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Systems
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Advanced Model-Based Systems Engineering
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Advanced Model-Based Systems Engineering

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6320

Special Issue Editors

Dr. Jinzhi Lu

E-Mail Website
Guest Editor
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Interests: model-based systems engineering; architecture design; digital engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guoxin Wang

E-Mail Website
Guest Editor
Institute of Industrial and Intelligent Systems Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: model-based systems engineering; knowledge engineering; intelligent design
Dr. Xiaochen Zheng

E-Mail Website
Guest Editor
School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China
Interests: MBSE; semantic modelling; digital twin
Dr. Foivos Psarommatis

E-Mail Website
Guest Editor
Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: systems modeling; industrial application; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since systems engineering has been widely used in complex system development, advanced model-based systems engineering (MBSE) has emerged as a crucial methodology for managing the complexities of systems which become more and more complex. By employing models as primary artifacts throughout the system lifecycle, MBSE enables a more integrated, efficient, and reliable approach to system design, analysis, and verification. Currently, model-based systems engineering has been proposed with different meanings and definitions and with different modeling languages and frameworks. Thus, this Special Issue aims to identify the nature of model-based systems engineering to explore recent advancements in MBSE methodologies, techniques, tools, and applications, as well as their impact on various domains.

Topics of Interest:

  • Advanced modeling languages and notations for MBSE.
  • Model-based requirements engineering and management.
  • Integration of MBSE with other engineering disciplines (e.g., software engineering, mechanical engineering).
  • Model-based system architecture and design.
  • Model-based simulation and verification techniques.
  • Formal methods and model checking in MBSE.
  • MBSE for system of systems, cyber–physical systems and Internet of Things (IoT) and system security.
  • Case studies and applications of MBSE in different domains (e.g., aerospace, automotive, healthcare).
  • Tools and frameworks for supporting MBSE activities.
  • Challenges and future directions in MBSE research and practice.
  • AI enabling techniques for MBSE.
  • Digital engineering.

Dr. Jinzhi Lu
Prof.dr. Guoxin Wang
Dr. Xiaochen Zheng
Dr. Foivos Psarommatis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • model-based systems engineering
  • architecture design
  • digital engineering

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Published Papers (7 papers)

