Model-Based Systems Engineering Research Articles

Assessment of large language models for use in generative design of model based spacecraft system architectures

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.

Considerations for Implementation of Model‐Based Systems Engineering in Different Sectors and System Types Based on Academic Literature

ABSTRACTThe emergence and adoption of Model‐Based Systems Engineering (MBSE) is driven in part by claimed benefits over traditional Document‐Based Systems Engineering. Despite high enthusiasm for MBSE, few studies have evaluated the value of MBSE implementation or the needs in MBSE implementation across various system types and industry sectors, which may require differing drivers of implementation and analyses of added value. This study investigates the perceptions of MBSE across sectors including Commercial, Contractor, and Government, as well as across system types including Astronautical, Aeronautical, and Infrastructure, to determine the perceived drivers and considerations of MBSE implementation across different industries. From 60 academic sources, this study collected and coded over 2900 claims about MBSE using 68 MBSE attributes. Our study's findings determine that the considerations for MBSE implementation likely vary by both sector and by system. Perceptions of MBSE, regardless of application grouping, were largely positive—negative attributes often captured unfavorable perceptions of implementation and tooling immaturity. The findings of this study suggest that practitioners may need to tailor the implementation of MBSE partly to their organizational priorities and needs, and to be cautious of relying only on broadly reported benefits of MBSE.

An Approach Integrating Model-Based Systems Engineering, IoT, and Digital Twin for the Design of Electric Unmanned Autonomous Vehicles

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.

Water Treatment Technologies: Development of a Test Bench for Optimizing Flocculation-Thickening Processes in Laboratory Applications

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.

Model Based Systems Engineering for Sustainable Autonomous Vehicle Design and Development

Model Based System Engineering (MBSE), introduced in the 2000s, has become a cornerstone for automobile companies like BMW, Toyota, and others prominently involved in the development of autonomous vehicles. MBSE is a unique systematic approach that uses designs and architecture instead of traditional document-centric methods. While the integration of MBSE in autonomous systems shows great promise for system development, there are still drawbacks due to the process of its complex integration. Currently, the engineering community is shifting its approach in systems engineering from document-based system engineering to MBSE. The shift has provided numerous advantages, one example being the enhancement of safety and security using Systems Modeling Language (SysML). Additionally, the continuous verification and validation of the system allowed by MBSE ensures that communication protocols meet real-time constraints. This study aims to address how MBSE can be used in autonomous vehicle development to improve functionality, secure connectivity, vehicle certification and enhance trust/confidence. Additionally, exploring how to overcome challenges such as streamlining existing requirements, test identification, navigating multi-perspective simulation, and improving vehicle-to-vehicle (V2V) communication. By using practical and multi-dimensional methods, formalisms, and applications, the future of MBSE shows great potential as a fundamental component to support effective, collaborative, and successful autonomous development environments.

Advancing model-based systems engineering in biomedical and aerospace research:

Model-Based Systems Engineering (MBSE) represents a modern methodology for developing complex systems using models, prioritizing alignment with customer preferences through comprehensive systems based modeling. Using PRISMA guidelines, data was gathered from peer-reviewed journals, systematic reviews, case studies, and computational studies from databases such as PubMed and Google Scholar, from the past 24 years. The study provides a comprehensive view of the current state of MBSE applications in healthcare and engineering addressing the practical challenges they face, offering strategic suggestions to improve future outcomes. This research introduces the Dynamic Risk Management Framework (DRMF), designed to leverage real-time data and predictive analytics to bolster system reliability and performance. The reviewed articles illuminate the essential role of MBSE in creating sophisticated systems and emphasize the need for improved modeling language integration, standardized processes, and increased interoperability. Further studies are required to validate its effectiveness and overcome its current limitations. As an emergent discipline within systems engineering, MBSE holds significant promise for future development, positioning itself as a critical tool for optimizing diverse fields of application. Further investigations are essential to validate MBSE's effectiveness and address its existing limitations.

Transitioning Perspectives: Agile and Waterfall Perceptions in the Integration of Model-Based Systems Engineering (MBSE) within Aerospace and Defense Industries

Transitioning Perspectives: Agile and Waterfall Perceptions in the Integration of Model-Based Systems Engineering (MBSE) within Aerospace and Defense Industries

SSMBERT: A Space Science Mission Requirement Classification Method Based on BERT

Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities.

