Model-Based Systems Engineering Research Articles

Model-Based Systems Engineering: Pioneering New Era in Astrophysics Mission Design

Model-Based Systems Engineering: Pioneering New Era in Astrophysics Mission Design

Semantic-based systems engineering for digitalization of space mission design

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.

Applying MBSE to Optimize Satellite and Payload Interfaces in Early Mission Phases

The use of model-based systems engineering (MBSE) has been increasingly explored recently in industries that require multi-discipline engineering coordination. In the European space industry, applying MBSE for the engineering of space systems has been an ongoing undertaking on many missions. In the following paper, the MBSE activities in CAMEO conducted during the A/B1 phases of a typical Earth observation satellite engineered by Airbus are discussed in detail in the form of a case study. The analyses shown are based around the modeling of the spacecraft electrical interfaces in CAMEO. This model was used to automate electrical interface control documents (EICDs) and enable the control of electrical interface development. These methodologies were further put in the context of Airbus’ satellite design processes to assess the benefits of an MBSE approach to the current electrical interface engineering procedure and the potential for the reuse of CAMEO models between satellite projects. The reduction in system engineering effort through the reuse of models to modularly create similar satellite systems for efficient concept evaluation and comparison is a clear benefit.

A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production

A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production

Innovative Vehicle Design Processes Based on the Integrated Framework for Abstract Physics Modeling (IF4APM)

In industrial vehicle design processes, most companies have implemented model-based systems engineering (MBSE). As a consequence, design processes are nowadays not driven by documents, but by digital models of the vehicle to be developed and its components. These models exist on different levels of abstraction. The models on the requirements level are already well defined as well as the models of the defined product behavior and product properties. In recent years, the specification of models on the level of product functions was largely clarified, and elaborate frameworks already exist. However, this is not yet true for the level between functions and definite properties; this level can be referred to as "abstract physics". The enormous importance of this level, which, amongst others, can represent the physical effect chains which allow a vehicle component to function, is expressed by several researchers. Several research works aim at specifying models on this level, but, until now, no general consensus can be identified, and the existing model specifications are less appropriate for the early stages of vehicle design. This paper explains an Integrated Framework for Abstract Physics Modeling (IF4APM), which incorporates different perspectives of abstract physics and is suited for the early phases. The explanation is based on typical components of several kinds of vehicles. The main advantages of the proposed approach are the consistent interconnection of abstract product models, the clearness and understandability of the resulting matrices, and the aptitude to be used in the early phases of a vehicle design process.

Engineering Systems with Standards and Digital Models: Development of a 15288-SysML Grid

The paradigm shift that has spurred the fourth industrial revolution, in what is termed Industry 4.0, has ushered in the need to adopt digital technologies throughout the worldwide industrial base to support system design efforts. The adoption of digital technologies with a digital enterprise and the creation of cyber–physical systems are central tenets of Industry 4.0 and directly support profitable business models, improvements in efficiency, and ensure durable quality for the modern industrial base. However, the techniques for engineering systems require new, improved, digital life cycle process models if Industry 4.0—and the goals for its integrated systems—are to be realized. The development of a technique that improves the life cycles for systems within the digital enterprise is required. The 15288-SysML Grid described herein supports the Industry 4.0 paradigm and its associated digital enterprise. This is accomplished through (1) the application of a modern life cycle process model (i.e., the adapted diamond); (2) the utilization of international standards for systems; and (3) the adoption of the four fundamental aspects of system design supported by model-based systems engineering (MBSE) and the systems modeling language (SysML).

Application of data element mapping and analysis for system definition to enable model‐based systems engineering

AbstractIn the connected age of the model‐based enterprise and model‐based systems engineering (MBSE), new systems engineering tools are needed to move from a functional, document‐centric, hierarchical view of data and information to the individual units of data or data element level view. Data element mapping and analysis (DEMA) is a technology‐agnostic analytical methodology that combines traditional functional analysis techniques, systems engineering elicitation practices, and novel data mapping techniques to provide a holistic view of a system's data and information flows at the individual units of data (data element level). In this research, DEMA was utilized to enable an enhanced system definition for the development of a verification and validation process as applied to a modeling and simulation environment. DEMA uncovered and visually mapped the hidden flow of approximately 1600 data vessel occurrences as inputs or outputs to 79 functional activities in 23 disparate storage locations. The results reveal that DEMA is a practical tool for both improving existing systems and defining new systems. The data element level view captured by DEMA can be used to define the interconnections between the system elements that are input to systems modeling language (SysML) models. Therefore, DEMA is a necessary and novel tool that can be used to enable system digitalization.

