An MBSE Methodology for Capability Systems Definition

Systems engineering is a transdisciplinary scientific and applied direction that combines the principles of a systems approach, methods, tools and standards that provide an integrated methodology and relevant technologies for the design and development of complex systems, their comprehensive analysis and verification, efficient and safe use. The modern approach to systems engineering is characterized by an accentuated modeling of systems throughout their life cycle with the ultimate goal of creating their digital twins. It is called model-based systems engineering (MBSE). Consistent integration of MBSE with software engineering leads to the formation of the MBSSE (Model-Based Systems and Software Engineering) direction. This textbook is devoted to this area, the main objective of which is to study the conceptual and scientific-methodological foundations of MBSE and MBSSE, models and methods of life cycle management of systems and software, basic standards of systems engineering, study and mastery of the UML and SysML systems modeling languages and the corresponding modeling tools, study of requirements engineering methods and development of a description of the system architecture, familiarization with the concept of digital twins and their role in MBSE, study of the MBSSE reference model and its relationship with the process standards of systems engineering, study of the methodological aspects of the system and software integration process. The final chapter of the textbook is devoted to the study of the mathematical foundations of systems engineering. The main aspects of the mathematical theory of systems by A. Wayne Wymore, which became an important stimulus for the development of the model-oriented approach in systems engineering, and modern research, including the apparatus of finite automata by David Harel, the formalism of modeling of discrete systems DEVS, research into category theory as a formal mathematical basis for model-based system design are considered. The authors recommend this course as a basic course for training IT professionals.

Nowadays, in software, electronics and automotive industries, it takes 1.5–2 years for a new product to appear, although it takes 3 or even more years to develop new space instruments. The update of higher education system standards is even more conservative. It takes 10 or more years. Because of this, graduates come into the industry with outdated knowledge. Thus, the problem is to synchronize the processes of updating technologies that ensure the creation of innovative products with educational programs. It can be solved by changing the paradigm of products creation based on the synthesis of deep and interdisciplinary educational, scientific, innovative and production activities with the model-based systems engineering software and methodological tools.The existing model-based systems engineering (MBSE) Software (SW) tools based on the Systems Modelling Language (SysML) are complex and expensive. This chapter raises the question, “Is it possible to make use of MBSE SW and methodological tools available to a wide audience of users?” To answer this question, the following MBSE approaches and tools were researched: Quality Function Deployment (QFD), House of Quality (HoQ), and SysML. Consequently, five methodological tools were developed for application during space instruments design and development life cycle stages. Tools are based on SysML, QFD, HoQ improvements and synthesis, and expressed in theoretical and practical algorithms.The theoretical algorithm determines “What” (input and output data) and “How” (by using which systems engineering (including MBSE) tools are methodically analysed at the certain space instruments life cycle stage. The practical algorithm automates the development of SysML diagrams by using data structured in HoQ models and widely available SW. It reduces the time to develop and update SysML requirements diagrams from several days to some minutes and enables implementation of SysML SW by many users. Altogether, the obtained results constitute the software and methodological toolkit (SMT) called “improved Quality Function Deployment for improved House of Quality”.To disseminate the obtained results, educational materials were created and validated during lectures on systems engineering. Lectures were supplemented with seminars and practicum. Practicum was focused on the design and development (D&D) of nanosatellites in the MBSE paradigm as the single project. The results showed that the developed SMT makes it possible to implement the life cycle of the cyber-physical system in the MBSE paradigm in a cost-effective way and a short time—in 6 months, students developed, conducted synthesis and flight tests of CubeSat-format satellite prototype and prepared the project documentation. In addition, students were motivated to study science, technology, engineering and math disciplines, MBSE tools, and become transdisciplinary specialists.In addition, developed SysML requirements models were applied to examine the “Preliminary Design” product life cycle (PLC) stage of different space projects. Such application allowed quick assessment of project results, identifying non-compliance with requirements, conducting beforehand all necessary corrections and generating correct new product documentation. Taking into account the mentioned advantages of the obtained MBSE SMT, it can be concluded that its usage improves the quality and accuracy of current projects and speeds up the planning process of future analogue projects by up to 60%. Consequently, projects (their life cycle stages) can be realized faster by 5–10%.These results allow to recommend for all interested in the innovative D&D of systems to initiate: formation of educational programs based on the project approach and MBSE paradigm; preparation of MBSE materials for online and offline teaching of students and teachers; development of domestic widely available software for the development of integrated SysML models with HoQ models that assures synchronization with CAE/CAD/CAM systems; support these recommendations for the implementation of the above-mentioned research results at educational organizations of different levels. The future research is focused on the application of the developed MBSE methodological toolkit during design and development with numerical modelling and operation of several CubeSat satellites.KeywordsSystems engineeringMBSEParadigmMethodological toolkitSysMLQFDHoQ

