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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 55 records · Page 3

NASA Space Flight Program and Project Management Handbook

This handbook is the companion document to NPR 7120.5F, NASA Space Flight Program and Project Management Requirements. It represents the accumulation of knowledge on managing program and projects derived from NASA’s human, robotic, and scientific missions. It incorporates the “corporate knowledge” for existing and future NASA space flight programs and projects, including NASA’s Artemis missions to establish a sustainable human presence on the Moon through collaboration with commercial and international partners, NASA’s James Webb Space Telescope (JWST) mission, and NASA’s robotic missions on Mars. The practices discussed have evolved as a function of NASA’s core values of safety, integrity, teamwork, excellence, and inclusion, and may also prove a resource for other agencies, the private sector, and academia. The knowledge gained from the Agency’s victories and defeats, including the checks and balances and initiatives to better control cost and risk, provides a foundation for continuing an exciting and healthy space program. This handbook provides implementation guidance for NPR 7120.5F and includes the changes and updates to key procedural requirements in NPR 7120.5F since NPR 7120.5E. The goal of the NPR requirements is to ensure programs and projects are developed and successfully executed in the most cost-effective and efficient manner possible. This handbook provides context, rationale, and explanation to facilitate the application of requirements and to pass on some of the hard-won best practices and lessons learned.

Tracy L Osborne↗

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗

Learning From What Goes Well

NASA’s System Wide Safey (SWS) project under the Aviation Operations and Safety Program (AOSP) of the Aeronautical Research Mission Directorate (ARMD) supports work demonstrating the human contribution to safety (HC2S) and the benefits of learning from all operations including learning from what goes well. Some of this work is being done in collaboration with the Flight Safety Foundation (FSF). The presentation provides the rationale for this work, its expected benefits, and its methodology.

rsilience↗

A Storm's Approach; Hurricane Shelter Training in a Digital Age

New York City's Office of Emergency Management (OEM) originally ran hundreds of classroom based courses, where they brought together civil servants to learn how to run a Hurricane Shelter (HS). This approach was found to be costly, time consuming and lacked any sense of an impending disaster and need for emergency response. In partnership with the City of New York University School of Professional studies, Gronstedt Group and Daden Limited, the OEM wanted to create a simulation that overcame these issues, providing users with a more immersive and realistic approach at a lower cost. The HS simulation was built in the virtual world Second Life (SL). Virtual worlds are a genre of online communities that often take the form of a computer-based simulated environments, through which users can interact with one another and use or create objects. Using this technology allowed managers to apply their knowledge in both classroom and remote learning environments. The shelter simulation is operational 24/7, guiding users through a 4 1/2 hour narrative from start to finish. This paper will describe the rationale for the project, the technical approach taken - particularly the use of a web based authoring tool to create and manage the immersive simulation, and the results from operational use.

Boyarsky, Andrew↗

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗

AI/ML-assisted Design of Phosphate Glass and Ceramic Nuclear Waste Forms

Borosilicate glass is the widely accepted waste form for immobilization of high and medium level nuclear wastes. Advances in nuclear energies and new reactor designs require the development of new waste forms. For example, wastes from molten salt reactors and reprocessing of nuclear fuels lead to salt-based wastes that are difficult to be immobilized by conventional borosilicate glasses due to limited solubility and waste loading. In designing new waste forms, machine learning (ML) and artificial intelligence (AI) based approaches are much needed and can be beneficial in enabling a more efficient design in large parameter spaces as compared to traditional Edisonian trial-and-error approaches. Here, we report in this paper the rationale and latest progress of our ML/AI-based design of phosphate-based waste forms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Scientific rationale and strategies for a first comet mission: Report of the Comet Halley science working group

The science objectives of a first comet mission are reviewed and related to what is known or can be expected to be learned in the near future from ground-based and near earth observations. A set of instruments and their science objectives are defined for a mission to Comet Halley during its 1985/86 apparition. The benefits from a fast flyby, a slow flyby, or a rendezvous mission and the relative impact of each on the instrument payload were assessed. The relative scientific value of encounters with the comet at distances from the sun ranging from 1 AU to 2.5 AU, including possible tradeoffs between flyby velocity and distance was considered. Pre- and post-perihelion encounters were likewise evaluated.

Source record↗

Adventures in cFS Unit Testing: Examining the Past to Explain the Present with an Eye toward the Future

An overview of my experiences writing unit tests for various projects with a specific focus on my work unit testing core Flight System (cFS) applications. I recount some of the direct personal experiences I have had that showed me the utility of having done unit testing for my projects. Many of the tips, tricks and pitfalls encountered during my time writing unit tests for the cFS app, CF, are examined. I also compare and contrast my cFS unit testing development with that of a parallel project, in which I write unit tests using RSpec, a testing framework for the Ruby programming language. I impart my complete methodology behind the CF app unit testing effort and the rationale for why I did it that way. Then I give some ideas for how you can do your own unit testing for cFS applications. You will also learn about my hopes for how unit testing cFS applications can be done going forward from where we are now.

