The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications
Abstract
This paper presents a review of the state of the art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such as energy generation and transport systems. The history of the digital twin is discussed first, along with a review of the associated literature; the process of synthesizing a digital twin is then considered, including definition of the aims and objectives of the digital twin. An example of the asset management phase for a wind turbine is included in order to demonstrate how the synthesis process might be applied in practice. In order to illustrate modeling issues arising in the construction of a digital twin, a detailed case study is presented, based on a physical twin, which is a small-scale three-story structure. This case study shows the progression toward a digital twin highlighting key processes including system identification, data-augmented modeling, and verification and validation. Finally, a discussion of some open research problems and technological challenges is given, including workflow, joints, uncertainty management, and the quantification of trust. In a companion paper, as part of this special issue, a mathematical framework for digital twin applications is developed, and together the authors believe this represents a firm framework for developing digital twin applications in the area of engineering dynamics.
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Cited by
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255
A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
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Digital twin, physics-based model, and machine learning applied to damage detection in structures
Ritto, T. G., & Rochinha, F. A. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107614 · Cited by 367
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304
Advancing digital twin implementation: a toolbox for modelling and simulation
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Metadata
- Authors
- Wagg, D. J., Worden, K., Barthorpe, R. J., & Gardner, P.
- Venue
- ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
- Publisher
- ASME International
- Published
- 2020-05-12
- Volume
- 6
- Issue
- 3
- ISSN
- 2332-9017 (ISSN-L)
- 2332-9025
- OpenAlex
- W3013932226
- Cited by
- 339
- References
- 80
- FWCI
- 22.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wagg, D. J., Worden, K., Barthorpe, R. J., & Gardner, P. (2020). Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering. https://doi.org/10.1115/1.4046739
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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