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This paper investigates using Large Language Models (LLMs) within Model Based Systems Engineering (MBSE) as the basis for generative design tools for spacecraft. This study has developed tooling for automatically generating system architecture, functions, modes, and components from an initial requirement set. Specifically, a Python tool was developed to couple the Capella MBSE tool to an LLM in order to facilitate a rapid generative design process. The approach was tested by application to three system design tasks: a European Space Agency Earth observation mission, a CubeSat payload design, and a masters’ degree level group design for an Earth observation spacecraft. For each, generated outputs were evaluated against those produced by technical designers. It was found that the generation of system modes and components was of good quality, providing high traceability and alignment against requirements, and also providing generated architectures that in some areas were more detailed than human-generated equivalents. Further development could provide spacecraft system engineers with an ‘AI design assistant’, as human input is still at the centre of the process and appears necessary to ensure a high-quality output. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided. • a novel systematic approach for designing interlaced unmanned spatial systems (IUST). • Introduced interlaced unmanned spatial systems (IUST) as a novel class of Space Robots. • Proposing a coherent Design system architecture includes multiple system aspects by optimizing the design system's Cybernetic Structure. • Modeling the systems design process architecture from a systems engineering perspective using Systems Engineering Modeling Language (SysML). [ABSTRACT FROM AUTHOR]

This study introduces an automated test bench designed to optimize flocculation-thickening processes in the wastewater treatment industry. Addressing current challenges in operational efficiency and cost reduction, the test bench employs Model-Based Systems Engineering (MBSE) principles, leveraging SysML modeling within the CESAM framework. By integrating advanced technologies, including PLC programming and a closed-loop control system, this bench provides precise and efficient testing under varying operational conditions. Economic implications are explored, demonstrating the cost-effectiveness of optimized flocculation processes, which reduce chemical use and operational expenditures while enhancing water clarity and sludge management. The system's 3D modeling enables detailed simulations, aiding in both research and pedagogical applications. This platform highlights the potential of MBSE in creating scalable, robust solutions that contribute to sustainable water management. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework aimed at integrating Digital Twins and cyber-physical production systems into the energy management of manufacturing facilities. To address the challenges of rising energy costs and environmental impacts, this framework combines digital modeling and customized energy management for direct manufacturing operations. Through a review of the existing literature, essential components such as physical models, a data platform, an energy optimization platform, and various interfaces are identified. Key requirements are defined in terms of functionality, performance, reliability, safety, and additional factors. The proposed framework includes the physical system, data platform, energy management system, and interfaces for both operators and external parties. The goal of this framework is to set the basis for allowing manufacturers to reduce energy consumption and costs during the lifecycle of assets more effectively, thereby improving energy efficiency in smart manufacturing. The study highlights opportunities for further research, such as real-world applications and sophisticated optimization methods. The advancement of Digital Twin technologies holds significant potential for creating more sustainable factories. [ABSTRACT FROM AUTHOR]