A road-map to Readily Available Early Validation & Verification of System Behaviour in Model-Based Systems Engineering using Software Engineering Best Practices

A road-map to Readily Available Early Validation & Verification of System Behaviour in Model-Based Systems Engineering using Software Engineering Best Practices

Rapid Integrated Design Verification of Vertical Take-Off and Landing UAVs Based on Modified Model-Based Systems Engineering

Unmanned Aerial Vehicle (UAV) development has garnered significant attention, yet one of the major challenges in the field is how to rapidly iterate the overall design scheme of UAVs to meet actual needs, thereby shortening development cycles and reducing costs. This study integrates a “Decision Support System” and “Live Virtual Construct (LVC) environment” into the existing Model-Based Systems Engineering framework, proposing a Modified Model-Based Systems Engineering methodology for the full-process development of UAVs. By constructing a decision support system and a hybrid reality space—which includes pure digital modeling and simulation analysis software, semi-physical simulation platforms, real flight environments, and virtual UAVs—we demonstrate this method through the development of the electric vertical take-off and landing fixed-wing UAV DB1. This method allows for rapid, on-demand iteration in a fully digital environment, with feasibility validated by comparing actual flight test results with mission indicators. The study results show that this approach significantly accelerates UAV development while reducing costs, achieving rapid development from “demand side to design side” under the “0 loss” background. The DB1 platform can carry a 2.5 kg payload, achieve over 40 min of flight time, and cover a range of more than 70 km. This work provides valuable references for UAV enterprises aiming to reduce costs and increase efficiency in the rapid commercialization of UAV applications.

Integrating axiomatic design and design structure matrix into model‐based systems engineering: A case study for emergency response space mission design

AbstractWith the increasing complexity of space missions due to technological advancements, model‐based systems engineering (MBSE) has become a new paradigm for systems engineering (SE), providing a more formal and accurate system engineering process while reducing costs. However, MBSE does not offer a comprehensive approach for effective system function design. This study introduces a conceptual design approach that integrates axiomatic design (AD) and design structure matrix (DSM) into the MBSE paradigm and applies it to functional and logical design. The research focuses on utilizing the design knowledge encapsulated by system models to facilitate the development of new systems. System models have the potential to bridge different design domains in AD, reveal implicit associations, and offer functional and structural analysis capabilities. To validate our approach, we conducted a case study on an Emergency Response Remote Sensing mission system. This case study demonstrates that our proposed methodology lends formality to traditional AD by deriving AD matrices and DSMs from a system model, thereby enhancing the system's structure while curtailing elemental alterations during iterative design processes. The novelty of this research lies in realizing the integration of traditional design theory with the model paradigm of SE, leading to an effective design solution during the functional and logical design stages.

Model-based systems engineering adoption in the U.S. Nuclear industry

This paper examines research covering applications of Model-Based Systems Engineering (MBSE) and the field of nuclear energy in the U.S. to gain a better understanding of how to better posture the industry for expansion while maintaining a high margin of safety. It provides an analysis of current research to identify the benefits and drawbacks of implementing MBSE, how the U.S. can benefit from MBSE methodologies in nuclear energy, and the overall impacts of implementing MBSE in American nuclear energy operations and development. This paper seeks to highlight how the benefits of utilizing MBSE in nuclear energy operations outweigh the negative outcomes of doing so.

Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0

In the context of Industry 4.0, model-based definition (MBD) has been an effective approach to creating 3D models contained all heterogeneous information needed to define a product, which proposes new challenges for the traditional subassembly identification method that only considers the geometric information of a product in assembly sequence planning. To bridge the gap, we propose a novel heterogeneous knowledge graph-driven subassembly identification method to enhance assembly sequence planning in the model-based systems engineering (MBSE) paradigm. Specifically, a heterogeneous knowledge graph is first constructed based on the shape information and engineering details of an MBD model. Next, an ensemble deep learning approach that combines graph neural networks with the community detection algorithm is proposed to effectively detect the subassembly from the MBD model. Finally, the feasibility and effectiveness of the proposed method are demonstrated through an example of car suspension subassembly identification, providing insight into the industrial implementation.

