASA Missions; 31) Feature Detection Routines; 32) Sample Features Detected in SSC Test Stand Data; and 33) Health Assessment Database (DIaK Repository).

Figueroa, Fernando↗
NASA NTRS2005

NASA Ames Arc Jets and Range, Capabilities for Planetary Entry

NASA is pursuing innovative technologies and concepts as part of America's Vision for Space Exploration. The rapidly emerging field of nanotechnology has led to new concepts for multipurpose shields to prevent catastrophic loss of vehicles and crew against the triple threats of aeroheating during atmospheric entry, radiation (Solar and galactic cosmic rays) and Micrometorid/Orbital Debris (MMOD) strikes. One proposed concept is the Thermal Radiation Impact Protection System (TRIPS) using carbon nanotubes, hydrogenated carbon nanotubes, and ceramic coatings as a multi-use TPS. The Thermophysics Facilities Branch of the Space Technology Division at NASA Ames Research Center provides testing services for the development and validation of the present and future concepts being developed by NASA and national and International research firms. The Branch operates two key facilities - the Range Complex and the Arc Jets. The Ranges include both the Ames Vertical Gun Range (AVGR) and the Hypervelocity Free Flight (HFF) gas guns best suited for MMOD investigations. Test coupons can be installed in the AVGR or HFF and subjected to particle impacts from glass or metal particles from micron to _ inch (6.35-mm) diameters and at velocities from 5 to 8 kilometers per second. The facility can record high-speed data on film and provide damage assessment for analysis by the Principle Investigator or Ames personnel. Damaged articles can be installed in the Arc Jet facility for further testing to quantify the effects of damage on the heat shield s performance upon entry into atmospheric environments.

Fretter, Ernest F.↗
NASA NTRS2010

Lunar Reconnaissance Orbiter (LRO) Thruster Control Mode Design and Flight Experience

National Aeronautics and Space Administration s (NASA) Goddard Space Flight Center (GSFC) in Greenbelt, MD, designed, built, tested, and launched the Lunar Reconnaissance Orbiter (LRO) from Cape Canaveral Air Force Station on June 18, 2009. The LRO spacecraft is the first operational spacecraft designed to support NASA s return to the Moon, as part of the Vision for Space Exploration. LRO was launched aboard an Atlas V 401 launch vehicle into a direct insertion trajectory to the Moon. Twenty-four hours after separation the propulsion system was used to perform a mid-course correction maneuver. Four days after the mid-course correction a series of propulsion maneuvers were executed to insert LRO into its commissioning orbit. The commission period lasted eighty days and this followed by a second set of thruster maneuvers that inserted LRO into its mission orbit. To date, the spacecraft has been gathering invaluable data in support of human s future return to the moon. The LRO Attitude Control Systems (ACS) contains two thruster based control modes: Delta-H and Delta-V. The design of the two controllers are similar in that they are both used for 3-axis control of the spacecraft with the Delta-H controller used for momentum management and the Delta-V controller used for orbit adjust and maintenance maneuvers. In addition to the nominal purpose of the thruster modes, the Delta-H controller also has the added capability of performing a large angle slew maneuver. A suite of ACS components are used by the thruster based control modes, for both initialization and control. For initialization purposes, a star tracker or the Kalman Filter solution is used for providing attitude knowledge and upon entrance into the thruster based control modes attitude knowledge is provided via rate propagation using a inertial reference unit (IRU). Rate information for the controller is also supplied by the IRU. Three-axis control of the spacecraft in the thruster modes is provided by eight 5-lbf class attitude control thrusters configured in two sets of four thrusters for redundancy purposes. Four additional 20-lbf class thrusters configured in two sets of two thrusters are used for Lunar Orbit Insertion maneuvers. The propulsion system is one the few systems on-board the LRO spacecraft that has built in redundancy. The Delta-H controller consists of a Proportional-Derivative (PD) controller with a structural filter on the thrusters and a Proportional controller on the reaction wheels. The PD control that employs the thrusters is used for attitude and rate control. The Proportional controller on the reaction wheels is used for commanding the wheels to a new momentum state. The ground commands used for the Delta-H controller are the system momentum vector, reaction wheel momentum, maximum expected command time, and which set of attitude control thrusters to use. The ability to command both the system momentum vector and reaction wheel momentum in the Delta-H controller provides both a capability and an additional source of operator error. Large angle slews via the Delta-H controller is achievable via this commands because these commands are used for the exit mode criteria. Setting these commands to non-consistent values prevents the mode from exiting nominally.

Hsu, Oscar C.↗
NASA NTRSDate not supplied

Developing Methods for Exercise System Kinematic Tracking

BACKGROUND How to quantify the load and forces produced by exercise equipment and their Vibration Isolation and Stabilization (VIS) platforms in-flight is an active area of investigation. Kinematic tracking paired with system modeling can provide insights as well as verification and validation of simulations used for system design and development. Traditional motion capture methods can require significant cost in equipment procurement and crew-time, but newer lessons learned can be leveraged [1]. The VIS systems of current and future exercise hardware on the International Space Station (ISS) such as the Cycle Ergometer with Vibration Isolation System (CEVIS) and the European Enhanced Exploration Exercise Device (E4D) are not currently outfitted with IMUs or similar measurement devices. Video-based methods would enable use of multi-purpose, crew-familiar flight equipment. An initial exploration of video-based solutions was performed utilizing 2-camera video from crew cycling on Teal-CEVIS on the ISS. METHODS AND RESULTS Our group has scoped a variety of video-based object tracking methods. To date, we have primarily investigated computer vision toolkits such as open CV. Techniques explored include key-point detection, background subtraction, region-of interest tracking, color-based tracking, tag masking and tracking, and corner detection. Although object-tracking and 6D pose estimation is a rich field, space applications are a unique problem that are challenging for existing software and toolkits. The majority of the existing object-tracking applications involve vehicles/pedestrians and household objects with simple backgrounds. We have identified the following features which pose particular challenges for on-station exercise equipment tracking: 1. Busy and visually cluttered background 2. Low-textured tracking object with relatively small motions 3. Occlusions and motion by human subject and loose, floating objects 4. Limited number of video cameras with no fixed global references 5. Limited ability to add tags, markers, or visual references to the tracking object 6. Lack of training data for Machine Learning (ML) algorithms CONCLUSION We will summarize the efficacy of techniques tested for a ground mock-trial and the on-station exercise trial. It is likely that human-in-loop feedback or a conglomerate of methods is required. ML-based methods, like those implemented for human body tracking [2], may still be a viable option, but more training data and validation is needed.

L Nilsson↗