The design of complex aerospace systems requires a broad multidisciplinary knowledge base and an iterative approach to accommodate changes effectively. Engineering knowledge is commonly represented through engineering analyses and descriptive models with underlying semantics. While guidelines from systems engineering methodologies exist to guide the development of system models, creating a system model from scratch with every new application/system requires research into more adaptable and reusable modeling frameworks. In this context, this research demonstrates how a physics-based multidisciplinary analysis and optimization tool, SUAVE, can be leveraged to develop a system model. By leveraging the existing physics-based knowledge captured within SUAVE, the process benefits from the expertise embedded in the tool. To facilitate the systematic creation of the system model, an ontological metamodel is created in SysML. This metamodel is designed to capture the inner workings of the SUAVE tool, representing its concepts, relationships, and behaviors. By using this ontological metamodel as a modeling template, the process of creating the system model becomes more structured and organized. Overall, this research aims to streamline the process of building system models from scratch by leveraging existing knowledge and utilizing an ontological metamodel as a modeling template. This approach enhances formal knowledge representation and its consistency, and promotes reusability in multidisciplinary design problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The economic impact of inefficient traffic control systems is significant owing to prolonged commute durations, and increased energy consumption. Traffic signal control systems (TSCSs) significantly influence traffic flows at intersections. Therefore, adaptive TSCSs (ATSCSs) that can adjust to traffic conditions in real‐time have been proposed as more efficient alternatives. However, the expensive implementation of these systems highlights the need for judicious investments in appropriate technologies and infrastructure. Therefore, a comprehensive technology roadmap should be built that guides the future development of traffic control and the infusion of technologies to address traffic needs. Additionally, as ATSCSs are developed and managed by local governments, the perspective of a government‐led technology roadmap is required to guide the roadmap development and implementation. Although studies have explored technology roadmaps across numerous sectors, the viewpoint of roadmap development guided by government entities is frequently neglected despite the role of these entities in shaping technological policies, underwriting research and development initiatives, and driving nationwide innovation strategies. In this study, a comprehensive framework is proposed for developing technology roadmaps tailored for systems and technologies led by governmental entities. This framework has been adapted from the Advanced Technology Roadmap Architecture (ATRA) and brought original adjustments thereby addressing the research gap. The study also presents strategic recommendations for the ATSCS implementation in South Korea, integrating systems engineering principles for a holistic approach to technological advancements. The framework can be replicated to serve as a guide for governments seeking to implement effective and efficient technology roadmaps for public infrastructure systems. [ABSTRACT FROM AUTHOR]

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology. [ABSTRACT FROM AUTHOR]

Robust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models. [ABSTRACT FROM AUTHOR]

Over the past 20 years, there have been high-profile examples of Department of the Navy (DON) unmanned undersea vehicle (UUV) programs with expensive cost overruns and late deliveries. While best practices exist to account for cost and schedule risk, this article presents a practical approach to assessing the likelihood of success through model-based conceptual design (MBCD) within a real-world problem context. The author applies model-based systems engineering (MBSE) to quantify the impact MBCD has on the success of a critical UUV software update when applied prior to expensive design, test, and delivery. First, the scope is framed with a realistic narrative, then the existing UUV development architecture is modeled to set the existing DON process in context. Next, a formal time and cost model is simulated to set baseline values for the modeled scenario, including user-focused MBCD. Finally, simulation and analysis of four scenarios capturing varying levels of rework demonstrate a significant reduction in time and cost. While narrowly focused on a UUV development scenario, future work can generalize the approach and quantify the impact of these methods to quantify, track, and mitigate program risk across DON acquisition programs. [ABSTRACT FROM AUTHOR]

Integrated photovoltaic-fuel cell (IPVFC) systems have the potential to contribute to sustainable energy production for grid and off-grid applications. While there are studies focusing on fundamental science and designs for IPVFC systems, there are few studies that have focused on the manufacturability of IPVFC systems, which is certainly the pathway for the commercialisation of the systems. This study explores manufacturing strategies that can be considered for exploiting a family of eleven IPVFC systems. A survey and systems thinking approach were used to investigate the potential modularisation and supply chain management of the systems to achieve an optimal lean and agile manufacturing strategy. Results show that the Photovoltaic-Thermal-Separate Converter-Inverter-Battery System received 25% of the responses. The optimal manufacturing strategy depends on the overall business strategy of the firm. The 17% preference for System 1 was significant compared to four members of the family of IPVFC systems (Systems 2, 6, 8 and 9) that received only 2% of the responses, and there is a likelihood that the demand for System 1 will be among the top 42% of the total demand of all the systems. Overall, this study provides new insights into how the family of IPVFC systems can contribute to realising greater access to cleaner energy, by extension contributing to net-zero efforts using solar energy and solar hydrogen. [ABSTRACT FROM AUTHOR]