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Design, Technical Development, and Evaluation of an Autonomous Compost Turner: An Approach towards Smart Composting

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future.

Development of a Body Weight Support System Employing Model-Based System Engineering Methodology

Partial body weight support systems have proven to be a vital tool in performing physical therapy for patients with lower limb disabilities to improve gait. Developing this type of equipment requires rigorous design process that obtains a robust system, allowing physiotherapy exercises to be performed safely and efficiently. With this in mind, a “Model-Based Systems Engineering” design process using SysML improves communication between different areas, thereby increasing the synergy of interdisciplinary workgroups and positively impacting the development process of cyber-physical systems. The proposed development process presents a work sequence that defines a clear path in the design process, allowing traceability in the development phase. This also ensures the observability of elements related to a part that has suffered a failure. This methodology reduces the integration complexity between subsystems that compose the partial body weight support system because is possible to have a hierarchical and functional system vision at each design stage. The standard allowed requirements to be established graphically, making it possible to observe their system dependencies and who satisfied them. Consequently, the Partial Weight Support System was implemented through with a clear design route obtained by the MBSE methodology.

Architecture Preliminary Design and Trade-Off Optimization of Stratospheric Airship Based on MBSE

System architecture design is crucial for forward design in aerostat system engineering, yet a comprehensive research framework has been lacking. This paper presents a new method for stratospheric airship architecture preliminary design and optimization trade-off, grounded in Model-Based Systems Engineering (MBSE) theory. Firstly, a requirement analysis for a stratospheric airship is conducted using SysML, leading to the analysis and acquisition of the airship’s mission architecture design. Additionally, a multidisciplinary coupling simulation platform is developed with MATLAB, and the architecture preliminary design’s Pareto front is derived using the NSGA-II algorithm. Finally, based on the Pareto optimization set, the TOPSIS algorithm is applied to derive the optimal architecture preliminary design scheme for the airship. The optimization results validate the accuracy of the architecture preliminary design obtained from the requirement analysis, the reliability of the multidisciplinary coupling simulation platform, and the feasibility of the optimization algorithms. This comprehensive study spans the requirement analysis to the optimal architecture scheme, providing theoretical reference and design guidance for the forward design of airship systems engineering.

Automation of the process of testing on-board systems of unmanned aerial vehicles

The article describes the experience of applying the methodology of Model-Based Systems Engineering (MBSE) in the organization of ground tests of the on-board equipment complex of unmanned aerial vehicles at Kronshtadt JSC. This methodology was successfully used in the development of various complex control systems such as embedded control systems, digital signal processing, computer vision, radar and communications. The analysis of the on-board equipment testing process using an existing ground test bench was performed, and a method for automating its operation has been proposed. The calculation of economic efficiency was performed.

Automatic generation of system model diagrams driven by multi-source heterogeneous data

The demand for functionality, performance, safety, and reliability in complex product domains is continuously increasing, leading to growing product complexity. Traditional document-based systems engineering approaches face numerous challenges in terms of communication efficiency, traceability, and maintenance when dealing with complex product system design. Consequently, Model-Based Systems Engineering (MBSE) has been widely adopted. To improve the efficiency of system modelling, an effective method is needed to automate the process by reusing existing design resources. This study proposes a knowledge graph-based approach for the automatic generation of system model diagrams, consisting of three main steps. Firstly, a system model graph is constructed using an existing system model repository. Then, the required modelling elements are extracted from design documents, and reusable model elements are obtained based on the system model graph. Lastly, a mapping relationship is established between system model elements extracted from multi-source heterogeneous data and SysML metamodel, enabling the automatic generation of system model diagrams based on SysML views. This approach effectively leverages the information from the existing system model repository and achieves automated mapping conversion from multi-source heterogeneous data to the SysML system model. A communication satellite case study is presented to demonstrate the capability of this method.

