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Model-Based Systems Engineering (MBSE) is an ambiguous concept that means many things to many different people. The purpose of this presentation is to “de-mystify” MBSE, with the intent of moving the sub-discipline forward. Model-Based Systems Engineering was envisioned to manage the increasing complexity within systems and System of Systems. This presentation defines MBSE as the formalized application of modeling (static and dynamic) to support system design and analysis, throughout all phases of the system lifecycle, and through the collection of modeling languages, structures, model-based processes, and presentation frameworks used to support the discipline of systems engineering in a model-based or model-driven context. Using this definition, the components of MBSE (modeling languages, processes, structures, and presentation frameworks) are defined from a language and tool agnostic perspective.

Model-Based Systems Engineering (MBSE) is a growing discipline for applying modeling tools to capture, connect, communicate and control a wide variety of system and project information throughout the lifecycle. A major challenge for engineers and organizations eyeing a move toward MBSE within their projects is the large and growing number and variety of tools available. Yet the selection of a specific tool has wide-ranging, enterprise-level implications for an entire organization, potentially influencing, enhancing (or constraining) systems engineering practices and protocols at almost every level. Despite the importance, tool selection is often arbitrary or narrow, focusing on the needs of a single user without wider consideration for the needs of the organization. Surprisingly, few studies have been done on systematic techniques to compare and contrast tools in ways that will support organizational selection. The research in this paper describes a standardized technique to help address this problem. The Sellers-Chell Method (SCM) uses the 17 basic Systems Engineering processes described in the NASA Procedural Requirements document 7123.1B as a framework to create an objective “Tool Capabilities Inventory.” The method then asks the assessor to create a simple, pre-defined system model example in the tool under consideration and then perform a “Tool Usability Assessment” derived from the famed Cooper-Harper pilot workload scale. The SCM was used to assess several different MBSE tools and found to have face validity in providing consistent results. While far from perfect, it has built-in flexibility to allow users to tailor it to their specific needs and offers the opportunity for fair and consistent results leading to a better fit between a specific MBSE tool and an organization’s needs.

This paper describes research into the application of Model-Based Systems Engineering (MBSE) tools and processes to Risk-Informed Design (RID). RID enables system risk analyses early in the lifecycle of spaceflight projects allowing designers to use risk as a design commodity and part of the overall trade space. RID uses a “minimum functionality” approach, whereby a minimal, single-string system design is first envisioned that only meets basic performance requirements without any regard to overall reliability or safety. Risk analyses are then used to apply informed design enhancements based on their contribution to risk reduction. A recent application of RID was the Altair Lunar Lander Project which was intended for human lunar exploration under NASA’s Constellation Program. The Altair project’s approach and results are reviewed and analyzed in this paper as a specific application of RID. In traditional projects, several tools such as Relex, Windchill, or SAPHIRE, are used in parallel to applying risk-informed design techniques. These analyses also traditionally occur later in the design cycle when changes are more difficult to implement. Safety and reliability analyses typically have no direct connection with the system architecture model, which accurately depicts the physical and functional constructs of a system, including the “ilities”. The model is directly impacted by the results of the analyses. This creates a time-consuming iterative process of analyses and modification because of the need to integrate several tools and teams. To improve this process, the research described here investigated the use of a single, cloud-based MBSE CAD tool called Innoslate that integrates failure analysis into the system architecture model. The specific focus of the research was on the analysis of system failure events through the use of a system architecture-modeling tool and the establishment of an MBSE process that enables system engineers to make risk-informed system modifications during development. The conclusion of the research was that MBSE in general, and Innoslate specifically, is capable of providing an integrated, effective, and quantitative means of developing a risk-informed system design using a minimum functionality baseline process. This can be applied to human and robotic spaceflight systems and other systems with similar complexity. The research demonstrated that random distributions could be added to failure probabilities in order to add “noise” to the results, a task that can be laborious, if not impossible, if performed using a calculator or spreadsheet. The research also demonstrated an end-to-end MBSE process that was applied to a basic system model and the Altair Project. Recommendations for future work conclude the paper.

Want to learn how to perform 21st Century systems engineering faster, better, and cheaper? The answer is with Model-Based Systems Engineering using the Lifecycle Modeling Language and the software, Innoslate. This book will show you how to optimize varying parameters and disciplines throughout the lifecycle of the system within cost and schedule constraints without compromising performance. Real MBSE enables the execution of many activities in parallel, thus enabling the “faster and cheaper” part. Many people can contribute to the design and development at the same time, because the information they create can be easily linked together to form abstractions that enable you to communicate the results at all levels.The author uses a methodology that includes the technique, processes, and tools. This methodology isn’t the only way to have a successful MBSE capability, but all three elements must be incorporated in any methodology you use. We offer this methodology as one that has proven successful over the past decade. It is based on methodologies used since the 1960s, but updated to the modern cloud computing, artificial intelligence age now emerging toward the end of the second decade of the 21st Century.Often people today work in a similar manner to how their grandparents worked in the 1960s, just with electronic tools instead of paper and pencil. Just creating a “model” doesn’t mean you are doing effective MBSE. This book will show you how to take MBSE into the 21st century.

Presented to Systems of Systems Engineering Collaborators Info Exchange by Steven H. Dam, Ph.D., ESEP May 19, 2015. This presentation covers why we have this new language, an overview of the Lifecycle Modeling Language, and how LML can support SoS.

"Internet of Things" (IoT) and "Smart City" are widely recognized to address the complexity of modern city operation. Concentration of population, scarcity of resources and environmental concerns are the main challenges that face city operators, and make ordinary service provisioning less efficient. In city environment, IoT sensors can be sources of real-time data; and, IoT actuators can execute real-time actions in the physical domain. IoT systems range from domain-specific to cross-sectoral systems where valuable data/ information flow across interconnected complex systems. Yet, to integrate domain-specific IoT systems into the complete vision of Smart City, as a System of Systems (SoS), there is a need to address heterogeneity of data sources, diversity of application domains and the big number of stakeholders across different phases of lifecycle. This paper suggests Service Lifecycle Management (SLM) concepts and Lifecycle Modeling Language (LML) to analyze, plan, specify, design, build and maintain IoT-enabled Smart City Service Systems.