Formal requirements modeling and traceability management are essential for effectively implementing Model-Based Systems Engineering (MBSE). However, few studies have explored the integration of requirement modeling, traceability management, and verification within MBSE-based systems engineering methodologies. Moreover, the predominant modeling language for MBSE, SysML, lacks sufficient capabilities for requirement description and traceability management and for depicting physical attributes and executable capabilities, making it challenging to verify functional and non-functional requirements collaboratively. This paper proposes an integrated approach for requirement modeling, traceability management, and verification, building on the previously proposed integrated modeling and the simulation language called X language. Our contributions primarily include defining the ReqXL specification for MBSE-oriented requirement modeling based on X language, proposing an algorithm for automatically generating requirement traces, and an integrated framework for requirements modeling, traceability management, and verification was developed by combining the X language with ReqXL. These functionalities were customized on the self-developed integrated modeling and simulation platform, XLab, which is specifically tailored for the X language. Furthermore, we showcase the efficacy and promise of our approach through a case study involving the design of an aircraft electrical system. [ABSTRACT FROM AUTHOR]

Pipe design in 3D is typically characterized by competing objectives, since the different design objectives, such as the reduction of length, weight, number of bends, manufacturing cost, and overall angle sum, are examples for such competing design goals, where one goal is often at the expense of the other. The origin of these competing design goals lies in the highly coupled problems of finding a permissible and collision-free pipe path in a complex 3D geometry and the physical properties of the path found. Because of the complex physics and geometry, these couplings are highly non-linear and mostly accessible via simulation only. The underlying pipe design optimization problem can thus not be solved explicitly and is tackled instead with a multi-disciplinary search procedure. Since the trade-offs between different competing evaluation objectives are often not known in advance, an automated design space exploration can be performed to generate different pipe designs, leading to well-informed design decisions by human experts. Such a design space exploration is shown and discussed using the pipework in a mounting rack in an Airbus A320 main landing gear bay. A total of 144 valid designs are generated, out of which the best in each criteria and the pareto-optimal solutions are automatically selected. Compared to the manually created Airbus A320 series solution, up to |$10.4\%$| of the pipe length or up to |$16.9\%$| of the bends can be saved using the same fixings and connection points, demonstrating both the feasibility and the industrial applicability of the automated toolchain. [ABSTRACT FROM AUTHOR]

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research. [ABSTRACT FROM AUTHOR]

The global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development. [ABSTRACT FROM AUTHOR]

This paper looks at the bacteria chemotaxis control process utilizing the System Modeling Language (SysML) to leverage well‐defined and proven engineering tools for architecting, analyzing, and refining complex systems. It proposes a new methodology called reverse‐engineering object‐oriented systems engineering method (RE‐OOSEM) that converts descriptive biology research information into descriptive systems engineering information. It utilizes SysML and model‐based systems engineering (MBSE) to capture system architecture from biological system knowledge and inputs them into systems engineering tools. From an engineering point of view, this allows greater insight into how biological systems operate and suggests how much model detail is required to uncover a top‐down system understanding. RE‐OOSEM methodology guides the SysML chemotaxis control capture process. SysML syntax is used instead of biological syntax to facilitate biological chemotaxis control system analysis from an engineered system point of view. The model can act as a scaffolding to help uncover system function, the relationships of system components and processes, and bioinformatic phenotype and genotype correlation. An executable MathWorks Stateflow chemotaxis control process model based on the SysML architectural model is included. The results show the following engineering perspective observations. (1) Several control components are not dedicated but are available and utilized when needed. (2) Individual chemoreceptors act together as a sensor array. (3) Phosphate groups act as a signaling mechanism. (4) Methylation via CH3 groups of the chemoreceptor results in sensitivity adaptation. (5) Closed‐loop control collaboratively utilizes ligand bonding, phosphorylation, and methylation. (6) Timing relationships of the control subprocesses give insight into the system's architecture. [ABSTRACT FROM AUTHOR]

