Model-Based Systems Engineering Research Articles

Avionics System Design using MBSE Methodology

Abstract: The West Air Sweden Flight 294 serves as a reminder of tragic crashes of aircraftdue to Avionics System failure. Due to malfunction of the Inertial Reference Unit (IRU), the aircraft lost its control resulting in tragic loss of life of the peopleon board. The Avionics System is a complex set of instruments, logical and physical links which must comply with the guidelines of Dob178c. The design of the system is complicated. This paper aims to understand the behavior of the Avionics System using the state-of-the-art Model Based System Engineering (MBSE) technology. This includes the two widely used tools – Capella and Simulink. We aim to understand the dynamic nature of avionics through these tools. Model Based System Engineering can be specifically beneficial because it will help the engineers to detect the flaws in the avionics system much earlier. This will potentially contribute to helping to prevent aircraft crashes due to avionics system failure. The idea of model-based system engineering includes the concept of taking a model as a reference to systematically solve the complex designs of any engineering stage. Before the concept of Model Based Systems Engineering came to be, Documents Based Systems Engineering was the trend in the system engineering department. However, it would take a lot of time, effort and accuracy to correctly pinpoint the information and accordingly design the system from a bunch of documents. MBSE, however, eases such kind of trouble by providing a model as a reference for the systems design

Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development.

Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO) for Early Design of Helicopter Primary Parameters

The design of a helicopter involves multiple disciplines and a large number of technical parameters with conflicting objectives, leading to the challenges of multidisciplinary coupling in design and optimization. In the context of the application of Model-Based Systems Engineering (MBSE) in complex product design, how to bridge the gap between MBSE and Multidisciplinary Design Analysis and Optimization (MDAO) to accelerate the early design process of helicopters remains an urgent problem to be addressed. The paper proposed an optimization framework that links MBSE and MDAO models for the design optimization problem of helicopters. On the MBSE side, the design optimization problem of helicopters was defined through the descriptive SysML model. On the MDAO side, the specific domain analysis model and optimization analysis workflow were established. The integration of design data was achieved through stereotype plugins and design components, bridging the gap between MBSE and MDAO, and providing accurate engineering data for system design verification. The paper provided an optimization case of a helicopter, with the optimal mission performance of the helicopter as the objective function. Through MDAO analysis, the design optimization of fuel consumption, take-off weight, and rotor dynamics parameters was completed, achieving an integrated process of system design and optimization.

Mission analysis and design of launch vehicle based on MBSE

A reusable launch vehicle is the basis for carrying out large-scale space activities and reducing the cost of space entry. It will greatly enhance the ability to enter space independently and come up with higher requirements for mission planning, demonstration, overall design, and system development of launch vehicles. Based on the model-based system engineering (MBSE) method, the forward process of model-based launch vehicle mission analysis and design is proposed first. Then, taking a launch vehicle as an example, the mission analysis and design are carried out, the mission is defined, the stakeholder requirements and mission requirements are identified, the mission application scenario is analyzed, the mission architecture is built, and the mission requirements model is established. Finally, by establishing the traceability relationship of mission requirements, the forward process of launch vehicle mission analysis is preliminarily realized, and the feasibility of the process is verified. It is of great engineering significance to improve the efficiency of launch vehicle mission analysis and system design.

Implementing the model-based systems engineering (MBSE) approach to develop an assessment framework for healthcare facility design

AbstractThe global elderly population rises, increasing dementia cases. Built environment impact on dementia health outcomes is known, forming the basis for evidence-based design studies. There's a need for a comprehensive assessment framework due to the complexity of interactions among Architectural Variables (AVs) and Health and Care Outcomes (HCOs). This paper proposes using Model-Based Systems Engineering (MBSE) to create such a framework. It collects data from 105 studies on 40 AVs, 36 HCOs, and 396 interactions. MBSE offers a holistic understanding, aiding healthcare facility design decisions.

Synchronous integration method of mechatronic system design, geometric design, and simulation based on SysML

The benefits of integrated design using the Model Based System Engineering (MBSE) approach in the design process of mechatronic systems have gradually become apparent. The automatic generation of simulation models and geometric models based on System Modeling Languages (SysML) models enables system engineers to swiftly analyze and simulate system performance, visually depict design outcomes, and expedite the product development process. Due to the current system modeling's lack of model integration and geometric design functions, this paper proposes an integrated design and simulation method for mechatronic systems that can carry out complete model synchronization and verification, rapid geometric solution generation, and visual representation. Furthermore, a corresponding model synchronization integration framework is established. This framework primarily encompasses system de-sign, system simulation, and geometric design, with its model integration method being model transformation and model synchronization. The paper concludes with an example of the design process of a quadruped robot to validate the framework and its supported methods, providing a reference for other system design and integration endeavors.

