SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This presentation discusses the NASA Ground, Test, and Surface Systems Taxonomy (TX-13) related capability gaps, including details of architecture, technology, engineering, and policy gaps for enabling sustainable lunar surface operations and subsequent Mars missions.
An overview of airships past, present, and future is provided in a Powerpoint-formatted presentation. This presentation was requested for transfer to the British MOD by Paul Espinosa of NASA Ames, Code PX. The presentation provides general information about airships divided into four main categories: the legacy of NASA Ames in LTA (Lighter-Than-Air), LTA taxonomy and theory, LTA revival and missions, and LTA research and technology.
There is growing capability in the field of “Big Data” and “Data Analytics” which Mass Properties Engineers might like to take advantage of. This paper utilizes an implementation of the IBM Knowledge Analytics and Watson search capabilities to explore a corpus of material developed primarily with the interests of Mass Properties Engineers and vehicle concept developers at its forefront. The full collection of SAWE (Society of Allied Weight Engineers, Inc.) Technical Papers from 1939 through 2015 is a major portion of the knowledge content. Additional aerospace vehicle design information includes metadata from AIAA (American Institute for Aeronautics and Astronautics), and INCOSE (International Council on Systems Engineering) as well as author-provided personal search material. This data is processed with respect to certain expected content, data taxonomies and key words to become the core data in NASA Langley Research Center’s “Vehicle Analysis Analytics”, IBM Watson Content. Processed data becomes the corpus of information which is interrogated to provide examples of finding data for mass regression analysis, technology impacts on MPE (Mass Properties Engineering), mass properties control, standards, and other aspects of interest.
Safety-critical computer systems must be engineered to meet system and software safety requirements. For legacy safety-critical computer systems, software safety requirements may not have been formally specified during development. When process-oriented software safety requirements are levied on a legacy system after the fact, where software development artifacts don't exist or are incomplete, the question becomes 'how can this be done?' The risks associated with only meeting certain software safety requirements in a legacy safety-critical computer system must be addressed should such systems be selected as candidates for reuse. This paper proposes a method for ascertaining formally, a software safety risk assessment, that provides measurements for software safety for legacy systems which may or may not have a suite of software engineering documentation that is now normally required. It relies upon the NASA Software Safety Standard, risk assessment methods based upon the Taxonomy-Based Questionnaire, and the application of reverse engineering CASE tools to produce original design documents for legacy systems.
Energy efficiency isn't just a good idea; it's a necessity, both for cost reasons and to meet federal regulatory requirements. First, rising energy unit costs continue to erode NASA's mission budget. NASA spent roughly $156M on facility energy in FY 2007. Although that represents less than one per cent of NASA's overall annual budget, the upward trend in energy costs concerns the agency. While NASA reduced consumption 13%, energy unit costs have risen 63%. Energy cost increases counteract the effects of energy conservation, which results in NASA buying less yet spending more. The second factor is federal energy legislation. The National Energy Conservation Policy Act, as amended by the Energy Policy Act of 2005, Executive Order (EO) 13423 (January, 2007), and the Energy Independence and Security Act (December, 2007), mandates energy/water conservation goals for all federal agencies, including NASA. There are also reporting requirements associated with this legislation. The Energy/Water Management Task was created to support NASA Headquarters Environmental Management Division (HO EMD) in meeting these requirements. With assistance from TEERM, HQ EMD compiled and submitted the NASA Annual Report to the Department of Energy FY 2007. The report contains information on how NASA is meeting federally mandated energy and water management goals. TEERM monitored input for timeliness, errors, and conformity to the new energy/water reporting guidelines and helped compile the information into the final report. TEERM also assists NASA Energy/Water Management with proposal and award calls, updates to the energy/water management database, and facilitating communication within the energy/water management community. TEERM is also supporting NASA and the Interagency Working Group (IWG) on Hydrogen and Fuel Cells. Established shortly after the President announced the Hydrogen Fuel Initiative in 2003, this IWG serves as the mechanism for collaboration among the Federal agencies involved in hydrogen-related research, development, and demonstration. TEERM developed a matrix showing all Hydrogen and Fuel Cell activities from the various NASA centers to be included in the Group's extensive hydrogen research taxonomy of past, present, and future hydrogen activities of the Federal government.
This presentation provides an overview of autonomous systems at NASA, including definitions, description of the Autonomous Systems SCLT (Systems Capability Leadership Team), and a taxonomy of autonomous systems.
