A Categorization of Digital Twin and Model-Based System Engineering Interactions
Source abstract
Abstract
The main goal of this study was to provide a new categorization of the different types of interactions between model-based system engineering (MBSE) and digital twins (DTs). To achieve this goal, an overview of the relationships between these two concepts was obtained based on a representative set of articles. The search identified 444 unique and valid records, of which 16 were selected for analysis based on article screening and eligibility assessments. The selected articles were then analyzed to identify the types of DT-MBSE relations and the area of the case study. As a result, the types of relationships were classified into two main categories: MBSE-based DTs and DTs that use MBSE system models. Finally, we present a case study of the Perception system, a system of systems designed to monitor and generate strategic assets through satellite data collection, further developing the capabilities established by the AlfaCrux satellite mission. Specifically, the case study focused on collecting data from a tower with micrometeorological instrumentation in the Brazilian Amazon Rainforest. The modeling was performed on the Capella software using the Arcadia method. In the case study, the system and the digital twin were designed in parallel based on a five-dimensional DT model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Technology Assessment and Management · Digital Transformation in Industry · Ergonomics and Human Factors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Technology, Campus Universitário Darcy Ribeiro, University of Brasília, Brasília 70910-900, DF, Brazil
Funding
Brazilian agencies of the Higher Education Personnel Improvement Coordination (CAPES) · National Council for Scientific and Technological Development (CNPq) · Federal District Research Support Foundation (FAPDF)
References (50)
Show source-provided references
1. Digital Twin in Industry: State-of-the-Art10.1109/TII.2018.2873186 ↗
2. Review of digital twin about concepts, technologies, and industrial applications10.1016/j.jmsy.2020.06.017 ↗
3. Digital Twins: State of the art theory and practice, challenges, and open research questions
4. Grieves, M. (2025, May 03). Digital Twin: Manufacturing Excellence Through Virtual Factory Replication; White Paper; 2014; pp. 1–7. Available online: https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication.
5. Glaessgen, E., and Stargel, D. (2012, January 23–26). The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference; 20th AIAA/ASME/AHS Adaptive Structures Conference; 14th AIAA, Honolulu, HI, USA.10.2514/6.2012-1818 ↗
6. Digital Twin in manufacturing: A categorical literature review and classification10.1016/j.ifacol.2018.08.474 ↗
7. Digital twin driven prognostics and health management for complex equipment10.1016/j.cirp.2018.04.055 ↗
8. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models10.1109/JPROC.2020.2998530 ↗
9. Autonomous, context-aware, adaptive Digital Twins—State of the art and roadmap10.1016/j.compind.2021.103508 ↗
10. Requirements and design patterns for adaptive, autonomous, and context-aware digital twins in industry 4.0 digital factories10.1016/j.compind.2023.103918 ↗
11. Web of Digital Twins10.1145/3507909 ↗
12. Stakeholders collaborations, challenges and emerging concepts in digital twin ecosystems10.1016/j.infsof.2024.107424 ↗
13. Digital Twin Platforms: Requirements, Capabilities, and Future Prospects10.1109/MS.2021.3133795 ↗
14. Douglass, B.P. (2016). Agile Systems Engineering, Morgan Kaufmann.
15. Friedenthal, S., Griego, R., and Sampson, M. (2007, January 24–28). INCOSE model based systems engineering (MBSE) initiative. Proceedings of the INCOSE 2007 Symposium, San Diego, CA, USA.
16. A Taxonomy of MBSE Approaches by Languages, Tools and Methods10.1109/ACCESS.2022.3222387 ↗
17. Towards collaborative life cycle specification of digital twins in manufacturing value chains10.1016/j.procir.2021.01.035 ↗
18. Madni, A., Madni, C., and Lucero, S. (2019). Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems, 7.10.3390/systems7010007 ↗
19. Lopez, V., and Akundi, A. (2022, January 25–28). A Conceptual Model-based Systems Engineering (MBSE) approach to develop Digital Twins. Proceedings of the 2022 IEEE International Systems Conference (SysCon), Montreal, QC, Canada.10.1109/SysCon53536.2022.9773869 ↗
20. Exploring the concept of Cognitive Digital Twin from model-based systems engineering perspective10.1007/s00170-022-09610-5 ↗
21. Madni, A.M., Erwin, D., and Madni, C.C. (2021, January 6–13). Digital Twin-enabled MBSE Testbed for Prototyping and Evaluating Aerospace Systems: Lessons Learned. Proceedings of the 2021 IEEE Aerospace Conference (50100), Big Sky, MT, USA.10.1109/AERO50100.2021.9438439 ↗
22. Model Based Systems Engineering applied to Digital Twin engineering: Why and how to?10.1016/j.ifacol.2024.09.126 ↗
23. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1136/bmj.n71 ↗
24. A Guide to Conducting a Standalone Systematic Literature Review
25. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications, 9.10.3390/publications9010012 ↗
26. The modelling and operations for the digital twin in the context of manufacturing10.1080/17517575.2018.1526324 ↗
27. Schluse, M., Atorf, L., and Rossmann, J. (2017, January 24–27). Experimentable digital twins for model-based systems engineering and simulation-based development. Proceedings of the 2017 Annual IEEE International Systems Conference (SysCon), Montreal, QC, Canada.10.1109/SYSCON.2017.7934796 ↗
28. Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts10.1109/JSYST.2019.2925627 ↗
29. Heber, D., and Groll, M. (2017, January 21–25). Towards a digital twin: How the blockchain can foster E/E-traceability in consideration of model-based systems engineering. Proceedings of the 21st International Conference on Engineering Design (ICED 17), Vol 3: Product, Services and Systems Design, Vancouver, BC, Canada.