A Unified Model-Based System and Integrated Logistics Support Architecture Modeling Framework

Model-based system engineering(MBSE) uses the architecture model to drive system development. It has been widely used in aeronautical and aerospace systems development. Integrated Logistics Support(ILS) design is an essential part of system engineering. Still, there is no clear guidance on how to perform model-based ILS architecture modeling. This paper proposes a unified model-based system and ILS architecture modeling framework, which analyzes the relationship between the system and ILS in different life cycle concepts and gives a clear ILS architecture modeling approach. The unified system and ILS architecture modeling way will take full advantage of the model from the mission level to the system level to guarantee the different architecture perspectives consistency and save cost and time in the engineering practice.

A proposed framework using systems engineering to design human-centric manufacturing systems for novel products to reduce complexity and risk

AbstractThe environment for powertrain production system engineers is changing radically. This initial prescriptive study proposes a systems engineering framework based on two previous case studies which are under review for publication concerning design of battery plants. The framework was developed based on ISO/IEC/IEEE 15288 standard using Concept of Operations and Model-Based Systems Engineering in a workshop setting, with a focus on visualisation to understand the practical and emotional needs of the humans in the system. The framework was validated by twelve senior project members.

AI-based analysis and linking of technical and organisational data using graph models as a basis for decision-making in systems engineering

AbstractThe increased complexity of development projects surpass the capabilities of existing methods. While Model Based Systems Engineering pursues technically holistic approaches to realize complex products, aspects of organization as well as risk management, are still considered separately. The identification and management of risks are crucial in order to take suitable measures to minimize adverse effects on the project or the organization. To counter this, a new graph-based method and tool using AI, tailored to the needs of complex development projects and organizations is introduced here.

Investigation of advantages of models and the modelling process by introducing a model evaluation concept

AbstractWithin Model-based Systems Engineering different kinds of model will be created to support the execution of engineering activites. This contribution introduces an evaluation concept which focuses on the model informativity and its usefulness within the modelling process. Thereby, it shall be investigated which advantages the integration of model elements based on different levels of abstraction and a reduction of model formalisation enables. For the model creation the analogue modelling method will be applied, which uses physical (tangible) model elements.

Leveraging design thinking in MBSE: mitigating data and information uncertainties – an integration model approach

AbstractThe evolving needs of customers and stakeholders necessitate the collaboration of diverse system elements within a cyber-physical, socio-technical network. Socio-technical systems are characterized by numerous complex interdependencies as well as by endogenous and exogenous influences. A key issue that developers must address is the mitigation of data and information uncertainties. The authors introduce an approach that operationalizes Design Thinking as a supporting sufficient condition within the context of designing system models in the realm of Model-Based Systems Engineering.

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between stakeholders, development processes, and models can decrease the effectiveness and efficiency of the system development. A modified MBSE toolchain prototype has been developed to implement traceability management; however, a lack of formal and structured specifications makes it difficult to describe the complex topology in traceability management scenarios using this MBSE toolchain, such as creating traceability between heterogeneous models, which leads to poor reusability of this MBSE toolchain in other traceability management scenarios. To formalize traceability management scenarios using the MBSE toolchain, a cognitive thread (CT) ontology is developed in this study. The CT ontology is a specification expressing the information of stakeholders, models, and development processes for traceability management, providing the cognition capability to analyze the interrelationships between them. Based on the implementation of the modified MBSE toolchain, the concepts and interrelationships in the CT ontology are identified. The CT ontology is designed to develop the MBSE toolchain prototype for building, managing, and analyzing traceability in various traceability management scenarios. A case study of an adaptive cruise control system design is used to evaluate the completeness of the CT ontology through qualitative and quantitative analyses. The results demonstrate that the proposed CT ontology formalizes the information related to traceability management while using the proposed MBSE toolchain and can also be used in common traceability management scenarios to design other complex engineered systems.

Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering

Digital Engineering (DE) must be able to maximize efficiency and update dynamically in order to keep up with the wave of global interconnection. The evolution of the Digital Twin (DT) plays a significant role in DE as it enables autonomous optimization and an iterative cycle. However, the intricacy of real-world settings renders conventional evolutionary investigations of a solitary DT inadequate in tackling intricate systems at a comprehensive lifecycle magnitude. To address this, this paper presents the Co-evolutionary DTs (CoEDT), which has several key characteristics. (1) CoEDT study the evolutionary behavior of interconnected multi-DT. (2) CoEDT use a co-evolutionary distributed system architecture and is a DT technology that integrate MBSE. (3) CoEDT offers detailed dynamic models for the complex interactions and co-evolution among multi-DT throughout the lifecycle. It also supports real time measurement of multi-DT behavior to detect system anomalies. The significance of CoEDT lies in providing a more comprehensive insight for future product development by constructing a parallel world that simulates the lifecycle. In the CoEDT, we tackle the behavioral identification and structure of these evolving multi-DT throughout product lifecycle through the following approaches. Initially, drawing inspiration from biological cytology, we have conceived a concept of Collective DTs (CollDTs) to observing a collective behavioral of multi-DT. Subsequently, we further developed CoEDT to depict co-evolutionary behavior patterns and established a co-evolution architecture for all DT throughout the lifecycle by fusing Model-Based Systems Engineering (MBSE). Then, dynamic expressions and algorithm that can measure the co-evolution of every DT are inferred using information theory. Finally, the viability of the proposed CoEDT framework is demonstrated through the development of a solid rocket engine, which promotes the application of DT in the DE.

Virtual design on the heat pump refrigeration cycle: challenges and approaches

Virtual product design (VPD) of the refrigeration cycle has long been a major subject of many engineers and manufacturers. Issues should be the question of how simulation gets close to the real world in virtual ways. It is especially true when we consider variable refrigerant flow (VRF) heat pumps, having various types of indoor unit combinations, long and complicated piping configurations, and simultaneous cooling and heating functions under extreme weather conditions. In this regard, model-based systems engineering (MBSE) or model-based design (MBD) guides a methodology to make a virtual model easily. Now, there are plenty of engineering platforms and tools to make a strong simulation model, which provides interfacing technology among multiple physics, different dimensions, and codes in different languages. In the study, the authors share their experience in the virtual design for a VRF heat pump system including the architecture, multiple physics, and the connection with the control SW. Modelica, an acausal and object-oriented modeling language, constructs the backbone of the model to describe the dynamic behavior of refrigerants and to combine external components in the form of functional mock-up units (FMUs). The developed virtual model is validated against measured data, showing it can reproduce all the essential physics of interest both in qualitative and quantitative ways.

Advanced simulation-based predictive modelling for solar irradiance sensor farms

ABSTRACT The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

Effective Model-Based Systems Engineering framework for academic nanosatellite project management and design

Effective Model-Based Systems Engineering framework for academic nanosatellite project management and design

Early Validation and Verification of System Behaviour in Model-based Systems Engineering: A Systematic Literature Review

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.

Bacterial chemotaxis control process analysis with SysML

AbstractThis 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.

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be considered. In this paper, a semantic Model-based Systems Engineering (sMBSE) approach is proposed to support the design of the entire metal forming process. A multi-architecture modeling language KARMA is used to develop meta-models and architecture models of the metal forming process from the perspectives of mission, operation, function, logic flow and physical structure. Enabled by customized software, the KARMA models are transformed to ontology models, which are then converted to Petri Net (PN) models. Simulations are conducted based on the PN models to evaluate the operational performance of the created processes. A case study based on a real metal forming scenario is conducted to evaluate the proposed approach using quantitative and qualitative analysis. The result proves that the proposed approach enables to formalize the entire architecture modeling for metal forming process; and to provide an efficient approach for evaluating the operational performance of the designed solution for initial verification.

Research on design requirements for passive residual heat removal system of lead cooled fast reactor via model-based system engineering

Traditional text-based system engineering, which has been used in the design and application of passive residual heat removal system (PRHRS) for lead-cooled fast reactors, is prone to several problems such as low development efficiency, long iteration cycles, and model ambiguity. This study aims to effectively address the above-mentioned problems by adopting a model-based system engineering (MBSE) method, which has been preliminarily applied to meet the design requirements of a PRHRS. The design process has been implemented based on the preliminary design of the system architecture and consists of three stages: top-level requirement analysis, functional requirements analysis, and design requirements synthesis. The results of the top-level requirements analysis and the corresponding use case diagram can determine the requirements, top-level use cases, and scenario flow of the system. During the functional requirements analysis, the sequence, activity, and state machine diagrams are used to develop the system function model and provide early confirmation. By comparing these sequence diagrams, the requirements for omissions and inconsistencies can be effectively checked. In the design requirements synthesis stage, the Analytic Hierarchy Process is used to conduct preliminary trade-off calculations on the system architecture, after which a white box model is established during the system architecture design. Through these two steps, the analysis and design of the system architecture are ultimately achieved. The resulting system architecture ensures the consistency of the design requirements. Ultimately, a functional hazard analysis was conducted for a specific incident to validate case requirements and further refine the system architecture. Future research can further reduce the design risk, improve the design efficiency, and provide a practical reference for the design and optimization of PRHRS in digital lead-cooled fast reactors.