Current workflows for creating simulations of ground vehicle systems are non-standardized and full of bottlenecks and inefficient point-to-point communication. This paper proposes the use of a SysML reference architecture to act as the single source of information for system-level simulation creation. SysML is already used to create reference architectures for system design, but extending it to the simulation domain will require special considerations, including how to represent models at different levels of fidelity, how to have an implementation-independent view of control algorithms, and how to link simulation artifacts to their description model counterparts. This paper details the additional challenges of managing simulations, defines a new simulation perspective for a SysML reference architecture, describes methods for mapping between the simulation and physical perspectives, and discusses the organization of a reference architecture for ground vehicle simulations. The benefits of using a SysML reference architecture for system-level simulation management include having a dynamic and centralized representation of existing simulation components, a standardized architecture to facilitate the creation of new simulations, and streamlined communication with all teams involved in creating simulations, reducing the overall cost of generating system-level simulations of ground vehicles. [ABSTRACT FROM AUTHOR]

This paper presents a method for creating the system architecture of existing industrial objects based on Model-Based Systems Engineering (MBSE) principles. The method aims to form a digital representation of physical objects, which is crucial in the digital transformation of industrial enterprises. It allows for the accurate reflection of all components, processes, functions, and interrelationships within an object. The methodology includes stages of data collection, structuring, development of ontological models, and the integration of a comprehensive system architecture into the digital space. This method was tested using a small hydroelectric power plant, revealing its key advantages and disadvantages and identifying areas for further improvement. The main findings indicate a significant improvement in understanding the system architecture for scenario modeling and digital operation of the objects. Despite challenges such as the need for multiple iterations and high data requirements, the methodology demonstrates the potential for applying MBSE in the digital transformation of existing industrial objects. [ABSTRACT FROM AUTHOR]

The whole train design of an Electric Multiple Unit (EMU) involves multiple domains and scenarios, thus requiring comprehensive consideration of various factors during the design process. Traditional design methods often utilize text-based approaches to model systems; however, such documentation-based designs often suffer from semantic heterogeneity, inconsistent data sources, and also struggle to provide a more intuitive overview of the overall design process. To address these issues, this paper proposes a method based on ontology–knowledge collaborative drive to achieve integration of the overall EMU design. Firstly, we employ the System Modeling Language (SysML) to construct the Model-Based Systems Engineering (MBSE) model of the EMU, establishing functional and physical architecture element models, with the EMU MBSE model serving as input. Subsequently, in the requirement model, architecture model, and traceability model, we utilize top-level ontology to construct the EMU ontology framework in a top-down manner. Lastly, leveraging the Neo4j database, we employ a knowledge graph (KG) approach to fill domain knowledge into each model in a bottom-up manner, thereby realizing the ontology–knowledge collaborative drive for the overall EMU design construction. The effectiveness of the proposed method is validated using the EMU Passenger Information System (PIS) and Traction transformer System (TS) as examples. [ABSTRACT FROM AUTHOR]

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method's effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development. [ABSTRACT FROM AUTHOR]

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases. [ABSTRACT FROM AUTHOR]

Purpose – Model-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development. Design/methodology/approach – The paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence. Findings – Firstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications. Research limitations/implications – Because of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized. Practical implications – This paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization. Originality/value – This paper fulfills an identified need to understand the current application status and future development trends of MBSE.

Abstract Due to a lack of resources, rural communities often face challenges when planning catastrophic events. This project involved applying systems thinking and model-based systems engineering to develop a proof-of-concept, multi-method computer simulation and then determining whether the simulation could be used to assess the efficacy of disaster planning approaches on health outcomes in rural communities, as a function of primary healthcare. The project focus was a rural or non-urban healthcare system experiencing a natural hazard. Both system dynamics and discrete event models were incorporated to represent subsystem operations, crucial disaster responses, as well as three key response systems: public health, emergency management, and healthcare. The subsystem models included several components: policies/procedures, communications, resources, exercises/drills/training, healthcare space and staff, and the flow of affected people into and through the system. The combined simulation can serve as a first step to a more comprehensive approach to helping rural communities achieve more efficient and effective healthcare planning for disaster responses.

