Search Results (27)

Unmanned Aircraft Systems (UAS) have seen a significant growth in civilian space over the past decade. The number one ranked challenge in UAS operations in Europe is regulatory obstacles such as the Specific Operations Risk Assessment (SORA) for 2023–2025. Existing approaches have focused on individual technical solutions (radio technologies, redundancy schemes, or cryptographic protections) or on high-level safety analysis, but have not integrated regulatory compliance, risk assessment, and repeatable systems models that directly support SORA artifact generation and rapid adaptation across BVLOS operational contexts. Thus, the current state-of-the-art apparatus lacks a systematic Model-Based Systems Engineering (MBSE) approach that can cater to Command and Control (C2) data-link design for Beyond Visual Line-of-Sight (BVLOS) missions. In this work, we propose an MBSE methodology designed to assist engineers in designing a C2 data link for BVLOS drone operations that complies with SORA regulations in the Netherlands and Europe. To validate the use of MBSE in a wide range of complex drone operations, we demonstrate how subtle modifications in the proposed engineering models can be made without any major overhaul of new SORA applications, and this is validate these changes through laboratory software tests and simulations. Full article

The V-Model is widely used in safety-critical engineering; however, its application to rapid prototyping remains challenging due to limited integration between lifecycle governance and executable validation workflows. This paper addresses end-to-end mechatronic ECU development through the general objective of designing an integrated, prototyping-oriented software platform that operationalizes a V-Model-based rapid prototyping lifecycle with explicit, process-level traceability. Four specific objectives structure the contribution. First, bibliometric analyses of the keywords “V-Model” and “Rapid Prototyping Platforms,” conducted using the Web of Science database and VOSviewer, highlight the limited integration of structured lifecycle approaches with practical prototyping workflows. Second, the V-Model is adapted for mechatronic ECU development by introducing domain-specific decomposition and a dependency-driven testing sequence. Third, the BIOComProP (Basic Input Output Communication Protocol Platform)—comprising reusable ECU firmware, PC based test software, and a dedicated request–response communication protocol—is developed, and its testing capabilities are mapped to V-Model phases. Finally, a logic-based workflow is defined to translate patent derived requirements into executable development and validation steps. The results demonstrate staged verification aligned with technical dependencies, structured traceability across development activities, and firmware reuse across multiple prototypes, offering a coherent and reproducible approach for rapid prototyping of mechatronic ECU systems without relying on heavyweight MBSE or ALM toolchains. Full article

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation. Full article

The use and importance of complex software-intensive systems are growing. As they are used in a wider range of situations in which dependability must be ensured, the reliability of the systems and of their components needs to be addressed throughout their lifecycle, including at early development stages. In addition, the means used to deal with reliability need to be linked to and integrated into the overall systems engineering practices and processes. Within this context, we present an approach to design reliable systems in practice in the scope of model-based systems engineering (MBSE) and knowledge-centric systems engineering (KCSE), two systems engineering perspectives whose adoption is increasing. While MBSE relies on explicit system models, KCSE places artificial intelligence at its core to capture, formalise, and reason over system knowledge. Both perspectives are combined to model systems and analyse whether their design addresses the expected system reliability properties, leveraging knowledge representation, natural language processing, and inference mechanisms. The approach links the processes and tools of Arcadia/Capella for MBSE and of SES Engineering Studio for KCSE. A joint application process has been defined for system modelling, ontology development, structured textual requirements specification, traceability management, and model quality analysis, all of which are targeted at system reliability. For validation, the approach has been applied on eight systems that cover five different application domains, considering tens of diagrams, of knowledge elements, of reliability properties, and of analysis possibilities. Based on the validation results, we argue that the approach is a feasible means to design reliable systems. The approach is also the first one that effectively combines MBSE with Arcadia/Capella and KCSE with SES to design reliable systems in practice. Full article

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely tied to software engineering. As software becomes a critical component of physical systems, such as vehicles, appliances, and production plants, bridging the gap between software engineering and other disciplines, such as mechanical, electrical, and civil engineering, becomes essential. Despite its potential, MBSE is still in its early stages when it comes to integrating executable models of physical systems into engineering environments. The purpose of this research is to assess the present capabilities of MBSE by identifying existing missing links, thereby enabling prospective users to make well-informed decisions about its integration into organizational processes. In this analysis, it is important to have a comprehensive view of the complexity of MBSE across different disciplines to obtain an overall picture. In addition to identifying open challenges, we present three critical gaps in the MBSE practice through a comprehensive demonstration case: limited tool interoperability and model integration, modeling language limitations, and dependence on a specialized workforce. Current studies largely view MBSE as the most applicable and effective for the design phase of the system life cycle. Yet, to capture MBSE in its entirety, its principles must be applied throughout the whole system life cycle. Full article