"unit testing"↗

The Trash Compaction Processing System (TCPS) Technology Demonstration and Risk Reduction Updates FY23-FY24

The Next STEP Phase BTrash Compaction Processing System (TCPS) is being developed for a technology demonstration on the International Space Station(ISS) to process common spacecraft consumables trash such as food packaging and clothing to reduce trash storage volume, recover water, safen(e.g. reduce biological activity), and shape the trash for storage, jettison, reuse (e.g.,radiation shielding) and/or recycling. Sierra Space is developing the flight demonstration hardware and National Aeronautics and Space Administration(NASA) continues to conduct risk reduction activities to exploreoperational scenarios, setting the stage for successful on-orbit tests. After the flight demonstration on ISS, the TCPS becomes available to be infused into NASA short-and long-term missions.This paper will discuss the rationale for the updated requirement definitions outlined in the 2023 International Conference on Environmental System TCPS paper and the ongoing risk reduction activities. In addition, drawing from lessons learned in the development of the TCPS, a discussion of other compression technologies will be introduced for various mission types.

Trash Compaction Processing System↗

The Trash Compaction Processing System (TCPS) Technology Demonstration and Risk Reduction Updates FY23-FY24 Presentation

The Next STEP Phase BTrash Compaction Processing System (TCPS) is being developed for a technology demonstration on the International Space Station(ISS) to process common spacecraft consumables trash such as food packaging and clothing to reduce trash storage volume, recover water, safen(e.g. reduce biological activity), and shape the trash for storage, jettison, reuse (e.g.,radiation shielding) and/or recycling. Sierra Space is developing the flight demonstration hardware and National Aeronautics and Space Administration(NASA) continues to conduct risk reduction activities to exploreoperational scenarios, setting the stage for successful on-orbit tests. After the flight demonstration on ISS, the TCPS becomes available to be infused into NASA short-and long-term missions.This paper will discuss the rationale for the updated requirement definitions outlined in the 2023 International Conference on Environmental System TCPS paper and the ongoing risk reduction activities. In addition, drawing from lessons learned in the development of the TCPS, a discussion of other compression technologies will be introduced for various mission types.

Trash Compaction Processing System↗

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗

New Metadata Capabilities within NASA's Common Metadata Repository (CMR)

This talk will convey the new capabilities and features of the UMM-Variables and the UMM-Services metadata model within the Common Metadata Repository (CMR) and how community engagement through the ESDIS Standards Office (ESO) review shaped the new versions of the models. The Unified Metadata Model (UMM) provides a common metadata model to unify legacy systems (i.e. GCMD (Global Change Master Directory), ECHO (Earth Observing System (EOS) Clearinghouse)) with new systems (i.e. CMR). The rationale and migration process of the Service Entry Resource Format (SERF) to the UMM-S will also be conveyed. The talk will conclude with discussing issues and lessons learned from the review process and how future reviews will be conducted to ensure a more targeted, meaningful review with a faster turn-around time for triage and implementation.

Earth Observing System↗

Case-Based Capture and Reuse of Aerospace Design Rationale

The goal of this project is to apply artificial intelligence techniques to facilitate capture and reuse of aerospace design rationale. The project applies case-based reasoning (CBR) and concept mapping (CMAP) tools to the task of capturing, organizing, and interactively accessing experiences or "cases" encapsulating the methods and rationale underlying expert aerospace design. As stipulated in the award, Indiana University and Ames personnel are collaborating on performance of research and determining the direction of research, to assure that the project focuses on high-value tasks. In the first five months of the project, we have made two visits to Ames Research Center to consult with our NASA collaborators, to learn about the advanced aerospace design tools being developed there, and to identify specific needs for intelligent design support. These meetings identified a number of task areas for applying CBR and concept mapping technology. We jointly selected a first task area to focus on: Acquiring the convergence criteria that experts use to guide the selection of useful data from a set of numerical simulations of high-lift systems. During the first funding period, we developed two software systems. First, we have adapted a CBR system developed at Indiana University into a prototype case-based reasoning shell to capture and retrieve information about design experiences, with the sample task of capturing and reusing experts' intuitive criteria for determining convergence (work conducted at Indiana University). Second, we have also adapted and refined existing concept mapping tools that will be used to clarify and capture the rationale underlying those experiences, to facilitate understanding of the expert's reasoning and guide future reuse of captured information (work conducted at the University of West Florida). The tools we have developed are designed to be the basis for a general framework for facilitating tasks within systems developed by the Advanced Design Technologies Testbed (ADTT) project at ARC. The tenets of our framework are (1) that the systems developed should leverage a designer's knowledge, rather than attempting to replace it; (2) that learning and user feedback must play a central role, so that the system can adapt to how it is used, and (3) that the learning and feedback processes must be as natural and as unobtrusive as possible. In the second funding period we will extend our current work, applying the tools to capturing higher-level design rationale.