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies. [ABSTRACT FROM AUTHOR]

A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system‐software modeling gap by integrating computational, software‐related, and model execution capabilities into OPM‐based conceptual modeling, resulting in a holistic unified executable quantitative‐qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web‐based collaborative conceptual OPM modeling framework. As a proof‐of‐concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high‐level architecting to detailed, discipline‐oriented design. Such a framework is a key to agile yet robust future development of software‐intensive systems. [ABSTRACT FROM AUTHOR]

The specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and evolving systems with complex interactions when following traditional development processes. To address this research gap, we propose a technique that starts with the early identification of validation concerns from a stakeholder perspective, which we use to systematically design tests that drive a scenario‐based modeling and analysis of system requirements. To ensure complete and consistent requirements and test specifications in a form that is required in automotive development projects, we develop a Model‐Based Systems Engineering (MBSE) methodology. This methodology supports system architects and test designers in the collaborative application of our technique and in maintaining a central system model, in order to automatically derive the required specifications. We evaluate our methodology by applying it at KOSTAL (Tier1 supplier) and within student projects as part of the masters program Embedded Systems Engineering. Our study corroborates that our methodology is applicable and improves existing requirements and test specification processes by supporting the integrated and stakeholder‐focused modeling of product and validation systems, where the early definition of stakeholder and validation concerns fosters a problem‐oriented, iterative and test‐driven requirements modeling. [ABSTRACT FROM AUTHOR]

Technology roadmapping is an important yet challenging strategic process involving inter‐disciplinary knowledge and requiring key decision‐makers participation. To improve and facilitate this activity, a concept of model‐based technology roadmapping has been proposed in the literature as an application of model‐based systems engineering practices to the strategic planning domain. At the same time, many modern complex technical systems are required to be not only functional, efficient, and reliable, but also useful, usable, and desirable, which shifts the focus of planning to the user experience (UX). In this paper, we developed a Domain‐specific language that uniformly represents key concepts related to the four levels essential for strategic planning in the user‐oriented business context: market, UX design, engineering design, and technology. A collaborative interactive software was developed to support the proposed methodology. As a case study, a sustainable transformation of the automotive sector was tested and implemented in the software. [ABSTRACT FROM AUTHOR]

The Cyber Evaluation and Management Toolkit (CEMT) is an open-source university research-based plugin for commercial digital model-based systems engineering tools that streamlines conducting cybersecurity risk evaluations for complex cyber-physical systems. The authors developed this research tool to assist the Australian Defence Force (ADF) with the cybersecurity evaluation of complicated systems operating in an increasingly contested and complex cyber environment. This paper briefly outlines the functionality of the CEMT including the inputs, methodology, and outputs required to apply the toolkit using a sample model of the process applied to a generic insider threat attack. A face validity trial was conducted on the CEMT, surveying subject-matter experts in the field of complex cybersecurity analysis and risk assessment to present the generic case study and gather data on the expected benefits of a real-world implementation of the process. The results of the face validity broadly supports the effectiveness and usability of the CEMT, providing justification for industry research trials of the CEMT. [ABSTRACT FROM AUTHOR]

This paper presents how a model‐based system engineering approach was used to introduce to master students the strength of and the challenges with a Product Lifecycle Management (PLM) system. The students were introduced to use various authoring applica?ons to build the product content to be managed by a PLM system. [ABSTRACT FROM AUTHOR]