Currently, a wide range of newly designed devices are based on high‐end quantum technologies. To successfully design a quantum system, it is necessary to appropriately address the increasing complexity which exists in the development procedure of the system. A suitable approach to deal with this problem is to employ systems engineering models and integrate them with domain engineering tools. The model‐based systems engineering (MBSE) methodology is commonly used to analyze, design, manufacture, and test various complex systems. In this paper, the MBSE approach is chosen for the development of quantum dot solar cells as a typical quantum system and to deal with the complexity existing in this procedure. The analysis, manufacturing, and verification, validation, and testing (VV&T) for this system are described using SysML in Cameo Systems Modeler software to represent the role of models in this regard. Then a detailed design is performed in MATLAB and integrated with SysML to identify how changing various parameters during the system development process affects the overall system performance. This technique facilitates the communication between different engineering teams and helps to manage the complexity in the entire system lifecycle.

Model Based Systems Engineering (MBSE) is an interesting alternative to traditional systems engineering methods. Instead of using electronic documents to record system information, MBSE uses a unified and coherent system model. Trade-offs are a major element of a space systems engineer's role in early system design. This can be a particularly challenging process in the domain of spacecraft, as the system designs are often very complex and the constraints can be difficult to characterize. There has been little previous research on the use of MBSE as a design space exploration tool or in support of trade-offs. This paper investigates the potential to use MBSE for design exploration and to understand trade-offs, through the creation of a new toolset including a SysML profile. The tool draws on generative design (allowing automatic guided generation of a multitude of design alternatives) and system optimization to rapidly generate and assess new designs using interactive analysis and visualizations. Techniques such as surrogate modelling, genetic algorithms and robustness measurements will be available in the toolset. The toolset was applied to a design scenario aiming to improve the trade off and design selection process for LEO Earth observation satellites. The upcoming ESA TRUTHS space mission was used as a case study and the design process was recorded and compared to a manual design exploration approach. The toolset was found to reduce the design exploration time by 38% to 96%, allow exploration of more designs in an equivalent time and provide better quantification of the relationships present in the design space, all without drops in selected design quality. For now, the toolset can only perform parameter variation in the design exploration and future work is expected to extend this to higher levels of variability. The study also discusses how the MBSE toolset could be applied to other missions, offering the same advantages to all early phase spacecraft designers.

The engineering of space systems is a collaborative, iterative process that integrates various domain-specific viewpoints to represent the final system. To ensure consistency across these viewpoints, the European Space Agency (ESA) employs Model-Based System Engineering (MBSE) and Semantic-Based System Engineering (SBSE) methodologies together to improve digital continuity and interoperability across collaborative space system developments. One significant application of semantic engineering in SE is the ESA MBSE Methodology. The ESA MBSE Methodology provides a standardized approach aligned with the European Cooperation for Space Standardization (ECSS), promotes interoperability across MBSE methodologies and tools, and overcomes integration challenges. ESA MBSE Methodology is the input for the Overall Semantic Modeling for Space System Engineering (OSMoSE) which leverages interoperability in the space community. Case studies, such as the EagleEye Earth Observation mission, demonstrate practical applications, highlighting how semantic models enhance efficiency in complex space systems. This paper discusses the importance of semantics and data management in SE and presents a practical solution derived from the ESA MBSE Methodology.

