Abstract
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.
Highlights
A digital twin is a virtual duplicate of a physical system
A data-based component is a required part of a digital twin, allowing unknown physics to be compensated for in predictions; one solution to how a digital twin accounts for missing physics
A Gaussian process model has been coupled with a physics-based model, allowing predictions to be augmented, overcoming poor predictive performance
Summary
A digital twin is a virtual duplicate of a physical system (called the physical twin). In terms of asset management (the context of this paper) digital twins have been considered for tasks such as damage detection and structural-health/condition monitoring [4,5,6,7], predictive maintenance [8,9,10,11], and uncertainty quantification [12,13,14]. The data-based model is incorporated into an active learning strategy, demonstrating that the digital twin can adapt to unseen structural states.
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