Leake, David B.↗

On Generalizably Improving RANS Predictions of Flow Separation and Reattachment

This work presents progress in an ongoing data-driven turbulence modeling effort at NASA Langley Research Center to improve predictions for separated flows. Two augmentation strategies are presented – one to improve predictions for the point of reattachment after smooth-body separation, and another to improve predictions for flow separation due to prolonged adverse pressure gradients. The reattachment augmentation was inspired from lessons learned during attempts at using field inversion and machine learning (FIML); however, it is prescribed as a conventional analytic closed-form expression. Results show that, when introduced into the SST k-w model, it shortens the otherwise overpredicted separation bubble length for three cases characterized by different Reynolds numbers and flow geometries. The second augmentation, which addresses flow separation under prolonged adverse pressure gradients, is inferred via FIML and applied as a neural network within the Spalart-Allmaras model. It uses features that are chosen such that the presented results show an improved predictive accuracy for the training case without affecting other cases where the augmentation is not needed. Further testing is required to establish the generalizability of this augmentation. The rationale behind the chosen features that serve as inputs to both these augmentations is also presented for the benefit of the reader.

machine learning↗

GPS Lessons Learned from the International Space Station, Space Shuttle and X-38

This document is a collection of writings concerning the application of Global Positioning System (GPS) technology to the International Space Station (ISS), Space Shuttle, and X-38 vehicles. An overview of how GPS technology was applied is given for each vehicle, including rationale behind the integration architecture, and rationale governing the use (or non-use) of GPS data during flight.

Goodman, John L.↗

The Near Earth Asteroid Medical Conditions List

Purpose: The Exploration Medical Capability (ExMC) element is one of six elements within NASA s Human Research Program (HRP) and is responsible for addressing the risk of "the inability to adequately recognize or treat an ill or injured crewmember" for exploration-class missions. The Near Earth Asteroid (NEA) Medical Conditions List, constructed by ExMC, is the first step in addressing the above-mentioned risk for the 13-month long NEA mission. The NEA mission is being designed by NASA's Human Space Flight Architecture Team (HAT). The purpose of the conditions list is to serve as an evidence-based foundation for determining which medical conditions could affect a crewmember during the NEA mission, which of those conditions would be of concern and require treatment, and for which conditions a gap in knowledge or technology development exists. This information is used to focus research efforts and technology development to ensure that the appropriate medical capabilities are available for exploration-class missions. Scope and Approach: The NEA Medical Conditions List is part of a broader Space Medicine Exploration Medical Conditions List (SMEMCL), which incorporates various exploration-class design reference missions (DRMs). The conditions list contains 85 medical conditions which could occur during space flight and which are derived from several sources: Long-Term Surveillance of Astronaut Health (LSAH) in-flight occurrence data, The Space Shuttle (STS) Medical Checklist, The International Space Station (ISS) Medical Checklist, and subject matter expert opinion. Each medical condition listed has been assigned a clinical priority and a clinical priority rationale based on incidence, consequence, and mitigation capability. Implementation: The conditions list is a "living document" and as such, new conditions can be added to the list, and the priority of conditions on the list can be adjusted as the DRM changes, and as screening, diagnosis, or treatment capabilities change. The NEA medical conditions list was used recently as the basis for identifying gaps in in-flight medical evaluation (screening) capabilities. Learning Objectives: The audience will become familiar with the approach taken by NASA's Exploration Medical Capability element in addressing the risk of inability to recognize and treat medical conditions in the setting of a Near Earth Asteroid mission. Which one of the following statements is incorrect? a) The Near Earth Asteroid (NEA) medical conditions list includes 85 medical conditions which could occur during space flight. b) Each condition on the NEA medical conditions list has been assigned a clinical priority and a clinical priority rationale. c) The NEA medical conditions list targets a mission to Mars. d) The NEA medical conditions list should be viewed as a "living document" where new conditions can be added and clinical priorities adjusted to address changes in the design reference mission or medical capabilities. The incorrect answer is c). The NEA medical conditions list targets a mission to a Near Earth Asteroid.

Barr, Yael R.↗

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