Leveraging Digital Twin Technology in Model-Based Systems Engineering
Open accessAbstract
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.
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The definitions by Lui et al. (2018) and Madni et al. (2019) are the most inclusive and appropriate for the construction sector because they address real-time monitoring of construction projects and predictive maintenance.
“These definitions are most inclusive and appropriate for the construction sector because they address the purpose of DT in this industryreal-time monitoring of construction projects and predictive maintenance.”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al. (2019)
Section 5, “Future Prospects” (page unavailable), describes real-time data dashboards for construction projects and says digital twins can improve decisions about construction projects and property maintenance. It discusses predictive maintenance in manufacturing and maintenance activities, but does not specifically connect it to construction projects or assess these definitions as the most inclusive and appropriate for the sector. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDigital Twins face data security and ownership challenges, such as cyber threats including access to confidential data.
“Data security and ownership Cyber threats such as access to confidential data [41].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [41]
Section 5, “Future Prospects” (page unavailable), identifies privacy and data ownership as concerns and describes unease about sharing data with suppliers and customers. The cited passages do not identify cyber threats or unauthorized access to confidential data as examples of those concerns. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlthough a DT can require a great upfront investment, it can provide significant investment return in the long-run operation of a system.
“Although a DT can require a great upfront investment, it can provide significant investment return in the long-run operation of a system (Madni et al, 2019).”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al, 2019
DT can predict a system's response to safety in cases of critical events and can reveal imminent issues before they become critical by comparing actual responses to predicted ones.
“DT can predict a system's response to safety in cases of critical events and can reveal imminent issues before they become critical by comparing actual responses to predicted ones (Madni et al, 2019).”
From A review of digital twin applications in construction (2022) · cites this paper as Madni et al, 2019
Digital Twins enhance real-time remote monitoring and predictive maintenance but also pose risks related to data security and interoperability when integrated with legacy systems.
“While they enhance real-time remote monitoring and predictive maintenance [39], they also pose risks related to data security and interoperability when integrated with legacy systems [41,42].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [41]
Management techniques for large virtualized infrastructure could be usefully adopted for managing Digital Twins.
“Management techniques for large virtualized infrastructure [163] could be usefully adopted in these cases.”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [163]
The Introduction and Section 2 describe digital twins as virtual models and discuss how they can be used, but do not address management techniques for large virtualized infrastructure. Searches for virtualized infrastructure, cloud management, and adopting such techniques for digital twins found no passage on that point. The paper’s discussion of virtual models alone does not verify what the citation is attached to. (LLM, conf=0.73)
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Falekas, G., & Karlis, A. D. (2021) · Energies · doi:10.3390/en14185933 · Cited by 141 · see citing works
State-of-the-art survey on digital twin implementations
Liu, Y. K., Ong, S. K., & Nee, A. Y. C. (2022) · Advances in Manufacturing · doi:10.1007/s40436-021-00375-w · Cited by 126 · see citing works
Digital Twins for Ports: Derived From Smart City and Supply Chain Twinning Experience
Klar, R., Fredriksson, A., & Angelakis, V. (2023) · IEEE Access · doi:10.1109/access.2023.3295495 · Cited by 137 · see citing works
Is Digital Twin Technology Supporting Safety Management? A Bibliometric and Systematic Review
Agnusdei, G., Elia, V., & Gnoni, M. (2021) · Applied Sciences · doi:10.3390/app11062767 · Cited by 102 · see citing works
Xames, M. D., & Topcu, T. G. (2024) · IEEE Access · doi:10.1109/access.2023.3349379 · Cited by 131 · see citing works
Digital Twin: From Concept to Practice
Agrawal, A., Fischer, M. A., & Singh, V. (2022) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0001034 · Cited by 121 · see citing works
Digital Twins for the Future Power System: An Overview and a Future Perspective
Song, Z. Q., Hackl, C. M., Anand, A., Thommessen, A., Petzschmann, J., Kamel, O., Braunbehrens, R., Kaifel, A. K., Roos, C., & Hauptmann, S. (2023) · Sustainability · doi:10.3390/su15065259 · Cited by 128 · see citing works
A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins
Dalibor, M., Jansen, N., Rumpe⋆, B., Schmalzing, D., Wachtmeister, L., Wimmer, M., & Wortmann, A. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111361 · Cited by 126 · see citing works
