Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications
By David James Wagg, Keith Worden, R. Barthorpe, P. Gardner. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 2020
298 citations recorded by the source corpus.
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.
Subfields: Industrial and Manufacturing Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/ac7e90a5cbe65bbae0b9d5668bf25a906ffa787f
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In Industrial and Manufacturing Engineering
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Citations in the corpus
- A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “Additionally, DT-based optimal control [126,127] as well as machine dynamics issues [128,129] from a rotating system [130] to feed drive [131,132] are other important aspects.”
- Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas (2022, background): “From delivering the raw materials, transactions, transportation, the ERP system manages the supply chain for the business organization [68].”
- Digital twin, physics-based model, and machine learning applied to damage detection in structures (2020, methodology): “A state-of-art digital twins for modelling and simulation in engineering dynamics applications can be found in [4].”