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At least 271 records · Page 15

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond low Earth orbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carry increased risks due to a number of factors, including distance from Earth, exposure to deep space hazards, reduced capacity and ability to resupply and evacuate, and increased communication delays. As distance from Earth grows and mission length increases, a growing proportion of mission risk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in the United States recommended the early and complete integration of the human system into the spacecraft and mission design process to mitigate this increased risk inherent in exploration missions. Exploration missions will require increasing levels of crew self-sufficiency that will challenge the current operational paradigms established in LEO. Enabling progressive Earth independence requires management of the increasingly complex interactions among spacecraft systems and integration of all the data and functions that affect human health and performance into one coordinated system –the Crew Health and Performance (CHP) system. This system is a critical spacecraft system that is analogous to other systems such as propulsion, guidance and navigation, or avionics. Design and integration of the CHP system requires the evidence-based merger of typically disparate disciplines such as medicine, human factors, and life support system design, using systems engineering (SE) practices. SE provides traceability of spacecraft requirements and enables reliable and repeatable trade space analysis of the many competing options for hardware and software to be included in the mission architecture. The approach described here enables spacecraft and mission designers to align the scope of a CHP system with mission specific requirements, decrease risk to the crew, and increase the probability of mission success.

Kerry McGuire↗

A computer simulator for development of engineering system design methodologies

A computer program designed to simulate and improve engineering system design methodology is described. The simulator mimics the qualitative behavior and data couplings occurring among the subsystems of a complex engineering system. It eliminates the engineering analyses in the subsystems by replacing them with judiciously chosen analytical functions. With the cost of analysis eliminated, the simulator is used for experimentation with a large variety of candidate algorithms for multilevel design optimization to choose the best ones for the actual application. Thus, the simulator serves as a development tool for multilevel design optimization strategy. The simulator concept, implementation, and status are described and illustrated with examples.

Padula, S. L.↗

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗

Lessons Learned in Systems Engineering Availability and Recommendations for Mission Technical Leaders

In spaceflight missions at Goddard Space Flight Center (GSFC), the Mission System Engineer (MSE) is the technical leader of the overall engineering team and also is the Independent Engineering Technical Authority. The responsibility of this role includes the definition of the mission design architecture, concept of operations, and mission requirements, management of risk throughout the development, and verification and validation of the final system performance and function amongst other duties. This responsibility inherently requires time management, enabling focus on a balanced development with appropriate risk. Time is the most valuable resource of the MSE. The system engineer’s availability to interact with the development team (often product or component design leads and technicians, often in different worksites) to discover and mitigate mission risks during development is key to mission success. This paper presents examples from Lunar Reconnaissance Orbiter, Landsat 9, and Neutron star Interior Composition ExploreR (NICER) which represent in-house and out-of-house hardware builds. These examples demonstrate how interactions between the Mission Systems Engineers and other project and partner engineers result in discovery of critical risks, leading to early mitigation with significant cost and performance savings. These three missions would have suffered test failures or on-orbit failures had their MSEs not set aside time to visit engineers and technicians that were working on key pieces of space flight hardware. Availability is more than just time; it is openness to listen to concerns and questions. It begins by building a level of trust in the team that it is safe to ask questions or share concerns without the fear of blame or additional workload. It also requires enabling informal conversations (over lunch, coffee, in the clean room, or at the team members desk, etc.) where key information can be exchanged, and team members may even provide an easy-to-implement mitigation idea for another subsystem. Availability is a highly valuable commodity and completely non-obvious to protect and optimize. The natural tendency of engineers is to keep themselves busy with solving problems that they know about. This paper is encouraging MSEs to resist this tendency to try and solve all the complex problems themselves and actively devote daily time to learning and solving problems that are found with informal communications with other team members.

Lessons Learned↗

Application of Model-Based Systems Engineering for the Development of the Asteroid Redirect Robotic Mission

Model-Based Systems Engineering (MBSE) can augment existing Systems Engineering (SE) processes to more efficiently deliver enhanced products over the project life cycle. Using a multi-user accessible System Model, MBSE has been successfully deployed for the conceptual and preliminary design development of the Asteroid Redirect Robotic Mission (ARRM). The paper provides an overview and examples of the targeted MBSE deployment for development of the mission operational concept, system description, and functional requirements. The paper also includes description of the challenges and lessons learned.

Sindiy, Oleg V.↗

NASA System Engineering Design Process

This slide presentation reviews NASA's use of systems engineering for the complete life cycle of a project. Systems engineering is a methodical, disciplined approach for the design, realization, technical management, operations, and retirement of a system. Each phase of a NASA project is terminated with a Key decision point (KDP), which is supported by major reviews.