The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engineering (MBSE) provides a promising approach to manage this complexity by enabling consistent and structured system representations. While domain-specific modeling languages (DSMLs) can tailor MBSE methods to specific domains, existing approaches often lack standardized semantics, user guidance, and tool support to ensure consistent model creation and verification. This paper introduces a DSML framework tailored for the conceptual design of production systems, integrating both methodological guidance and standard-based domain knowledge. The approach builds upon the Software Platform Embedded Systems (SPES) framework and extends Systems Modeling Language (SysML) through the Unified Modeling Language (UML) profile mechanism, providing clear modeling constructs, viewpoint-specific diagram types, and automated consistency checks. To enhance comprehensibility and domain alignment, the framework incorporates supplementary DSMLs that capture structures and semantics from established industrial standards. The proposed method is evaluated using an aircraft production case study, demonstrating improved applicability of MBSE for the conceptual design of complex production systems. Full article

The automotive industry is undergoing a significant transition, where the development of Battery Electric Vehicles (BEV) and the increasing use of intelligent vehicle functions are transforming vehicles into advanced Cyber-Physical Systems. For heavy-duty OEMs, this transition challenges a Product Development (PD) heritage inherent in an ecosystem of established processes, IT systems, and organization structures. This study primarily comprises semi-structured interviews, conducted at a heavy-duty OEM, and a focused literature search. The study contributes by the following: (i) identifying key PD challenges in the ICE–BEV transition, (ii) outlining obstacles in adopting Model-Based Systems Engineering (MBSE) for managing architectural complexity, and (iii) synthesizing recommendations for architecture-driven collaboration. Interview findings, highlighted intertwined challenges such as fragmented architecture descriptions across physical and software domains, weak continuity between early-phase system context and detailed design, and collaboration constrained by inconsistent terminologies, strained communication channels, and manual reconciliation of architectural information through documents and disconnected tools. These factors hinder function-component traceability and concurrent development across domains. While MBSE is often recommended to address such issues, practical obstacles are noted, including trade-offs between modeling effort and fidelity, limited support for early spatial layout integration, difficulties in bridging physical and software architectures, and the limited integration of document-based practices preferred in early conceptual phases. Based on these insights, the study recommends architecture-driven collaboration anchored in a federated vehicle-architecture description, supported by a distributed systems-engineering function. A layered development approach combining document artifacts with progressively rigorous MBSE is advised for early-phase agility, later-stage traceability, and structured information flow. Full article

Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in industry adoption of MBSE, prior research by the authors identified challenges such as tool limitations, knowledge gaps, cultural and political barriers, costs, and the level of customer understanding and acceptance of MBSE practices. Additionally, another study by the authors points out a gap between industry demands for MBSE skills in new hires and the current academic training programs. To further assess the MBSE industry’s workforce needs, this paper introduces a two-phase method for the Structured Extraction of MBSE competencies using large language models based on current workforce demands from LinkedIn job postings. Phase 1 involved extracting 1960 job descriptions from LinkedIn using the term “model-based systems engineer.” In phase 2, large language models (LLMs) employing deep transformer architectures were used to transform unstructured text into structured data. An AI agent was used as an autonomous software layer to manage every interaction between the raw dataset from Phase 1 and the LLM. Supported by the analyzed data, a competency framework is proposed that summarizes the tools, technical skills, and soft skills expected of a model-based systems engineer by the industry. The framework is designed to include core competencies shared across all MBSE roles, with specific competencies tailored for aerospace & defense, manufacturing and automotive, and software and IT sectors. Full article

The main goal of this study was to provide a new categorization of the different types of interactions between model-based system engineering (MBSE) and digital twins (DTs). To achieve this goal, an overview of the relationships between these two concepts was obtained based on a representative set of articles. The search identified 444 unique and valid records, of which 16 were selected for analysis based on article screening and eligibility assessments. The selected articles were then analyzed to identify the types of DT-MBSE relations and the area of the case study. As a result, the types of relationships were classified into two main categories: MBSE-based DTs and DTs that use MBSE system models. Finally, we present a case study of the Perception system, a system of systems designed to monitor and generate strategic assets through satellite data collection, further developing the capabilities established by the AlfaCrux satellite mission. Specifically, the case study focused on collecting data from a tower with micrometeorological instrumentation in the Brazilian Amazon Rainforest. The modeling was performed on the Capella software using the Arcadia method. In the case study, the system and the digital twin were designed in parallel based on a five-dimensional DT model. Full article