Combine the key concepts of MBSE with the essentials of their practical application. This tutorial is designed help you leverage model‐based techniques to better meet the engineering challenges of analyzing stakeholder needs and defining system solutions. The tutorial uses a practical top‐down system analysis and design example and exercise to explore the application of layered model‐based systems engineering (MBSE) to system definition challenges. It examines the range of views available to the systems engineer for understanding and communicating design choices and provides a practical introduction to constructing the model and generating each representation. As the analysis and design advances, the engineer will use a wide variety of views drawn from traditional and SysML representations to portray aspects of the model and gain understanding of stakeholder concerns and the design choices to be made. This tutorial works for both new and experienced systems engineers, capability analysts and others working on an integrated project team, and program/project/capability managers. While a basic understanding of the systems engineering process is assumed, the MBSE framework and approach provide the foundation for those not experienced in systems engineering. The detailed discussion of MBSE methodology and model representations including SysML and traditional SE products delivers value for all but the most experienced model‐based systems analysts and engineers. This is an MBSE methods, models, and representations tutorial, not a tool tutorial. The focus is on the practical application of essential MBSE for project success. The concepts taught and methods shared are applicable regardless of the systems engineering tools the attendee may choose.

This chapter examines the life cycle of complex systems in light of the dynamic interconnections among the university, industry, and government sectors. Each sector is motivated in its resource allocation by principles discussed elsewhere in this book and yet remains complementary establishing enduring and fundamental relationships. Industry and government depend upon an educated workforce; universities depend upon industry to spark the R&D which is needed and to sponsor some basic research and much applied research. Government depends upon industry to address operational needs and provide finished products while universities offer government (along with industry) problem solving and problem solving environments. The life cycle of complex systems in this chapter will be examined in this context, providing historical examples. Current examples will then be examined within this multidimensional context with respect to the phases of program and project life cycle management from requirements definition through retirement and closeout of systems. During the explanation of these examples, the advances in research techniques to collect, analyze, and process the data will be examined.

Traditional document‐based practices in systems engineering are being transitioned to model‐based ones. Adoption of model‐based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)’s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.

The complex system engineering in the aerospace domain is undergoing a digital transformation, with Model-Based Systems Engineering (MBSE) serving as the core driving force. By establishing a unified model framework, MBSE has revolutionized traditional development paradigms and significantly enhanced multidisciplinary collaboration and full lifecycle management capabilities. However, MBSE still faces bottlenecks in model standardization, cross-domain integration, and dynamic decision-making. This paper examines MBSE model mappings across key aerospace mission phases: planning, requirement analysis, design, and on-orbit operations. It identifies core AI-empowered scenarios such as intelligent requirement capture, multidisciplinary collaborative optimization, and virtual-physical fusion verification. The study explores the synergistic application of AI and MBSE in aerospace complex system engineering, providing methodological support for the digital transformation and intelligent upgrading of China’s aerospace equipment. The complex system engineering in the aerospace domain is undergoing a digital transformation, with Model-Based Systems Engineering (MBSE) serving as the core driving force. By establishing a unified model framework, MBSE has revolutionized traditional development paradigms and significantly enhanced multidisciplinary collaboration and full lifecycle management capabilities. However, MBSE still faces bottlenecks in model standardization, cross-domain integration, and dynamic decision-making. This paper examines MBSE model mappings across key aerospace mission phases: planning, requirement analysis, design, and on-orbit operations. It identifies core AI-empowered scenarios such as intelligent requirement capture, multidisciplinary collaborative optimization, and virtual-physical fusion verification. The study explores the synergistic application of AI and MBSE in aerospace complex system engineering, providing methodological support for the digital transformation and intelligent upgrading of China’s aerospace equipment.