Roman, Jose↗

Intake runner for gaseous fuel engine, system, and method

A gaseous fuel engine system includes a hydrogen fuel supply, an engine housing having an intake port extending to a cylinder, and an intake runner. The intake runner includes a valve mount attached to a conduit and having a valve mounting face, and a gaseous fuel admission valve is mounted upon the valve mounting face. A hydrogen fuel passage extends from the valve mount through the conduit and is oriented angularly to at least one of a clamping face of the intake runner or the valve mounting face. Related apparatus and methodology is also disclosed.

Singh, Jaswinder↗

An Integrated High Pressure SOFC and Premixed Compression Ignition (PCI) Engine System

Components for a transformative, high pressure, intermediate temperature solid oxide fuel cell (SOFC) and premixed compression ignition (PCI) engine system were developed. The resulting system is a 1 MWe natural gas fueled hybrid SOFC and reciprocating internal combustion engine system capable of achieving over 70% electrical efficiency at a cost of $0.87/W. To achieve 70% electric efficiency, the SOFC system operates at 80% fuel utilization; however, when operating at this high level of fuel utilization, fuel to the engine (anode tailgas) is extremely dilute. Accordingly, a substantial portion of the effort focused on development and assessment of the combustion strategy capable of consuming this dilute fuel mixture. The engine testing effort showed that pre-chamber assisted compression ignition provides the best combination of controllability, efficiency and emissions. The engine experiments showed that high efficiency combustion can be achieved with near zero criteria pollutants. It is likely that the engine system could operate with minimal aftertreatment requirements, reducing complexity, cost, and precious metal usage. System level modeling was used to assess the overall efficiency, determine cost and efficiency tradeoffs, and demonstrate the potential to perform load following with the hybrid system. Finally, advanced manufacturing was used to demonstrate the potential to fabricate heat exchangers needed for the hybrid system from low chromium materials, which addresses the issue of chromium poisoning of the SOFC stack material.

03 NATURAL GAS↗

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗

Socio-Technical Perspective on Interdisciplinary Interactions During the Development of Complex Engineered Systems

This study investigates interdisciplinary interactions that take place during the research, development, and early conceptual design phases in the design of large-scale complex engineered systems (LaCES) such as aerospace vehicles. These interactions, that take place throughout a large engineering development organization, become the initial conditions of the systems engineering process that ultimately leads to the development of a viable system. This paper summarizes some of the challenges and opportunities regarding social and organizational issues that emerged from a qualitative study using ethnographic and survey data. The analysis reveals several socio-technical couplings between the engineered system and the organization that creates it. Survey respondents noted the importance of interdisciplinary interactions and their benefits to the engineered system as well as substantial challenges in interdisciplinary interactions. Noted benefits included enhanced knowledge and problem mitigation and noted obstacles centered on organizational and human dynamics. Findings suggest that addressing the social challenges may be a critical need in enabling interdisciplinary interactions

McGowan, Anna-Maria R.↗

Twenty Years of Systems Engineering on the Cassini-Huygens Mission

Over the past twenty years, the Cassini-Huygens Mission has successfully utilized systems engineering to develop and execute a challenging prime mission and two mission extensions. Systems engineering was not only essential in designing the mission, but as knowledge of the system was gained during cruise and science operations, it was critical in evolving operational strategies and processes. This paper discusses systems engineering successes, challenges, and lessons learned on the Cassini-Huygens Mission gathered from a thorough study of mission plans and developed scenarios, and interviews with key project leaders across its twenty-year history.

Manor-Chapman, Emily↗

Systems Engineering Technical Authority: A Path to Mission Success

The systems engineering of space missions to study planet Earth has been an important focus of the National Aeronautics and Space Administration (NASA) since its inception. But all space missions are becoming increasingly complex and this fact, reinforced by some major mishaps, has caused NASA to reevaluate their approach to achieving safety and mission success. A new approach ensures that there are adequate checks and balances in place to maximize the probability of safety and mission success. To this end the agency created the concept of Technical Authority which identifies a key individual accountable and responsible for the technical integrity of a flight mission as well as a project-independent reporting path. At the Goddard Space Flight Center (GSFC) this responsibility ultimately begins with the Mission Systems Engineer (MSE) for each satellite mission. This paper discusses the Technical Authority process and then describes some unique steps that are being taken at the GSFC to support these MSEs in meeting their responsibilities.

Andary, James F.↗

NASA's Systems Engineering Approaches for Addressing Public Health Surveillance Requirements

NASA's systems engineering has its heritage in space mission analysis and design, including the end-to-end approach to managing every facet of the extreme engineering required for successful space missions. NASA sensor technology, understanding of remote sensing, and knowledge of Earth system science, can be powerful new tools for improved disease surveillance and environmental public health tracking. NASA's systems engineering framework facilitates the match between facilitates the match between partner needs and decision support requirements in the areas of 1) Science/Data; 2) Technology; 3) Integration. Partnerships between NASA and other Federal agencies are diagrammed in this viewgraph presentation. NASA's role in these partnerships is to provide systemic and sustainable solutions that contribute to the measurable enhancement of a partner agency's disease surveillance efforts.