This article proposes a novel methodology aimed at streamlining the system’s development process. By examining existing state-of-the-art approaches and the capabilities inherent in Model-Based Systems Engineering (MBSE) tools, the article introduces a methodology centered around transforming a descriptive Systems Modeling Language (SysML) model into a digital twin. This virtual representation of the physical asset leverages real-time data and simulations to mirror its behavior and characteristics. When integrated with MBSE, this synergy allows for a comprehensive and dynamic approach, enhancing innovation by providing a holistic and adaptable framework for designing, analyzing, and optimizing complex systems throughout their lifecycle. The practical application of this Real-Time Communication and Data Acquisition (RT-CDA) methodology is implemented in a context and operational scenario of an electric unmanned autonomous vehicle employing both Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) approaches. The methodology empowers systems engineers to iteratively update and refine their system model’s fidelity based on real-world testing insights. The article specifically demonstrates the real-time communication capabilities achieved between an electric unmanned autonomous vehicle (a physical asset) and a descriptive (SysML) model, illustrating the real-time data aspect integral to the concept of a digital twin. This study serves as a foundation for future endeavors, envisioning real-time communication among virtual and physical models to construct comprehensive digital twins of complex systems to predict behavior and performance. Full article

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. Full article

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. Full article

One major challenge of today’s product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled. Full article

The use of different techniques and tools is a common practice to cover all stages in the development life-cycle of systems generating a significant number of work products. These artefacts are frequently encoded using diverse formats, and often require access through non-standard protocols and formats. In this context, Model-Based Systems Engineering (MBSE) emerges as a methodology to shift the paradigm of Systems Engineering practice from a document-oriented environment to a model-intensive environment. To achieve this major goal, a formalised application of modelling is employed throughout the life-cycle of systems to generate various system artefacts represented as models, such as requirements, logical models, and multi-physics models. However, the mere use of models does not guarantee one of the main challenges in the Systems Engineering discipline, namely, the reuse of system artefacts. Considering the fact that models are becoming the main type of system artefact, it is necessary to provide the capability to properly and efficiently represent and retrieve the generated models. In light of this, traditional information retrieval techniques have been widely studied to match existing software assets according to a set of capabilities or restrictions. However, there is much more at stake than the simple retrieval of models or even any piece of knowledge. An environment for model reuse must provide the proper mechanisms to (1) represent any piece of data, information, or knowledge under a common and shared data model, and (2) provide advanced retrieval mechanisms to elevate the meaning of information resources from text-based descriptions to concept-based ones. This need has led to novel methods using word embeddings and vector-based representations to semantically encode information. Such methods are applied to encode the information of physical models while preserving their underlying semantics. In this study, a text corpus from MATLAB Simulink models was preprocessed using Natural Language Processing (NLP) techniques and trained to generate word vector representations. Then, the presented method was validated using a testbed of MATLAB Simulink physical models in which verbalisations of models are transformed into vectors. The effectiveness of the proposed solution was assessed through a use case study. Evaluation of the results demonstrates a precision value of 0.925, a recall value of 0.865, and an F1 score of 0.884. Full article

Model-based systems engineering (MBSE) is a methodology that focuses on creating and exploiting the digital system and engineering domain models as the primary means of exchange of information, feedback, and requirements, as opposed to document-centric systems engineering. The numerical model that encompasses all structural information lies at the heart of the system. Furthermore, this computational model can calculate crucial parameters such as displacements, stresses, and strains. The innovative SEC4TD project, funded by the EIT Raw Materials, proposes an integrated end-to-end (E2E) solution composed of three hardware and software innovative products for mining operators and service providers that will enable multi-scale multi-platform data collection and visualization, events prediction, effective information management, and data traceability. Within this framework, SEC4TD has commenced utilizing the MBSE approach to develop a system specifically for tailings dams. The system integrates all the data from the field, laboratory tests, and monitoring systems to assess the stability of the tailings dam. In the event of unsatisfactory results or the identification of potentially dangerous occurrences, the system immediately notifies the engineering personnel responsible for the tailings storage facility. The MBSE system for tailings dams has been tested at the two tailings storage facilities, one in Poland and another in Bosnia. The article describes the concept, architecture, and data used in the system. Full article