30. Unified modeling for digital twin of a knowledge-based system design10.1016/j.rcim.2020.102074 ↗
31. Systems engineering and digital twin: A vision for the future of cruise ships design, production and operations10.1007/s12008-019-00621-3 ↗
32. Operationalizing digital twins through model-based systems engineering methods10.1002/sys.21559 ↗
33. Representing adaptation options in experimentable digital twins of production systems10.1080/0951192X.2019.1599433 ↗
34. A Holistic System Lifecycle Engineering Approach—Closing the Loop between System Architecture and Digital Twins10.1016/j.procir.2019.04.257 ↗
35. Construction method of shop-floor digital twin based on MBSE10.1016/j.jmsy.2021.05.004 ↗
36. Digital twins of product families in aviation based on an MBSE-assisted approach10.1016/j.procir.2021.05.144 ↗
37. Meierhofer, J., Schweiger, L., Lu, J., Züst, S., West, S., Stoll, O., and Kiritsis, D. (2021). Digital Twin-Enabled Decision Support Services in Industrial Ecosystems. Appl. Sci., 11.10.3390/app112311418 ↗
38. Munoz, P., Troya, J., and Vallecillo, A. (2021, January 10–15). Using UML and OCL Models to Realize High-Level Digital Twins. Proceedings of the 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Fukuoka, Japan.10.1109/MODELS-C53483.2021.00037 ↗
39. Maio, M.D., Kapos, G.D., Klusmann, N., Atorf, L., Dahmen, U., Schluse, M., and Rossmann, J. (2018, January 1–3). Closed-Loop Systems Engineering (CLOSE): Integrating Experimentable Digital Twins with the Model-Driven Engineering Process. Proceedings of the 2018 IEEE International Systems Engineering Symposium (ISSE), Rome, Italy.10.1109/SysEng.2018.8544392 ↗
40. Fink, A. (2019). Conducting Research Literature Reviews: From the Internet to Paper, Sage Publications.
41. Webster, J., and Watson, R.T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Q., 26.
42. Borges, R.A., dos Santos, A.C., Silva, W.R., Aguayo, L., Borges, G.A., Karam, M.M., de Sousa, R.B., García, B.F.A., Botelho, V.M.d.S., and Fernández-Carrillo, J.M. (2022). The AlfaCrux CubeSat Mission Description and Early Results. Appl. Sci., 12.10.3390/app12199764 ↗
43. Araújo, A.C., Nobre, A.D., Kruijt, B., Elbers, J.A., Dallarosa, R., Stefani, P., Von Randow, C., Manzi, A.O., Culf, A.D., and Gash, J.H.C. (2002). Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonian rainforest: The Manaus LBA site. J. Geophys. Res. Atmos., 107.10.1029/2001JD000676 ↗
44. Roques, P. (2018). Systems Architecture Modeling with the Arcadia Method: A Practical Guide to Capella, ISTE Press.10.1016/B978-1-78548-168-0.50001-3 ↗
45. de Almeida, D.P., Graics, B., Chagas, R.A.J., de Sousa, F.L., and Mattiello-Francisco, F. (2021, January 22–26). Towards Simulation of CubeSat Operational Scenarios under a Cyber-Physical Systems View. Proceedings of the 2021 10th Latin-American Symposium on Dependable Computing (LADC), Florianópolis, Brazil.10.1109/LADC53747.2021.9672594 ↗
46. Roques, P. (2016, January 27–29). MBSE with the ARCADIA Method and the Capella Tool. Proceedings of the 8th European Congress on Embedded Real Time Software and Systems, Toulouse, France.
47. Digital Twin in Aerospace Industry: A Gentle Introduction10.1109/ACCESS.2021.3136458 ↗
48. Helmholtz Cage Design and Validation for Nanosatellites HWIL Testing10.1109/TAES.2019.2898309 ↗
49. Tabletop Testbed for Attitude Determination and Control of Nanosatellites10.1061/(ASCE)AS.1943-5525.0000952 ↗
50. A review of balancing methods for satellite simulators10.1016/j.actaastro.2021.05.037 ↗