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This presentation reflects discussion points made by participants during the Systems Engineering Best Practices workshop held during the 2024 In-Person LEARN Forum at NASA Ames Research on September 25, 2024. Points covered include those related to systems engineering in terms of best practices as applied to technical documentation, engineering activities, and risk management.
This is a presentation on Mechanical Systems Engineering for Optical Payloads.
NASA's Office of Exploration Systems (OExS) is organized to empower the Vision for Space Exploration with transportation systems that result in achievable, affordable, and sustainable human and robotic journeys to the Moon, Mars, and beyond. In the process of delivering these capabilities, the systems engineering function is key to implementing policies, managing mission requirements, and ensuring technical integration and verification of hardware and support systems in a timely, cost-effective manner. The OExS Development Programs Division includes three main areas: (1) human and robotic technology, (2) Project Prometheus for nuclear propulsion development, and (3) Constellation Systems for space transportation systems development, including a Crew Exploration Vehicle (CEV). Constellation Systems include Earth-to-orbit, in-space, and surface transportation systems; maintenance and science instrumentation; and robotic investigators and assistants. In parallel with development of the CEV, robotic explorers will serve as trailblazers to reduce the risk and costs of future human operations on the Moon, as well as missions to other destinations, including Mars. Additional information is included in the original extended abstract.
Principles, techniques, and approach used in conducting training program in space system engineering
The document contains the proceedings of the training workshop on Innovative Design of Complex Engineering Systems. The workshop was held at the Peninsula Higher Education Center, Hampton, Virginia, March 23 and 24, 2004. The workshop was jointly sponsored by Old Dominion University and NASA. Workshop attendees came from NASA, other government agencies, industry and universities. The objectives of the workshop were to a) provide broad overviews of the diverse activities related to innovative design of high-tech engineering systems; and b) identify training needs for future aerospace work force development in the design area. The format of the workshop included fifteen, half-hour overview-type presentations, a panel discussion on how to teach and train engineers in innovative design, and three exhibits by commercial vendors.
The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers (SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This presentation will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V) tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This presentation details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.
Ion engine systems for control of earth satellites
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. Text-based information about past engineering failures presents one possible solution by facilitating the retrieval of information that can inform new designs. However, identifying documents containing relevant information and extracting required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing-based framework is proposed to discover relevant knowledge from documents containing failure-related design information. Documents containing usable information are filtered using sentiment analysis based on a custom lexicon specialized for engineering design and by filtering out documents containing only irrelevant topics. Next, from the identified usable documents, information relating to engineering failures, contributing factors that can be controlled at design time (“risk factors”), and recommended preventative actions are extracted. Semantic similarity is then used to group similar pieces of extracted information for improved generalizability. The proposed framework is applied to NASA’s Lessons Learned Information System (LLIS). The framework can be used to identify documents containing usable failure-related design information from other databases, extract relevant information from these documents, and generalize the acquired knowledge such that it can be applied to novel systems.
Human exploration of the solar system requires fully autonomous systems when travelling more than 5 light minutes from Earth. This autonomy is necessary to manage a large, complex spacecraft with limited crew members and skills available. The communication latency requires the vehicle to deal with events with only limited crew interaction in most cases. The engineering of these systems requires an extensive knowledge of the spacecraft systems, information theory, and autonomous algorithm characteristics. The characteristics of the spacecraft systems must be matched with the autonomous algorithm characteristics to reliably monitor and control the system. This presents a large system engineering problem. Recent work on product-focused, elegant system engineering will be applied to this application, looking at the full autonomy stack, the matching of autonomous systems to spacecraft systems, and the integration of different types of algorithms. Each of these areas will be outlined and a general approach defined for system engineering to provide the optimal solution to the given application context.
The turbo-shaft engine is an important propulsion system used to power vehicles on land, sea, and in the air. As the power plant for many high performance helicopters, the characteristics of the engine and control are critical to proper vehicle operation as well as being the main determinant to overall vehicle performance. When applied to vertical flight, important distinctions exist in the turbo-shaft engine control system due to the high degree of dynamic coupling between the engine and airframe and the affect on vehicle handling characteristics. In this study, the impact of engine control system architecture is explored relative to engine performance, weight, reliability, safety, and overall cost. Comparison of the impact of architecture on these metrics is investigated as the control system is modified from a legacy centralized structure to a more distributed configuration. A composite strawman system which is typical of turbo-shaft engines in the 1000 to 2000 hp class is described and used for comparison. The overall benefits of these changes to control system architecture are assessed. The availability of supporting technologies to achieve this evolution is also discussed.
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.
Systems engineering studies for automated biological laboratory for exploration of life on Mars
This paper summarizes the past and present successes of the Habitability and Human Factors Branch (HHFB) at NASA Johnson Space Center s Space Life Sciences Directorate (SLSD) in including the Human-As-A-System (HAAS) model in many NASA programs and what steps to be taken to integrate the Human-Centered Design Philosophy (HCDP) into NASA s Systems Engineering (SE) process. The HAAS model stresses systems are ultimately designed for the humans; the humans should therefore be considered as a system within the systems. Therefore, the model places strong emphasis on human factors engineering. Since 1987, the HHFB has been engaging with many major NASA programs with much success. The HHFB helped create the NASA Standard 3000 (a human factors engineering practice guide) and the Human Systems Integration Requirements document. These efforts resulted in the HAAS model being included in many NASA programs. As an example, the HAAS model has been successfully introduced into the programmatic and systems engineering structures of the International Space Station Program (ISSP). Success in the ISSP caused other NASA programs to recognize the importance of the HAAS concept. Also due to this success, the HHFB helped update NASA s Systems Engineering Handbook in December 2007 to include HAAS as a recommended practice. Nonetheless, the HAAS model has yet to become an integral part of the NASA SE process. Besides continuing in integrating HAAS into current and future NASA programs, the HHFB will investigate incorporating the Human-Centered Design Philosophy (HCDP) into the NASA SE Handbook. The HCDP goes further than the HAAS model by emphasizing a holistic and iterative human-centered systems design concept.
The Dawn GDS Team met the SC Sim integration challenge in eight months. The GDS System Engineering approach in response to the SC Simintegration challenge, focused on a set of key practices: decomposition of project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; risk management thru management of expectations; and aggregation of intermediate products into a final product. By maintaining a a system-level focus, the overall systems engineering process unified team GDS Team members with a common goal: the success of the ground system as a whole and not just the success of their individual expert contributions. Incorporation of Agile-type development efforts were aligned with a risk strategy based on team-oriented principles and expectations management, thus achieving a more stable baseline solution without compromising the integrity of the GDS design.
The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.
The CEOS Systems Engineering Office will present a 2007 status report of the CEOS constellation process, present a new systems engineering framework, and analysis results from the GEO Societal Benefit Area (SBA) assessment and the OST constellation requirements assessment.
A discussion of Systems Engineering principles as related to failure prevention methodologies and technologies. The presentation outlines requirements development, design reviews, verification and validation, and finally risk management prectices.