The Lunar and Mars Exploration Program Office (LMEPO) was tasked to define candidate architectures for the Space Exploration Initiative to submit to NASA senior management and an externally constituted Outreach Synthesis Group. A systematic, structured process for developing, characterizing, and describing the alternate mission architectures, and applying this process to future studies was developed. The work was done in two phases: (1) national needs were identified and categorized into objectives achievable by the Space Exploration Initiative; and (2) a program development process was created which both hierarchically and iteratively describes the program planning process.
For the last 5 years, NASA Goddard has been investigating Distributed Spacecraft Missions (DSM) system architectures, surveying past, current and potential mission concepts, developing several taxonomies and identifying some key technologies that will enable future DSM mission design, development, operations and management. This paper summarizes this Initiative and the talk will provide details about specific Goddard DSM projects that are currently underway and that are relevant to future Earth Science missions.
For the last 5 years, NASA Goddard has been investigating Distributed Spacecraft Missions (DSM) system architectures, surveying past, current and potential mission concepts, developing several taxonomies and identifying some key technologies that will enable future DSM mission design, development, operations and management. This paper summarizes this Initiative and the talk will provide details about specific Goddard DSM projects that are currently underway and that are relevant to future Earth Science missions.
A relational data base, called the Operational Experience Data Base, was constructed to electronically store and organize human-factors information from the Skylab and Space Shuttle missions. The taxonomy used to organize this data base builds on the one used for the Skylab human-machine experiments. This information can be used by NASA engineers and operations personnel to remedy design problems, or expand on design successes. Work to improve and expand the data base is underway.
This report documents the work performed by Lockheed Martin Aeronautics (LM Aero) under NASA contract NNL06AA08B, delivery order NNL07AB06T. The Concept Development for Software Health Management (CDSHM) program was a NASA funded effort sponsored by the Integrated Vehicle Health Management Project, one of the four pillars of the NASA Aviation Safety Program. The CD-SHM program focused on defining a structured approach to software health management (SHM) through the development of a comprehensive failure taxonomy that is used to characterize the fundamental failure modes of safety-critical software.
NASA's Global Change Master Directory (GCMD) develops and expands a hierarchical set of controlled vocabularies (keywords) covering the Earth sciences and associated information (data centers, projects, platforms, instruments, etc.). The purpose of the keywords is to describe Earth science data and services in a consistent and comprehensive manner, allowing for the precise searching of metadata and subsequent retrieval of data and services. The keywords are accessible in a standardized SKOSRDFOWL representation and are used as an authoritative taxonomy, as a source for developing ontologies, and to search and access Earth Science data within online metadata catalogues. The keyword development approach involves: (1) receiving community suggestions, (2) triaging community suggestions, (3) evaluating the keywords against a set of criteria coordinated by the NASA ESDIS Standards Office, and (4) publication/notification of the keyword changes. This approach emphasizes community input, which helps ensure a high quality, normalized, and relevant keyword structure that will evolve with users changing needs. The Keyword Community Forum, which promotes a responsive, open, and transparent processes, is an area where users can discuss keyword topics and make suggestions for new keywords. The formalized approach could potentially be used as a model for keyword development.
Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.
Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.
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).
Within NASA, exploration capability gaps are defined as the difference between the current state-of-the-art in capabilities and the anticipated needs of future human spaceflight architectures. As NASA and its partners’ capabilities for human exploration of deep space continue to mature, it is necessary to understand the capability gaps that require closure to support future habitation systems, such as the Lunar Surface Habitat (SH) and Mars Transit Habitat (TH) currently in concept development. This paper will identify high-priority capability gaps for exploration habitation and show potential options for gap closure through investment in technology, development, and testing. High-priority capability gaps are divided into the following general taxonomy areas: human health/life support/habitation systems, flight computing and avionics, power and energy storage, communications and navigation, thermal management systems, human exploration destination systems, autonomous systems, sensors and instruments, GNC (guidance, navigation, and control), robotic systems, ground and uncrewed surface systems, and materials/structures/mechanical systems/manufacturing. In the gap identification process, teams of discipline experts from across NASA reviewed the latest habitation architecture needs against current capabilities to understand where gaps may exist. The results of the assessment established a basis for the current state-of-the-art within each gap and identified the capability needs of the proposed exploration missions the gap links to. An assessment of how each test platform (e.g., Ground, International Space Station (ISS), Commercial Low Earth Orbit (LEO) Destinations, Gateway) may be leveraged to mature capabilities and potentially provide a route to gap closure will be discussed. The notional timeline for gap closure to support reference missions and impacts to overall schedule are also assessed where appropriate. Based on the capability gap analysis described above, the paper summarizes important technology maturation considerations for human exploration architectures, with a focus on the Mars TH. The previously published NASA habitation ground rules and assumptions document is used as the basis to classify gaps as enabling, enhancing, or “push” opportunities for a particular architecture. Stepwise technology maturation plans/considerations are presented for some selected critical gaps. Overall, the analysis in this paper is intended to help influence development priorities for habitation systems, where high-priority, critical gaps are those currently assessed as having a low probability of closure by the anticipated need date. Capability gap analysis also informs the risk register for exploration habitation systems and mitigation strategies to ensure readiness of key technologies to support future mission timelines. Linkage between capability gaps for Moon and Mars is noted, as closure of a gap at a Lunar destination may subsequently enable or enhance Mars TH architectures.