The Effects of the Assessed Perceptions of MBSE on Adoption

AbstractTraditional document‐based systems engineering (SE) practices have proven inadequate to address modern systems' increasing complexity, leading to shortcomings in SE outcomes. To address these limitations, model‐based systems engineering (MBSE) has emerged with an emphasis on the system model as the primary SE artifact. Despite its potential, MBSE has not achieved widespread adoption. This study explores MBSE adoption challenges through the lens of the diffusion of innovations theory to identify factors hindering its adoption. The study's methodology includes a survey distributed to SE professionals focusing on perceptions of attributes of MBSE identified by the diffusion of innovations theory, current use of models and MBSE, and basic demographic information. Results highlighted that respondents recognize the relative advantage of MBSE in improving data quality and traceability, but perceived complexity and compatibility with existing practices still present barriers to adoption. Subpopulation analysis reveals that those who are not already involved in MBSE efforts face the additional adoption obstacles of limited trial opportunities and tool access (chi‐squared test of independence between these populations resulted in p = 0.00). The survey underscores the potential for closer alignment between MBSE and existing SE methodologies to improve the perceived compatibility of MBSE. Future studies would benefit from examining additional variables identified by the diffusion of innovations theory, incorporating control questions to differentiate between perceptions of SE generally and MBSE specifically, identification of better methods to assess current MBSE use by participants, and measures to broaden the participant scope. Finally, the imminent introduction of SysML v2 presents a unique opportunity to reshape perceptions of MBSE to improve the adoption rate of MBSE and enhance its accessibility among SE professionals.

A Rapid Review of How Model‐based Systems Engineering is Used in Healthcare Systems

AbstractThis study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability.

Early Validation of SysML Architectures by Extending MBSE with Co‐Simulation using FMI and SSP

AbstractDue to complexity increases in modern systems and the digitalization paradigm shift, industrial development requires the integration of new technologies and methods to keep product quality high while reducing time to market. One emerging paradigm in the Systems Engineering (SE) discipline is ModelBased methods and technologies, and correspondingly Model‐Based Systems Engineering (MBSE) is seeing increased adoption. With mature MBSE application, several benefits can be expected from the availability of models, even from the very early stages of development, enabling increased communication clarity, cross‐domain collaboration, traceability, and analysis. Notably, MBSE enables (Co‐)simulation even at the early stage of architecture/design by leveraging model‐based capabilities. Co‐simulation specifically enables a smooth and seamless integration of different models defined across layers of abstraction, for example, system logical architecture and system physical architecture. However, while MBSE is assisting with many aspects of development it is still a predominantly isolated set of activities throughout the development, especially on the left‐hand side of the traditional V‐model. In this work we discuss the status of Co‐simulation in industrial MBSE and list several existing challenges, then we propose a novel framework for implementing Co‐simulation and exemplify using a real scenario how we might address the observed challenges. The framework hinges on the newly proposed SSP standard and extends the currently industrially adopted FMI (version 3) standard through embedding the FMI file format using various scripts, demonstrated in the Python language for this paper. Finally, we propose a set of recommendations for future investigations to strengthen Co‐simulation in industrial MBSE.

A Method for Human Systems Integration Requirements within Model Based Systems Engineering

AbstractThe Department of Defense (DoD) employs broad human factors requirements across various applications, resulting in a universal application of the same standards to a multitude of DoD acquisition systems. In unconventional warfare, specifically within missions conducted by US Special Operations Command (USSOCOM), operators face intensified workloads and domain‐specific challenges that current human factors considerations do not adequately address. The objective of this paper aims to introduce a novel framework, the Relational and Technological Capstone (RTC), designed to expand existing Human Systems Integration (HSI) requirements. The objective is to enhance the consideration of human factors in USSOCOM missions by addressing the unique challenges posed by intensified workloads and domain‐specific ontologies. The RTC employs a methodology‐driven approach utilizing both architectural and parametric diagrams. It integrates with Model Based Systems Engineering (MBSE) to improve the design of human‐system interactions, incorporating a Special Operations Task List and Performance Shaping Factors (PSFs) into aggregated parametrics. The results of this paper demonstrate the efficacy of RTC within MBSE, showcasing its value through improved design processes and as a foundation for new programs. The containment tree format aids in developing USSOCOM MBSE and opens possibilities for automation tools. Continual use of RTC contributes to the maturity of MBSE models and diagrams, fostering the evolution of a federation‐of‐models and Program of Record standards. This not only benefits subsequent SOCOM programs and projects but also facilitates cross‐cooperation with other nations in optimizing special operation acquisitions. The ultimate goal is to center the RTC around the operator, ensuring compatibility and optimization across cooperative nations' special operation acquisitions.

