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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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“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)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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]
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“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
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“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
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“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]
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“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]
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Citations
Cites
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 3×
“Finally, the systems on board the digital twin are capable of mitigating damage or degradation by activating self-healing mechanisms, or by recommending changes in mission profile to decrease loads, thereby increasing both the life span and the probability of mission success [9].”
BackgroundInfluentialA Review of the Roles of Digital Twin in CPS-based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0
Uhlemann, T. H.-J., Lehmann, C., & Steinhilper, R. (2017) · Procedia CIRP · doi:10.1016/j.procir.2016.11.152 · Cited by 994
Haag, S., & Anderl, R. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.02.006 · Cited by 559
Toward a Digital Twin for real-time geometry assurance in individualized production
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543
Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0
Schluse, M., Priggemeyer, M., Atorf, L., & Roßmann, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2804917 · Cited by 524
The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems ☆
Uhlemann, T., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.04.043 · Cited by 426
Model‐based systems engineering: Motivation, current status, and research opportunities
Madni, A. M., & Sievers, M. (2018) · Systems Engineering · doi:10.1002/sys.21438 · Cited by 424
Self-citationSchluse, M., & Rossmann, J. (2016) · 2016 IEEE International Symposium on Systems Engineering (ISSE) · doi:10.1109/syseng.2016.7753162 · Cited by 292
Research works printed but not linked: 6
- Madni, A.M. (2017). Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyper-Connected World, Springer. Foreword by Norm Augustine.
- Zeigler, B., Muzy, A., and Kofman, E. (2018). Theory of Modeling and Simulation: Discrete Event and Iterative System Computational Foundations, Academic Press. [3rd ed.].
- Madni, A.M., Sievers, M., Erwin, D., Madni, A., Ordoukhanian, E., and Pouya, P. (2019, January 7–11). Formal Modeling of Complex Resilient Networked Systems. Proceedings of the AIAA Science and Technology Forum, San Diego, CA, USA.
- Madni, A.M. (2018, January 22–24). Next Generation Adaptive Cyber-Physical Systems. Proceedings of the 21st Annual Systems Engineering Conference, Tampa, FL, USA.
- Madni, A.M., Madni, C.C., and Sievers, M. (2018, January 7–12). Adaptive Cyber-Physical-Human Systems. Proceedings of the 2018 INCOSE International Symposium, Washington, DC, USA.
- Kraft, E. (2017, January 25). Challenges and Innovations in Digital Systems Engineering. Proceedings of the NDIA 20th Annual Systems Engineering Conference, Springfield, VA, USA.
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Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01512-w · Cited by 700
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Metadata
- 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 › Mathematics › Modeling and Simulation › Modeling and Simulation Systems
- Keywords
- digital twin, model-based systems engineering, virtual prototype, system simulation, Internet of Things
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