Enhancing collaborative PHM of complex equipment: A MBSE approach for PEMFC health management systems

Model-Based Systems Engineering (MBSE) is acknowledged as an anticipatory design methodology. In addressing the operational and maintenance challenges of complex equipment, particularly those capable of self-healing, a Prognostics and Health Management for Digital Twin (PHMDT) framework has been developed in this research. This framework, rooted in MBSE principles, targets digital twins and encompasses four key subsystems: Virtual-Real Mapping, Prediction, Visualization, and Strategy Development & Evaluation. These subsystems facilitate the seamless transition from stakeholder requirements to subsystem analysis and design. Illustrated within the MagicGrid framework, practical implementation scenarios within the PEMFC system are presented. Each proton exchange membrane fuel cell (PEMFC) subsystem is meticulously crafted, leveraging monitoring matrices, refined characterization equations, bespoke state prediction models, and self-healing recovery strategy formulation. Through comprehensive visualization and integration, a robust PHM system is realized, affirming the efficacy and applicability of the PHMDT framework. The aim of this study is to propose a framework that is more suitable for collaborative PHM of complex equipment and to explore in detail the specific implementation structure of each subsystem in the health management of PEMFC.

Automated scenario generation from Operational Design Domain model for testing AI-based systems in aviation

AbstractApplications based on artificial intelligence (AI) promise benefits, ranging from improved performance to increased capabilities in many industries. In the aviation domain, one example is the new Airborne Collision Avoidance System (ACAS X). The current investigation aims at combining ACAS X and AI to maintain its performance while decreasing the memory footprint. However, the anticipation of AI being increasingly used confronts regulators with challenges in terms of safety assurance and certification. Consequently, the European Union Aviation Safety Agency (EASA) published a concept paper for machine learning applications in aviation. Both, the Concept of Operation (ConOps) in combination with an Operational Design Domain (ODD), are listed as objectives to be met for the safety analysis. From a developer’s perspective, this raises questions on how to effectively derive the ODD from ConOps and test the given system based on the ODD description. Based on an exemplary use case of a Near Mid-Air Collision avoidance between two aircraft through the advisories of ACAS X, a highly automated framework for generating and testing synthetic data is proposed. Using this framework, 1800 Near Mid-Air Collision scenario files are created and automatically executed in the simulation environment FlightGear. Scenario-based testing is used for the logging of ACAS X advisory data and evaluating it against predefined requirements. By this approach, an efficient way of verifying system requirements and conducting automated testing based on the ODD definition is demonstrated. Throughout this process, Model-Based Systems Engineering (MBSE) is used to reduce and manage complexity. The framework in this paper enables a systematic and highly automated approach for scenario generation based on the ODD.

Handling Complexity in Virtual Battery Development with a Simplified Systems Modeling Approach

Lithium-ion battery systems are a core component for electric mobility, which has become increasingly important in the last decade. The rising number of new manufacturers and model variants also increases competitive pressure. Competition is shortening development times. At the same time, the range of technology options for batteries is growing steadily. Fast and well-founded concept development is becoming even more essential in this increasingly complex environment. For this purpose, various model-based systems engineering (MBSE) methods are analyzed and evaluated. Based on this, the battery modeling framework is derived and described, tailored to the needs of battery development. The validation of the methodological approach is demonstrated by the simulation workflow from an electrical cell characterization to the thermal evaluation of different cooling methods.

Human Factors Engineering (HFE) Considerations for Mounting Internal Interfaces in Heavy Vehicles

Trucking fleets often need to install additional displays and consoles in vehicles to interface with the driver. These devices may be used for fleet management, assistance with logistics, or connecting to business applications. However, if these devices are installed without consideration of the drivers, the users may become distracted or frustrated with the system. Therefore, a human factors engineering (HFE) approach to the installation of additional console(s) reduces the likelihood of rework, distractions, and driver inconvenience. Model-based systems engineering (MBSE) approach utilizing best practices for HFE design is presented in this paper. The results of the effort are captured with a prototype assembly bracket used in the production version of the heavy vehicle. The plan-do-study-act (PDSA) model-based human factors systems engineering methodology demonstrates efficiencies in the design and implementation of the display system. This method proved effective as new mounts were fitted in a preproduction electric vehicle before they were released in production.

