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NESC Technical Bulletin 23-06:Considerations for Software Fault Prevention and Tolerance
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By Space Force
Suicide prevention is a top military priority every day, but takes on even greater focus each September, designated since 2008 as National Suicide Prevention month.
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By Space Force
History was made on Aug. 16, as six Space Force students out of basic military training became the first Guardians to graduate technical training at the U.S. Air Force Honor Guard at Joint Base Anacostia-Bolling.
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Johnson Space Center: ORDEM represents the state of the art in orbital debris models intended for engineering analysis. It is a data-driven model, relying on large quantities of radar, optical, in situ, and laboratory measurement data. When released, it was the first software code to include a model for different orbital debris material densities, population models from low Earth orbit (LEO) all the way to Geosynchronous orbit (GEO), and uncertainties in each debris population.
ORDEM allows users to compute the orbital debris flux on any satellite in Earth orbit. This allows satellite designers to mitigate possible orbital debris damage to a spacecraft and its instruments using shielding and design choices, thereby extending the useful life of the mission and its experiments. The model also has a mode that simulates debris telescope/radar observations from the ground. Both it and the spacecraft flux mode can be used to design experiments to measure the meteoroid and orbital debris environments.
ORDEM is used heavily in the hypervelocity protection community, those that design, build, and test shielding for spacecraft and rocket upper stages. The fidelity of the ORDEM model allows for the optimization of shielding to balance mission success criteria, risk posture, and cost considerations.
As both government and civilian actors continue to exploit the space environment for security, science, and the economy, it is important that we track the debris risks in increasingly crowded orbits, in order to minimize damage to these space assets to make sure these missions continue to operate safely. ORDEM is NASA’s primary tool for computing and mitigating these risks.
ORDEM is used by NASA, the Department of Defense, and other U.S. government agencies, directly or indirectly (via the Debris Assessment Software, MSC-26690-1) to evaluate collision risk for large trackable objects, as well as other mission-ending risks associated with small debris (such as tank ruptures or wiring cuts). In addition to the use as an engineering tool, ORDEM has been used by NASA and other missions in the conceptual design phase to analyze the frequency of orbital debris impacts on potential in situ sensors that could detect debris too small to be detected from ground-based assets.
Commercial and academic users of ORDEM include Boeing, SpaceX, Northrop Grumman, the University of Colorado, California Polytechnic State University, among many others. These end users, similar to the government users discussed above, use the software to (1) directly determine potential hazards to spaceflight resulting from flying through the debris environment, and (2) research how the debris environment varies over time to better understand what behaviors may be able to mitigate the growth of the environment.
The quality and quantity of data available to the NASA Orbital Debris Program Office (ODPO) for the building, verification, and validation of the ORDEM model is greater than for any other entity that performs similar research. Many of the models used by other research and engineering organizations are derived from the models that ODPO has published after developing them for use in ORDEM.
ORDEM Team
Alyssa Manis Andrew B, Vavrin Brent A. Buckalew Christopher L. Ostrom Heather Cowardin Jer-chyi Liou John H, Seago John Nicolaus Opiela Mark J. Matney, Ph.D. Matthew Horstman Phillip D. Anz-Meador, Ph.D. Quanette Juarez Paula H. Krisko, Ph.D. Yu-Lin Xu, Ph.D. Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
Office of Technology, Policy and Strategy (OTPS) View the full article
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Ames Research Center: ProgPy is an open-source Python package supporting research and development of prognostics, health management, and predictive maintenance tools.
Prognostics is the science of prediction, and the field of Prognostics and Health Management (PHM) aims at estimating the current physical health of a system (e.g., motor, battery, etc.) and predicting how the system will degrade with use. The results of prognostics are used across industries to prevent failure, preserve safety, and reduce maintenance costs.
Prognostics, and prediction in general, is a very difficult and complex undertaking. Accurate prediction requires a model of the performance and degradation of complex systems as a function of time and use, estimation and management of uncertainty, representation of system use profiles, and ability to represent impact of neighboring systems and the environment. Any small discrepancy between the model and the actual system is compounded repeatedly, resulting in a large variation in the resulting prediction. For this reason, prognostics requires complex and capable algorithms, models, and software systems.
The ProgPy architecture can be thought of as three innovations: the Prognostic Models, the Prognostic Engine, Prognostic Support Tools.
The first part of the ProgPy innovation is the Prognostic Models. The model describes the prognostic behavior of the specific system of interest. ProgPy’s architecture includes a spectrum of modeling methodologies, ranging from physics-based models to entirely data-driven or hybrid techniques. Most users develop their own physics-based model, train one of the ProgPy data-driven models (e.g., Neural-Network models), or some hybrid of the two. A set of mature models for systems like batteries, electric motors, pumps, and valves are distributed in ProgPy. For these parameterized models, users tune the model to their specific system using the model tuning tools. The Prognostics Engine and Support Tools are built on top of these models, meaning a user that creates a new model will immediately be able to take advantage of the other features of ProgPy.