The current state of the practice of Model‐Based Systems Engineering (MBSE) methodologies, system specification models, and executable system simulations lack the capability of simulation execution within a single system specification model with the fidelity required for time‐dependent simulation towards the design, analysis, and optimization of complex software‐based systems. In this paper we develop and demonstrate a modular simulation‐based MBSE approach capable of filling this gap. To accomplish this within a single system specification model we embed a combination of modeled simulation activities, opaque behaviors, and simulation specific functions to execute and manage the time‐dependent simulation variable arrays. We applied our MBSE approach on the design and analysis of a hybrid on‐prem/cloud data system example that meets the complex, software‐based, and time‐dependent requirements this approach was built to solve. The results show that our approach produces a modular and time‐dependent simulation‐enabled system specification model that accurately estimates cloud‐based system performance and storage cost as a function of time. Emergent system behaviors observed from the simulation results indicate the system model provides foundational design and analysis capabilities that are required for applying system optimization algorithms. [ABSTRACT FROM AUTHOR]

Aircraft systems development is complex and time‐consuming. Model‐based Product Line Engineering (MBPLE) aims to reuse assets between projects to accelerate the development of systems at their early stage. Despite guidance from standards, MBPLE practitioners still face the challenge of deploying an appropriate configuration management strategy. This paper presents and demonstrates a configuration management strategy to support practitioners deploying MBPLE for aircraft systems development. We developed this strategy to comply with ISO/IEC 26580 and address pending standard ambiguities using best practices from product lines for systems, software, business processes, and systems of systems. The proposed strategy supports long‐term product line evolution, management of different asset types, and independent product environments. [ABSTRACT FROM AUTHOR]

Model‐Based Systems Engineering (MBSE) has been utilized within the automotive industry for several years. Increasing complexity due to highly automated, connected vehicles demands methods of coping with this complexity. In most cases, currently, only specific partial aspects or single methods of MBSE are used, which even varies across different companies. This paper aims to examine the current implementation of MBSE based on samples collected from companies acting within the automotive industry. Various aspects are explored, including the scope of application throughout the product life cycle, the use of simulation methods and the collaboration with other disciplines within product development. In the end, an evaluation discusses the reasons for the current state, and recommendations are given. [ABSTRACT FROM AUTHOR]

The growing demand for efficient, resilient, and sustainable electricity infrastructure has led to the emergence of smart grids as cyber‐physical systems of systems. Co‐simulation has proven an effective tool for their analysis and validation by coordinating independent subsystem simulations. However, the reuse and integration of diverse models in co‐simulation poses challenges, requiring compatibility and integration efforts. In response, this paper proposes a model taxonomy with the purpose of facilitating co‐simulation; it comprises three layers: concrete‐instance models, abstract‐instance models, and type models. The taxonomy contributes to the creation of independently developed models that can be seamlessly integrated into a coupled co‐simulation. Furthermore, it reflects the emergence of digital twins in smart grid engineering by the explicit distinction of abstract and concrete instances. The three‐layer taxonomy was derived and validated through a case study on co‐simulation of electric‐vehicle charging infrastructure. The research further analyzes and formalizes three modeling‐and‐simulation challenges framed through the lens of the taxonomy: the integration of models across all three layers, the merging of layers, and the consolidation of instance models to craft joint co‐simulation scenarios. Finally, three concrete recommendations for research and industrial practice are given. Thereby, the study contributes to the efficient and effective model‐based validation of cyber‐physical systems of systems using co‐simulation. [ABSTRACT FROM AUTHOR]

The rapid advancement and diversification of technical domains, particularly in automotive and smart grid sectors, are pivotal in driving the emerging energy revolution. This evolution is instrumental in governing the future of smart cities, characterized by escalating complexity and diversity within these domains. Such a landscape necessitates seamless collaboration among various domain experts, a task often complicated by the prevalent use of domain‐specific languages and tools tailored to specific engineering needs. This poses a significant challenge towards cross‐domain interoperability. Addressing this challenge, our research introduces a novel approach leveraging abstraction layers inspired by the Software Platform Embedded Systems (SPES) methodology. This approach aims to enhance the compatibility of domain‐specific frameworks, with a focus on the Smart Grid Architecture Model (SGAM) and the Automotive Reference Architecture Model (ARAM). By applying these SPES‐inspired abstraction layers, our work facilitates the reconciliation of varying levels of detail across different domains. The paper culminates in a proof of concept that demonstrates the practical implementation of this approach, showcasing a method to achieve effective cross‐domain interoperability. This implementation not only underscores the feasibility of our proposed solution but also illuminates a pathway for managing the intricate interplay of systems in the rapidly evolving landscape of smart cities. [ABSTRACT FROM AUTHOR]