Those looking to advocate for Model‐Based Systems Engineering (MBSE) in the Systems Engineering field often turn to more established fields that have made a similar transition to models to assure others it will be beneficial. One practice that is often compared to MBSE is Computer‐Aided Design (CAD) from the field of mechanical engineering. However, the adequacy of this comparison is challenged upon a side‐by‐side examination of what MBSE and CAD are. Based on the established definitions, it is evident that while CAD can be considered a method of mechanical drawing, MBSE cannot be described as only a method. MBSE is more than installing and utilizing software, it is a process in and of itself. Comparing MBSE to CAD runs the risk of oversimplifying MBSE and setting up expectations that may not be met. Therefore, while CAD may represent a similar paradigm shift as MBSE in terms of digitalization, it may not serve as an adequate paradigm in terms of adoption and benefits. In this paper, we characterize and compare CAD and MBSE and identify the similarities and differences between them. We use the resulting insights to level the expectations of adopting and using MBSE.

<title>ABSTRACT</title> <p>The International Council on Systems Engineering <ext-link ext-link-type="uri" xlink:href="https://www.incose.org/">https://www.incose.org/</ext-link> is a recognized standards body defining a system engineering knowledge-base, but this knowledge falls short of fully recognizing manufacturing in the Systems Engineering (SE) framework. To be inclusive, Manufacturing needs to join in the initiative of Model Based Systems Engineering to be relevant and succeed in the digital transformation in the field of systems engineering. This paper addresses this need in manufacturing by applying Model Based System Engineering (MBSE) to the identification and management of key characteristics so that a more relevant set of Manufacturing requirements can be introduced into the MBSE construct and help realize manufacturing resilience and become a full SE partner.</p> <p><bold>Citation:</bold> “Identification and Management of Key Characteristics in Product Development Using Model Based Systems Engineering,” In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.</p>

The Prognostic and Health Management (PHM) system of an aircraft has complex structures and diverse functions. It is highly coupled with other systems, such as the avionics system and flight management system. The Model-based Systems Engineering (MBSE) method is effective to support the design and verification of the aircraft PHM system. As a powerful semantic web construction method, ontology has been widely used to express design information, such as the concepts and the relationships between them. However, traditional graphical MBSE models have a natural weakness in transforming into ontology. In this paper, a semantic MBSE method is proposed to support the transform of the ontology model. Firstly, according to the design characteristics of the aircraft PHM system, a meta-model library of the aircraft PHM system is developed to support the design and evaluation. An MBSE modeling method based on requirement analysis, function analysis, logical architecture design, and physic architecture design is applied in the PHM design process. Secondly, the semantic system modeling language KARMA based on “graph, object, property, point, relationship, role, and extension” (GOPPRRE) is used to transfer the graphical MBSE model to the semantic MBSE model, which can be easily transformed to an ontology model. Finally, an ontology based on semantic modeling is developed to describe the MBSE entities and to support MBSE design. In this paper, a case study of an aircraft fuel PHM system is carried out to validate the proposed method. Based on the developed meta-model library, a complete MBSE design process for the aircraft PHM system is realized. And then an ontology model supporting PHM system design is generated from the semantic MBSE model. The MBSE ontology provides a shareable capability to help designers communicate effectively. Quantitative analysis based on ontology is also provided to verify the complexity and scale of the MBSE design process. Moreover, logical reasoning ability can also be provided to support the early requirement traceability for MBSE design. In general, the case study results show the feasibility and effectiveness of the proposed method for the aircraft PHM system design.