Digital Twin and Its Application in Transportation Infrastructure
Gao, Y., Qian, S., Li, Z., Wang, P., Wang, F., & He, Q. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540108 · Cited by 119 · see citing works
The role of AI for developing digital twins in healthcare: The case of cancer care
Kaul, R., Ossai, C. I., Forkan, A. R. M., Jayaraman, P. P., Zelcer, J., Vaughan, S., & Wickramasinghe, N. S. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1480 · Cited by 125 · see citing works
Digital Twin in the Maritime Domain: A Review and Emerging Trends
Madusanka, N. S., Fan, Y., Yang, S., & Xiang, X. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11051021 · Cited by 125 · see citing works
Blockchain-Based Secure Communication of Intelligent Transportation Digital Twins System
Liu, J., Zhang, L., Li, C., Bai, J., Lv, H., & Lv, Z. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3183379 · Cited by 114 · see citing works
A review of digital twin capabilities, technologies, and applications based on the maturity model
Liu, Y., Feng, J., Lu, J., & Zhou, S. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102592 · Cited by 123 · see citing works
On the requirements of digital twin-driven autonomous maintenance
Khan, S., Farnsworth, M., McWilliam, R. P., & Erkoyuncu, J. A. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.08.003 · Cited by 123 · see citing works
Hosamo, H., Hosamo, M., Nielsen, H., Svennevig, P. R., & Svidt, K. (2022) · Advances in Building Energy Research · doi:10.1080/17512549.2022.2136240 · Cited by 106 · see citing works
Barriers to the Adoption of Digital Twin in the Construction Industry: A Literature Review
Opoku, D.-G. J., Perera, S., Osei‐Kyei, R., Rashidi, M., Bamdad, K., & Famakinwa, T. (2023) · Informatics · doi:10.3390/informatics10010014 · Cited by 121 · see citing works
Digital Twin: Where do humans fit in?
Agrawal, A., Thiel, R. W., Jain, P., Singh, V., & Fischer, M. A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104749 · Cited by 119 · see citing works
Overview on Digital Twin for Autonomous Electrical Vehicles Propulsion Drive System
Ibrahim, M., Rassõlkin, A., Vaimann, T., & Kallaste, A. (2022) · Sustainability · doi:10.3390/su14020601 · Cited by 115 · see citing works
Options for and Challenges of Employing Digital Twins in Construction Management
Salem, T., & Dragomir, M. (2022) · Applied Sciences · doi:10.3390/app12062928 · Cited by 90 · see citing works
Naderi, H., & Shojaei, A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104785 · Cited by 115 · see citing works
Methods for enabling real-time analysis in digital twins: A literature review
Eshaghi, M. S., Anitescu, C., & Rabczuk, T. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107342 · Cited by 114 · see citing works
Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin
Park, H.-A., Byeon, G., Son, W., Jo, H.-C., Kim, J.-Y., & Kim, S. (2020) · Energies · doi:10.3390/en13205504 · Cited by 104 · see citing works
Multimedia knowledge‐based bridge health monitoring using digital twin
Kang, J., Chung, K., & Hong, E. J. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10649-x · Cited by 108 · see citing works
Digital Twin Technology in Built Environment: A Review of Applications, Capabilities and Challenges
Mousavi, Y., Gharineiat, Z., Karimi, A. A., McDougall, K., Rossi, A., & Barsanti, S. G. (2024) · Smart Cities · doi:10.3390/smartcities7050101 · Cited by 117 · see citing works
Lv, Z., & Xie, S. (2024) · Digital Twin · doi:10.12688/digitaltwin.17524.2 · Cited by 112 · see citing works
Smart home energy management systems: Research challenges and survey
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A review of the Digital Twin technology for fault detection in buildings
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Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
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Combined Use of GPR and Other NDTs for Road Pavement Assessment: An Overview
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Digital twin in power system research and development: Principle, scope, and challenges
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A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector
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From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins
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Advances of Digital Twins for Predictive Maintenance
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Cyber-agricultural systems for crop breeding and sustainable production
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- Authors
- Madni, A. M., Madni, C. C., & Lucero, S. D.
- Affiliations
- Intelligent Systems Technology (United States) (US)
- Research and Engineering, Office of the USD, Washington, DC 20301–3030, USA
- Corresponding author
- Madni, Azad M.
- Venue
- Systems
- Publisher
- MDPI AG
- Published
- 2019-01-30
- Volume
- 7
- Issue
- 1
- Pages
- 7
- ISSN
- 2079-8954 (ISSN-L)
- OpenAlex
- W2912589244
- Cited by
- 1,050
- References
- 23
- Citations by year
- 2026: 124 · 2025: 166 · 2024: 189 · 2023: 201 · 2022: 159 · 2021: 121 · 2020: 68 · 2019: 22
- FWCI
- 51.22
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
- Keywords
- digital twin, model-based systems engineering, virtual prototype, system simulation, Internet of Things
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