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Research

20 pages, 2656 KiB  
Article
An All-Hazards Return on Investment (ROI) Model to Evaluate U.S. Army Installation Resilient Strategies
by Gregory S. Parnell, Robert M. Curry, Eric Specking, Anthony Beger, Randy Buchanan, Susan Wolters, John P. Richards and Patrick R. Ables
Systems 2025, 13(2), 90; https://doi.org/10.3390/systems13020090 - 31 Jan 2025
Viewed by 406
Abstract
The paper describes our project to develop, verify, and deploy an All-Hazards Return of Investment (ROI) model for the U. S. Army Engineer Research and Development Center (ERDC) to provide army installations with a decision support tool for evaluating strategies to make existing [...] Read more.
The paper describes our project to develop, verify, and deploy an All-Hazards Return of Investment (ROI) model for the U. S. Army Engineer Research and Development Center (ERDC) to provide army installations with a decision support tool for evaluating strategies to make existing installation facilities more resilient. The need for increased resilience to extreme weather caused by climate change was required by U.S. code and DoD guidance, as well as an army strategic plan that stipulated an ROI model to evaluate relevant resilient strategies. During the project, the ERDC integrated the University of Arkansas designed model into a new army installation planning tool and expanded the scope to evaluate resilient options from climate to all hazards. Our methodology included research on policy, data sources, resilient options, and analytical techniques, along with stakeholder interviews and weekly meetings with installation planning tool developers. The ROI model uses standard risk analysis and engineering economics terms and analyzes potential installation hazards and resilient strategies using data in the installation planning tool. The ROI model calculates the expected net present cost without the resilient strategy, the expected net present cost with the resilient strategy, and ROI for each resilient strategy. The minimum viable product ROI model was formulated mathematically, coded in Python, verified using hazard scenarios, and provided to the ERDC for implementation. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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24 pages, 7034 KiB  
Article
An Approach Integrating Model-Based Systems Engineering, IoT, and Digital Twin for the Design of Electric Unmanned Autonomous Vehicles
by Clara A. Ramirez, Priyanshu Agrawal and Amy E. Thompson
Systems 2025, 13(2), 73; https://doi.org/10.3390/systems13020073 - 23 Jan 2025
Viewed by 455
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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22 pages, 3298 KiB  
Article
Stages of Systems Engineering: An Analysis and Characterization of Systems Engineering Approaches
by Iris Graessler and Benedikt Grewe
Systems 2025, 13(1), 53; https://doi.org/10.3390/systems13010053 - 16 Jan 2025
Viewed by 711
Abstract
In the engineering of complex technical systems, Systems Engineering (SE) is a key approach that is becoming increasingly relevant in more and more industries due to the ever-increasing complexity of systems. Over the decades of practical application and research, various specializations and forms [...] Read more.
In the engineering of complex technical systems, Systems Engineering (SE) is a key approach that is becoming increasingly relevant in more and more industries due to the ever-increasing complexity of systems. Over the decades of practical application and research, various specializations and forms of the Systems Engineering approach have developed, but there has so far been a lack of an overarching context and positioning in meaningful stages for the introduction of Systems Engineering in companies. For this reason, this research will systematize common Systems Engineering approaches and bring them together in a stage model for Systems Engineering. Based on a systematic literature review, use cases are identified for each approach and stage, which support companies in selecting an approach suitable for their own organization. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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22 pages, 5611 KiB  
Article
Numerical Design Structure Matrix–Genetic Algorithm-Based Optimization Method for Design Process of Complex Civil Aircraft Systems
by Qiucen Fan, Yanlong Han, An Zhang and Wenhao Bi
Systems 2024, 12(12), 566; https://doi.org/10.3390/systems12120566 - 16 Dec 2024
Viewed by 599
Abstract
In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept [...] Read more.
In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept of overlap coefficient is proposed to further sort out the forward and backward logical relationships between design tasks and the civil aircraft system design process optimization model based on a numerical design structure matrix. The algorithm NSGA-II is improved and verified with the flight control system design as a case study. The results show that the proposed method can effectively improve the efficiency of complex system design and provide technical support for the optimization of the design process of complex civil aircraft systems. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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16 pages, 736 KiB  
Article
Modelling and Intelligent Decision of Partially Observable Penetration Testing for System Security Verification
by Xiaojian Liu, Yangyang Zhang, Wenpeng Li and Wen Gu
Systems 2024, 12(12), 546; https://doi.org/10.3390/systems12120546 - 9 Dec 2024
Viewed by 619
Abstract
As network systems become larger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by [...] Read more.
As network systems become larger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by adversarial relationships and the “fog of war” is an unavoidable problem in penetration testing research. However, related methods have largely focused on the uncertainty of state transitions in the penetration testing process, and have generally ignored the uncertainty caused by partially observable conditions. To address this new uncertainty introduced by partially observable conditions, we model the penetration testing process as a partially observable Markov decision process (POMDP) and propose an intelligent penetration testing decision method compatible with it. We experimentally validate the impact of partially observable conditions on penetration testing. The experimental results show that our method can effectively mitigate the negative impact of partially observable conditions on penetration testing decision. It also exhibits good scalability as the size of the target network increases. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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31 pages, 26175 KiB  
Article
X-RMTV: An Integrated Approach for Requirement Modeling, Traceability Management, and Verification in MBSE
by Pengfei Gu, Yuteng Zhang, Zhen Chen, Chun Zhao, Kunyu Xie, Zhuoyi Wu and Lin Zhang
Systems 2024, 12(10), 443; https://doi.org/10.3390/systems12100443 - 20 Oct 2024
Viewed by 1307
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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30 pages, 5751 KiB  
Article
Method for Developing the System Architecture of Existing Industrial Objects for Digital Representation Tasks
by Vladimir Badenko, Vladimir Yadykin, Vladimir Kamsky, Arina Mohireva, Andrey Bezborodov, Egor Melekhin and Nikolay Sokolov
Systems 2024, 12(9), 355; https://doi.org/10.3390/systems12090355 - 9 Sep 2024
Viewed by 1244
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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