In the context of its initiative to promote the adoption of model‐based approaches in the development of present and upcoming missions, the European Space Agency (ESA) organised this year its annual workshop on Model‐Based Space Systems and Software Engineering (MBSE2023) at its European Space Research and Technology Centre (ESTEC) premises. The aim of this year's workshop was to investigate how the model‐based systems engineering community could contribute to bridging the gap with domain‐specific model‐based approaches used in subsystem design. The World Café Method was used to facilitate the group discussions, the outcomes of which are summarized and presented in this paper. [ABSTRACT FROM AUTHOR]

In product development, rarely a product is developed from scratch. In many cases, a product is developed from a prior design or several prior designs. The associated development cost is actually a switching cost (or some called reuse cost), representing the cost incurred from developing the product from prior designs. So far, no proven works in switching cost estimation were found for products developed using MBSE (Model‐Based Systems Engineering). Today, MBSE is being widely adopted. It is important to develop switching cost estimation methods that leverage models and support model‐based development needs. This paper, per the best of our knowledge, is among the firsts that developed switching cost methods for MBSE. Our work identifies different use case scenarios/phases in an MBSE development cycle, and provides corresponding switching cost estimation methods, laying down the fundamental methodology for the model‐based switching cost estimations. Using SysML, an example use case of derivative airplane electrical power system is used to demonstrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

New developments in the area of the Industrial Internet‐of‐Things (IIoT) and Industry 4.0 offer huge potential for a more efficient and flexible industrial production, but are also accompanied by rising system complexity. Consequently, to deal with the increased system complexity, novel approaches, such as reference architectures, are emerging. However, most of these approaches are not yet mature and rather theoretical than ready‐to‐use. Thus, companies need to be provided with frameworks that actively support the transformation of their systems towards Industry 4.0. One of those frameworks has been introduced with Reference Architecture Model Industry 4.0 (RAMI 4.0), which counteracts the mentioned complexity and can be used for various use cases. However, as most of its concepts are too general to be applied directly to actual systems, the need for directly applicable reference architectures emerges. Therefore, this paper proposes a method to derive more detailed reference architectures based on RAMI 4.0 by making use of model‐based systems engineering (MBSE), which target single manufacturing domains rather than the whole industry. Therefore, relevant stakeholders are analyzed and different types of reference architectures targeting their concerns are identified. The resulting reference architectures should be ready‐to‐use for interested manufacturers and thus, enhance the acceptance of RAMI 4.0 as well as improve systems engineering in industrial manufacturing. Finally, the developed reference architecture is evaluated in a proof‐of‐concept case study of a flexible production system. [ABSTRACT FROM AUTHOR]

Heavy vehicles operating for less than truckload (LTL) carriers are utilized to the maximum extent possible for the operator to maximize vehicle return on investment. However, the decision to purchase new vehicles, reallocate the vehicle, or retire the vehicle is based on complex and interacting factors like performance degradation, total cost of ownership, new regulatory pressures, and maintenance costs. The problem of optimizing fleet capacity is well suited to a model‐based systems engineering approach. Using SysML as the language and MagicGrid as the method, a model for fleet vehicle replacement and utilization was built to understand the best way to maximize and grow shipping capacity. The process started with identifying stakeholders and their needs and ended with system parametric models capable of computing costs. This model has the potential to optimize operating costs for fleets and maximize the use of the vehicle assets. Not only do these optimizations improve company financial performance, they reduce the need to unnecessarily replace expensive equipment, which is a more sustainable business practice. [ABSTRACT FROM AUTHOR]