The increasing system complexity due to technological advances in recent decades requires the implementation of Model-Based System Engineering (MBSE) languages. While MBSE languages can support in creating detailed and precise system descriptions, requirements, functions, and component interactions, the development of several modelling languages has formed a challenge for system engineers to select a suitable modelling language for complex operations. Since the implementation of MBSE modelling languages is extensive and requires high resources, system engineers need to select the most suitable one for their project. Furthermore, the prepared system models are utilized by different end-users such as system engineers, operators, and marketing professionals. As a result, it is necessary to integrate the perception of end-users in the modelling language selection process. Hence, a decision-making support framework needs to be developed, which will incorporate the opinions of end-users in the selection process. Correspondingly, this study proposes a Multi-Criteria Decision-Making framework aimed to select the most appropriate and practical modelling language. The framework integrates the end-user’s perception in the selection process using the Technology Acceptance Model and reckons the MBSE language features as comparison criteria. To analyze the data collected from the end-users, integration of Multinomial Process Tree modelling and Bayesian inference is developed. The applicability of the proposed model was tested in a ship pilotage operation case study. The results show that the framework can support system engineers during the initial selection process of the MBSE modelling language.

This paper presents an overview of why Model Based System Engineering (MBSE) is the mandatory solution to engineer complex railway systems and subsystems, giving some insights about the complexity and technical challenges. But its main focus will be on how MBSE can be a key enabler on the implementation of an extend enterprise approach in the engineering domain. The case study is on rolling stock systems and brake subsystems developed by the two companies Bombardier Transportation and Knorr-Bremse which are in a customer-supplier relationship.

Conventional disaster response paradigms are fundamentally constrained by reliance on human-centric intelligence, which introduces significant cognitive and heuristic biases, resulting in sub-optimal decision-making and inefficient resource allocation under high-stress, dynamically evolving scenarios. This research posits a novel framework that transcends these limitations by operationalizing a cyber-physical System of Systems (SoS) architecture where a heterogeneous fleet of Uncrewed Systems (UxS) functions as a high-fidelity Digital Twin (DT). The cognitive core of this framework is a Model-Based Artificial Intelligence (MBAI) engine, a synergistic integration of Model-Based Systems Engineering (MBSE) methodologies with advanced AI. This MBAI leverages pre-compiled Pattern Libraries (PL), constraint-based mathematical models, and predictive physics-based and stochastic simulations (ModSim) to explore potential state-space evolutions and derive optimal control policies. The DT provides a real-time, synchronous emulation of the physical environment by assimilating multi-modal data streams from UxS sensor suites and disparate inter-agency systems. The system provides the functional mechanisms to enforce data consistency across a federated architecture, thereby adhering to the single source of truth principle, and meticulously documents data lineage to maintain a forensically sound chain of custody for all collected data objects. Architecturally, the complex UxS fabric is conceptualized using an SoS methodology and decomposed via the rigorous Systems Engineering (SE) Vee model to ensure robust integration, verification, and validation of all subsystems. Operationally, the MBAI-driven DT autonomously establishes comprehensive situational awareness, performs multi-objective optimization to orchestrate a minimum viable plan, and dynamically allocates assets to mitigate impacts in high-vulnerability sectors. The system is designed to interface directly with critical infrastructure nodes such as power, communication, and transportation to model interdependencies, predict and preempt cascading failures, and reinforce Emergency Support Functions (ESF). Initial field deployment of physical UxS assets focuses on high-resolution geospatial data acquisition to calibrate and validate the DT's underlying models. The longitudinal, high-fidelity data generated throughout the response lifecycle (from heroic to disillusionment phases) is invaluable for post-hoc forensic analysis, model refinement, and provides a quantitative, auditable basis for Federal Emergency Management Agency (FEMA) assessments and insurance claims adjudication, ensuring verifiable data provenance and maintaining a strict chain of custody for evidentiary purposes. This extensible framework is agnostic to disaster typology and is engineered to enhance operational resiliency across a spectrum of catastrophic events, from hydrometeorological and geophysical events to technological and anthropogenic crises.