The Prognostic Engine is the most important part of ProgPy and forms the backbone of the software. The Prognostics Engine uses a Prognostics Model to perform the key functions of prognostics and health state estimation. The value in this design is that the Prognostics Engine can use any ProgPy model, whether it be a model distributed with ProgPy or a custom model created by users, to perform health state estimation and prognostics in a configurable way. The components of the Prognostics Engine are extendable, allowing users to implement their own state estimation or prediction algorithm for use with ProgPy models or use one distributed with ProgPy. Given the Prognostics Engine and a model, users can start performing prognostics for their application. This flexible and extendable framework for performing prognostics is truly novel and enables the widespread impact of ProgPy in the prognostic community.
The Prognostic Support Tools are a set of features that aid with the development, tuning, benchmarking, evaluation, and visualization of prognostic models and Prognostics Engine results (i.e., predictions). Like the Prognostic Engine, the support tools work equally with models distributed with ProgPy or custom models created by users. A user creating a model immediately has access to a wide array of tools to help them with their task.
Detailed documentation, examples, and tutorials of all these features are available to help users learn and use the software tools.
These three innovations of ProgPy implement architectures and widely used prognostics and health management functionality, supporting both researchers and practitioners. ProgPy combines technologies from across NASA projects and mission directorates, and external partners into a single package to support NASA missions and U.S. industries. Its innovative framework makes it applicable to a wide range of applications, providing enhanced capabilities not available in other, more limited, state-of-the-art software packages.
ProgPy offers unique features and a breadth and depth of unmatched capabilities when compared to other software in the field. It is novel in that it equips users with the tools necessary to do prognostics in their applications as-is, eliminating the need to adapt their use case to comply with the software available. This feature of ProgPy is an improvement upon the current state-of-the-art, as other prognostics software are often developed for specific use cases or based on a singular modeling method (Dadfarina and Drozdov, 2013; Davidson-Pilon, 2022; Schreiber, 2017). ProgPy’s unique approach opens a world of possibilities for researchers, practitioners, and developers in the field of prognostics and health management, as well as NASA missions and U.S. industries.
ProgPy Team:
Adam J Sweet, Aditya Tummala, Chetan Shrikant Kulkarni Christopher Allen Teubert Jason Watkins Kateyn Jarvis Griffith Matteo Corbetta Matthew John Daigle Miryam Stautkalns Portia Banerjee Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
Office of Technology, Policy and Strategy (OTPS) View the full article
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By NASA
When/Where
August 27-28, 2024
NASA Jet Propulsion Laboratory in Pasadena, CA
Who may attend?
Invited participants from the NASA Centers, NASA HQ, and the broader community of IR technology developers and stakeholders. All participants must be U.S. Persons – the meeting will be held at the CUI level and presentations may contain ITAR material.
Registration will be available, soon!
Purpose
The purpose of the TIM is to openly discuss and review the current state of IR technology in the 2-1000 µm wavelength range. This workshop is intended to evaluate existing relevant NASA-needed technologies and developments, identify opportunities for investments and collaboration, and formulate agency-level strategies to meet its near- and far- term needs for science and exploration missions. The presentations and contact information list will be captured in a proceedings package that will be available to all attendees and NASA stakeholders.
Background
IR detector technology is critical for NASA’s future missions, many of which require state-of-the-art infrared payloads in support Science Mission Directorate (SMD), Space Technology Mission Directorate (STMD), and Exploration Mission Directorate (EOMD). IR sensors utilized in infrared missions span a wide gamut, including multispectral, polarimetric imaging, point-source detection, scanning dispersive hyperspectral imaging, staring interferometric hyperspectral imaging, and astronomical imaging. Space-qualified IR detectors are a leading item on NASA’s critical technology lists as they are key enablers for many science missions. The objectives and IR sensor needs for future NASA missions are described in the most recent decadal surveys for Earth Science, Planetary Science, Heliophysics, and Astronomy and Astrophysics:
Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space Origins, Worlds, and Life: A Decadal Strategy for Planetary Science and Astrobiology 2023-2032 Solar and Space Physics: A Science for a Technological Society Pathways to Discovery in Astronomy and Astrophysics for the 2020s To promote knowledge sharing among science and engineering practitioners external- and internal-to NASA, the NASA Engineering and Safety Center (NESC) Sensors & Instrumentation Technical Discipline Team (S&I TDT) recently established an IR Detector Community of Practice (IR CoP).
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