Towards a Fault Management Analysis Tool for Model Centric Systems Engineering

In an effort to effectively develop more complex spacecraft fault management (FM) systems new technologies are sought to enable rapid diagnostic model generation and validation, and provide tools to perform FM analyses Model-based Systems Engineering approaches to FM system development are uniquely suited to be combined with model-based tools currently utilized in the design of other parts of flight systems. Combined tools utilizing information from a common system model can reduce design inconsistencies and gaps in analyses. Tighter integration of FM with other system-level and subsystem-level hardware/software development activities allows crucial redundancy and sensor placement trades to be performed earlier and throughout the mission lifecycle. Our work has been towards the integration of a model-based fault management tool suite called MONSID®, with JPL’s Computer Aided Engineering for Systems ARchitecture (CAESAR ) platform as a way to improve FM system modeling and analysis. MONSID relies on application-specific models of the system being monitored. MONSID models consist of interconnected elements representing system hardware and measurement/command input points, called the topology. Model topology design is currently a manual process and often relies heavily on paper documentation such as hardware/software specs, engineering drawings, and interface control documents. CAESAR is a semantically- driven toolchain for model-based system engineering. At the core is a system model expressed in the Ontological Modeling Language (OML). It is intended to support semantic modeling, consistency validation, and continuous integration. A goal of the combined toolset is to automate FM model development by directly extracting models from CAESAR and then analyzing them in MONSID. Analyses currently available in MONSID include model topology inspection and validation and fault isolation capability based on sensor placement. While we have focused on two specific tools, the integration approaches can be leveraged by other semantically driven model-centric platforms and tools. This paper describes the evolution of our integration approaches as we evaluated different insertion points in the CAESAR toolchain with respect to MONSID model requirements. The MONSID-CAESAR tool is demonstrated on a simplified example of a spacecraft heat reclamation system. Results of the generated MONSID model are discussed, including levels of automation achieved and information surfaced to the users about the extracted model topology.

Mission Design and Validation of a Fixed-Wing Unmanned Aerial Vehicle for Environmental Monitoring

Climate change is becoming a worldwide emergency. In order to prevent catastrophic levels of climate change, three broad categories of action are ongoing: cutting emissions, adapting to climate impacts, and financing required adjustments. Cutting emissions requires stopping the use of fossil fuels in favor of renewable energy sources. Adapting to climate change and financing required adjustments need instruments for the understanding of the source causes and how effective the potential measures are. In this context, the use of Unmanned Aerial Vehicles for environmental monitoring is continuously increasing thanks to their ability to collect a wide range of environmental data, from the quality of air to the health status of vegetation, waters, and lands. This paper describes the research activities that are being performed for the design and development of a 100 kg Max Take Off Mass prototype zero-emission Unmanned Aerial Vehicle, named Daphne, destined for environmental monitoring, surveillance, and inspection missions. The developed prototype will drive the next industrialization of the vehicle. A particular focus is given to the design of the power system, based on the use of Proton Exchange Membrane fuel cells fueled with green hydrogen, the integration of the sensors allowing for multipurpose observations and measurements, and the design and validation of the relative multi-purpose missions via an innovative approach based on Model-Based System Engineering.

Prognostics and Health Management for Electrified Aircraft Propulsion: State of the Art and Challenges

Abstract In recent years, the aviation industry has witnessed a transformative wave of innovation in electrified aircraft propulsion (EAP), driven by sustainability and efficiency goals. Integration of novel electrical subsystems, including high-voltage power electronics, motors/generators, and energy storage devices, has introduced intricate complexities. In this context, an intensified focus on prognostics and health management (PHM) is imperative, considering the heightened reliability needs in a transportation propulsion application. This paper extensively analyzes the current state of the art in PHM applicable to various EAP systems and components crucial for the functioning of electric aircraft. Typical fault modes and fault management strategies are analyzed at various levels of systems hierarchy. An integral aspect of our investigation involves the identification of critical gaps within existing PHM frameworks, guiding the research agenda for enhanced reliability and performance. Moreover, the distributed nature and increasing complexity of electric propulsion systems underscore the importance of model-based systems engineering (MBSE). We advocate for the exploration of MBSE not only to inform the design and implementation of PHM solutions but also to facilitate certification and Verification and Validation activities. Additionally, the paper offers insights into existing tools and simulation software packages capable of integrating traditional gas turbine modules with electric subsystems, as well as simulating various faulty conditions in EAP relevant to PHM development. Key gaps in these tools are emphasized, drawing attention to areas that require further refinement and development. This comprehensive exploration aims to pave the way for future advancements in PHM tailored for the unique challenges posed by electric aircraft propulsion systems.