Vann, Timi↗

Retaining Systems Engineering Model Meaning Through Transformation: Demo 2

Digital engineering strategies typically assume that digital engineering models interoperate seamlessly across the multiple different engineering modeling software applications involved, such as model- based systems engineering (MBSE), mechanical computer-aided design (MCAD), electrical computer-aided design (ECAD), and other engineering modeling applications. The presumption is that the data schema in these modeling software applications are structured in the familiar flat- tabular schema like any other software application. Engineering domain-specific applications (e.g., systems, mechanical, electrical, simulation) are typically designed to solve domain-specific problems, necessarily excluding explicit representations of non-domain information to help the engineer focus on the domain problems (system definition, design, simulation). Such exclusions become problematic in inter-domain information exchange. The obvious assumptions of one domain might not be so obvious to experts in another domain. Ambiguity in domain-specific language can erode the ability to enable different domain modeling applications to interoperate, unless the underlying language is understood and used as the basis for translation from one application to another. The engineering modeling software application industry has struggled for decades to enable these applications to interoperate. Industry standards have been developed, but they have not unified the industry. Why is this? The authors assert that the industry has relied on traditional database integration methods. The basic issue prohibiting successful application integration then is that traditional database-driven integration does not consider the distinct languages of each domain. An engineering models meaning is expressed through the underlying language of that engineering domain. In essence, traditional integration methods do not retain the semantic context (meaning) of the model. The basis of this research stems from the widely held assumption that systems engineering models are (or can be) structured according to the underlying semantic ontology of the model. This assumption can be imagined from two thoughts. 1) Digital systems engineering models are often represented using graph theory (the graph of a complex systems model can contain millions of nodes and edges). When examining the nodes one at a time and following the outbound edges of each node one by one, one can end up with rudimentary statements about the model (i.e., node A relates to node B), as in a semantic graph. 2) Likewise, from the study of natural languages, a sentence can be structured into unambiguous triples of subject-predicate-object within formal and highly expressive semantic ontologies. The rudimentary statements about a systems model discerned with graph theory closely mimic the triples used in the ontologies that try to structure natural languages. In other words, a systems models semantic graph can be (or is) structured into an ontology. Additionally, it is well established in industry that through natural language processing (NLP), which provides the means to create language structures, that computers can interpret ontological graphs. Therefore, the authors hypothesized that if the integrity of the underlying semantic structure of a systems model is retained, the contextual meaning of the model is retained. By structuring system models into the triples of the underlying ontology during the transformation from one MBSE application to another, the authors have provided a proof of the concept that the meaning of a system model can be retained during transformation. The authors assert that this is the missing ingredient in effective systems model-to-model interoperability. ACKNOWLEDGEMENTS The authors would like to thank the FY19 Model Interoperability team members who provided a solid foundation for the FY20 team to leverage: John McCloud, for the work he did to guide us toward the right use of technology that will appropriately discover and manipulate ontologies. Carlos Tafoya, for the work he did to develop an application programming interface (API)/Adapter that would export ontology-based data from GENESYS. Peter Chandler, for the work he did to architect our overall integration solution, with an eye toward the future that would influence a large-scale federated production-level systems engineering digital model ecosystem.

42 ENGINEERING↗

Model-Based Systems Engineering Approach to Managing Mass Margin

When designing a flight system from concept through implementation, one of the fundamental systems engineering tasks ismanaging the mass margin and a mass equipment list (MEL) of the flight system. While generating a MEL and computing a mass margin is conceptually a trivial task, maintaining consistent and correct MELs and mass margins can be challenging due to the current practices of maintaining duplicate information in various forms, such as diagrams and tables, and in various media, such as files and emails. We have overcome this challenge through a model-based systems engineering (MBSE) approach within which we allow only a single-source-of-truth. In this paper we describe the modeling patternsused to capture the single-source-of-truth and the views that have been developed for the Europa Habitability Mission (EHM) project, a mission concept study, at the Jet Propulsion Laboratory (JPL).

cloud comu↗

Model-Based Systems Engineering in Concurrent Engineering Centers

Concurrent Engineering Centers (CECs) are specialized facilities with a goal of generating and maturing engineering designs by enabling rapid design iterations. This is accomplished by co-locating a team of experts (either physically or virtually) in a room with a narrow design goal and a limited timeline of a week or less. The systems engineer uses a model of the system to capture the relevant interfaces and manage the overall architecture. A single model that integrates other design information and modeling allows the entire team to visualize the concurrent activity and identify conflicts more efficiently, potentially resulting in a systems model that will continue to be used throughout the project lifecycle. Performing systems engineering using such a system model is the definition of model-based systems engineering (MBSE); therefore, CECs evolving their approach to incorporate advances in MBSE are more successful in reducing time and cost needed to meet study goals. This paper surveys space mission CECs that are in the middle of this evolution, and the authors share their experiences in order to promote discussion within the community.

MODEL↗