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Article

Towards the Development of an Operational Digital Twin

1
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield S1 3JD, UK
2
Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK
3
Department of Mechanical Engineering, University of Bristol, Bristol BS8 1TR, UK
*
Author to whom correspondence should be addressed.
Vibration 2020, 3(3), 235-265; https://doi.org/10.3390/vibration3030018
Received: 29 June 2020 / Revised: 3 August 2020 / Accepted: 2 September 2020 / Published: 4 September 2020
(This article belongs to the Special Issue Data-Driven Modelling of Nonlinear Dynamic Systems)
A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase. View Full-Text
Keywords: digital twin; data-driven modelling; machine learning; validation; active learning; hybrid testing; active control digital twin; data-driven modelling; machine learning; validation; active learning; hybrid testing; active control
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MDPI and ACS Style

Gardner, P.; Dal Borgo, M.; Ruffini, V.; Hughes, A.J.; Zhu, Y.; Wagg, D.J. Towards the Development of an Operational Digital Twin. Vibration 2020, 3, 235-265. https://doi.org/10.3390/vibration3030018

AMA Style

Gardner P, Dal Borgo M, Ruffini V, Hughes AJ, Zhu Y, Wagg DJ. Towards the Development of an Operational Digital Twin. Vibration. 2020; 3(3):235-265. https://doi.org/10.3390/vibration3030018

Chicago/Turabian Style

Gardner, Paul, Mattia Dal Borgo, Valentina Ruffini, Aidan J. Hughes, Yichen Zhu, and David J. Wagg. 2020. "Towards the Development of an Operational Digital Twin" Vibration 3, no. 3: 235-265. https://doi.org/10.3390/vibration3030018

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