Hyundai's Modular MBSE Approach to ‘Purpose Built Vehicle’ Architecture Development

AbstractThis paper discusses an ongoing effort at Hyundai to develop a model‐based systems engineering (MBSE) methodology for cross‐domain vehicle architecture development that is practical at the enterprise level. Our approach follows a modular modeling process that complements MBSE. In the age of smart mobility, automobile systems are indeed interdependent elements of a system of systems (SoS). Connectedness favors numerous mobility features that emerge due to the interfaces among these constituent systems. Managing emergent product line variations, changing consumer demands coupled with faster market response require manufacturers to modularize their architecture development processes. This can help scale MBSE across vehicle programs. This study proposes a modular system architecture approach for developing Hyundai's purpose‐built vehicle (PBV) concept that maintains a link to the legacy vehicle breakdown structures already in use. Using the Arcadia method, the ‘to‐be’ developed electric vehicle is expressed as a hierarchical functional partitioning of the subsystem modules. A physical architecture is defined as a solution to the functional partitioning based on an existing vehicle breakdown in a combined ‘top‐down/bottom‐up’ workflow thus capturing a realistic system decomposition. In the SoS hierarchy, the PBV is at the top level and is partitioned into multiple levels of nested subsystem architectures owned and developed by designated module teams in a distributed modeling environment. Results of the preliminary architecture modularization effort indicate significant potential for benefits over classical architecture modeling such as iterative knowledge capture, enhanced reusability across projects, products and programs, and distributed vehicle performance development across the extended MBSE enterprise, which includes the tier 1/2 suppliers.

MBSE for Real World Teams

AbstractWith the release of the OMG Systems Modeling Language (SysML) (Object Management Group, 2007) there was a surge of enthusiasm for model‐based systems engineering (MBSE). Expectations were high. Cumbersome, fragmented documents would be completely replaced by coherent, fully integrated models. The models would serve as the “Single Version of the Truth” (later transitioning to models being part of the “Authoratative Source of Truth”).While progress has been made, this utopian vision has largely failed to materialize. Certainly tool and language limitations have been a factor. However, the bigger factor in many communities has been human factors challenges. In our enthusiasm to propagate the MBSE vision, we forgot to perform the most basic systems engineering task: stakeholder analysis. This paper will introduce some of the basic human factors issues that were overlooked. The paper will then discuss three concrete cases in which a community was struggling with the abstract, radical change approach and how the teams involved have modified this approach and tooling to make it more practical and successful for those communities. Finally, the paper will conclude with some recommendations to consider when undertaking the introduction of MBSE methods in a new community.

Model‐Based Systems Engineering (MBSE) Application in Nuclear Power Plants (NPP)

AbstractCompanies in the nuclear power sector are constantly being challenged to improve their safety and reliability due to increasing complexity arise from evolving safety regulations, long production life, interdisciplinary collaboration, and the need for analyzing the impact of the changes in an operational life cycle. Recognizing these challenges, the paper proposes a transition to Model‐Based Systems Engineering (MBSE) as a transformative solution to improve the management of such complex systems. With this objective, this paper presents a workflow implementation that demonstrates the MBSE methodologies to define a concept model, system architecture, impact analysis, safety and reliability analysis, and operational decision‐making of Nuclear Power Plants (NPP). The paper concludes that MBSE provides a potent approach to managing NPP by employing graphical models to develop interrelated systems that has strong adaptability to heterogeneous environments and regulatory changes. The simulation results demonstrated an NPP life cycle, impact analysis, and a test case for model‐based safety and reliability analysis for regulatory compliance, operational efficiency, balance safety, and informed decision‐making in NPP. The study also leads to a number of interesting directions of future work such as synchronization through Product Lifecycle Management, integration with Building Information Modeling, Model‐Based Commissioning/Decommissioning, and Model‐Based Cyber System Security tailored for nuclear power systems.