Within NASA, exploration capability gaps are defined as the difference between the current state-of-the-art in capabilities and the anticipated needs of future human spaceflight architectures. As NASA and its partners’ capabilities for human exploration of deep space continue to mature, it is necessary to understand the capability gaps that require closure to support future habitation systems, such as the Lunar Surface Habitat (SH) and Mars Transit Habitat (TH) currently in concept development. This paper will identify high-priority capability gaps for exploration habitation and show potential options for gap closure through investment in technology, development, and testing. High-priority capability gaps are divided into the following general taxonomy areas: human health/life support/habitation systems, flight computing and avionics, power and energy storage, communications and navigation, thermal management systems, human exploration destination systems, autonomous systems, sensors and instruments, GNC (guidance, navigation, and control), robotic systems, ground and uncrewed surface systems, and materials/structures/mechanical systems/manufacturing. In the gap identification process, teams of discipline experts from across NASA reviewed the latest habitation architecture needs against current capabilities to understand where gaps may exist. The results of the assessment established a basis for the current state-of-the-art within each gap and identified the capability needs of the proposed exploration missions the gap links to. An assessment of how each test platform (e.g., Ground, International Space Station (ISS), Commercial Low Earth Orbit (LEO) Destinations, Gateway) may be leveraged to mature capabilities and potentially provide a route to gap closure will be discussed. The notional timeline for gap closure to support reference missions and impacts to overall schedule are also assessed where appropriate. Based on the capability gap analysis described above, the paper summarizes important technology maturation considerations for human exploration architectures, with a focus on the Mars TH. The previously published NASA habitation ground rules and assumptions document is used as the basis to classify gaps as enabling, enhancing, or “push” opportunities for a particular architecture. Stepwise technology maturation plans/considerations are presented for some selected critical gaps. Overall, the analysis in this paper is intended to help influence development priorities for habitation systems, where high-priority, critical gaps are those currently assessed as having a low probability of closure by the anticipated need date. Capability gap analysis also informs the risk register for exploration habitation systems and mitigation strategies to ensure readiness of key technologies to support future mission timelines. Linkage between capability gaps for Moon and Mars is noted, as closure of a gap at a Lunar destination may subsequently enable or enhance Mars TH architectures.
The NASA Engineering and Safety Center (NESC) was established to improve safety through engineering excellence within NASA programs and projects. As part of this goal, methods are being investigated to enable the NESC to become proactive in identifying areas that may be precursors to future problems. The goal is to find unknown indicators of future problems, not to duplicate the program-specific trending efforts. The data that is critical for detecting these indicators exist in a plethora of dissimilar non-conformance and other databases (without a common format or taxonomy). In fact, much of the data is unstructured text. However, one common database is not required if the right standards and electronic tools are employed. Electronic data mining is a particularly promising tool for this effort into unsupervised learning of common factors. This work in progress began with a systematic evaluation of available data mining software packages, based on documented decision techniques using weighted criteria. The four packages, which were perceived to have the most promise for NASA applications, are being benchmarked and evaluated by independent contractors. Preliminary recommendations for "best practices" in data mining and trending are provided. Final results and recommendations should be available in the Fall 2005. This critical first step in identifying "unknown unknowns" before they become problems is applicable to any set of engineering or programmatic data.