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Systems are changing and engineering practices must mind the balance between evolutionary and revolutionary change as we move towards increasingly agile processes, enabled by interconnected tools, to best provide for partnered collaboration. This is the first of many evolutions of the Goddard Digital Engineering strategy, in preparation for the NASA 2040 vision.
Systems are changing and engineering practices must mind the balance between evolutionary and revolutionary change as we move towards increasingly agile processes, enabled by interconnected tools, to best provide for partnered collaboration. The Digital Thread is a foundational capability for a functional Digital Engineering Ecosystem, which is Phase 1 of Goddard's Digital Engineering strategy in an effort to achieve Goddard 2040 vision.
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National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
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The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions). This paper will summarize the approach, scope, parsing patterns applied, metamodel, and associated workflows for the As-Is modeling. It will also summarize the results and insights gleaned during that phase, including the review process. These insights have informed the analysis and will be discussed. The analysis modeling phase will also be summarized including how the stakeholders were engaged, how the common elements were handled and dispositioned, and will also describe some of the plans for the future of NASA NPDs and NPRs.
The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions).
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The DAVINCI mission will scientifically study the Venusian atmosphere to better understand the current state of the planet and its evolutionary history. This will be accomplished by deploying a descent probe to collect in-situ atmospheric dynamic and spectroscopic measurements, characterize ambient temperatures and pressures during descent, and surface imaging below the cloud layer. To better characterize the DAVINCI In-Situ Campaign, a success tree, developed in the SysML tool, MagicDraw, was created to define the events necessary to have a successful in-situ campaign, for each phase of the campaign. This success tree was also used to validate DAVINCI Program, Mission, and Element level requirements. In addition, because DAVINCI is a single-string mission, the success tree was also used to identify areas of redundancy and resiliency throughout the campaign. The initial redundancy assessment recommended further on-orbit evaluation of the probe’s communication system, as well as further evaluation of the ability of the mass spectrometer turbo-molecular pump to handle the expected decent environment. During the integration and test phase of the overall mission, the success tree will be used to guide the mission validation process, helping to ensure that the in-situ mission elements can perform in accordance with the DAVINCI In-Situ Concept of Operations document.
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The System-Wide Safety project at NASA is developing the concept and a subset of supporting technologies for evolving safety management from a post-accident iterative improvement on safety performed by large commercial operators only to predictive and increasingly autonomous safety functions that are performed during operations across vehicles, airports, and services. The system of interest is the In-time Aviation Safety Management System (IASMS), recommended by the National Academies for NASA to develop the concept in coordination with the broad and diverse community of stakeholders in aviation safety. Because the system context is the airspace and aviation industry, it is important to create and maintain traceability from the research work and system architecture elements developed at NASA to community-created roadmaps and aviation agency guidance.
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The DAVINCI mission will scientifically study the Venusian atmosphere to better understand the current state of the planet and its evolutionary history. This will be accomplished by deploying a descent probe to collect in-situ atmospheric dynamic and spectroscopic measurements, characterize ambient temperatures and pressures during descent, and surface imaging below the cloud layer. To better characterize the DAVINCI In-Situ Campaign, a success tree, developed in the SysML tool, MagicDraw, was created to define the events necessary to have a successful in-situ campaign, for each phase of the campaign. This success tree was also used to validate DAVINCI Program, Mission, and Element level requirements. In addition, because DAVINCI is a single-string mission, the success tree was also used to identify areas of redundancy and resiliency throughout the campaign. The initial redundancy assessment recommended further on-orbit evaluation of the probe’s communication system, as well as further evaluation of the ability of the mass spectrometer turbo-molecular pump to handle the expected decent environment. During the integration and test phase of the overall mission, the success tree will be used to guide the mission validation process, helping to ensure that the in-situ mission elements can perform in accordance with the DAVINCI In-Situ Concept of Operations document.