Abstract:
In this presentation we describe a time-evolving digital twin and its application to a proof-of-concept engineering dynamics example. The digital twin is constructed by combining physics-based and data-based models of the physical twin, using a weighting technique. The resulting model combination enables the temporal evolution of the digital twin to be optimised based on the data recorded from the physical twin. This is achieved by creating digital twin output functions that are optimally-weighted combinations of physics- and/or data-based model components that can be updated over time to reflect the behaviour of the physical twin as accurately as possible. Approximate Bayesian computation (ABC) is used for the physics-based model in this work, on the premise that relatively simple physical models are only rarely available in the context of digital twins. For the data-based model, a nonlinear auto-regressive exogeneous (NARX) neural network model was used. The engineering dynamics example is a system consisting of two cascading tanks driven by a pump. The data received by the digital twin is segmented so that the process can be carried out over relatively short time-scales. In this example, the weightings are computed based on error and robustness criteria. The results show how the time-varying water level in the tanks can be captured with the digital twin output functions, and a comparison is made with three different weighting choice criteria.
Prof. David Wagg is a Professor of Nonlinear Dynamics at the University of Sheffield. His research is focused on understanding and controlling nonlinear structural dynamics. Some of his current research activities relate to, so called, « digital twins », where data and models are combined to create a « virtual duplicate » of the structure of interest.
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