Chapter 30 On Key Technologies for Realising Digital Twins for Structural Dynamics Applications D. J. Wagg, P. Gardner, R. J. Barthorpe, and K. Worden Abstract The term digital twin has gained increasing popularity over the last few years. The concept, loosely based on a virtual model framework that can replicate a particular system for contexts of interest over time, will require the development and integration of several key technologies in order to be fully realised. This paper, focusing on vibrationrelated problems in mechanical systems, discusses these key technologies as the building blocks of a digital twin. The example of a simulation digital twin that can be used for asset management is then considered. After briefly discussing the building blocks required, the process of data-augmented modelling is selected for detailed investigation. This concept is one of the defining characteristics of the digital twin idea, and using a simple numerical example, it is shown how augmenting a model with data can be used to compensate for the inherent model discrepancy. Finally the implications of this type of data augmentation for future digital twin technology is discussed. Keywords Digital twin · Dynamics · Mechanical · Virtualisation · Vibration 30.1 Introduction The digital twin concept is based on creating a virtual model framework that can replicate a particular system for contexts of interest over time. For example, a digital twin can be considered as a process, a product or some combination of both. At the most basic level, a digital twin is defined as a virtual duplicate of an engineering system built from a combination of models and data. In this sense the digital twin is more than just a computer-based simulation of the system of interest. Most importantly, the digital twin should have the ability to be used as a predictive tool to inform key engineering decisions, and it will be argued that this is one of its defining characteristics. A good introduction to the idea of the digital twin, including the background and history of the topic, is given by Datta [1–3]. There are multiple other examples of using the digital twin concept for engineering applications in the literature. For example, improving manufacturing processes [4–6], additive manufacturing [7, 8], aerospace engineering [2, 9], offshore drilling [3], product design [10–13] and nuclear fusion [14]. All these applications can be categorised into broad classes of tasks that the digital twin is being asked to achieve (with considerable overlap). In the context considered here, this will specifically be to make predictions for condition or structural health monitoring (SHM) purposes, and to understand the current state of the physical twin. The aim of this paper is to show an example of how a digital twin can be built for engineering applications which have time-dependent (dynamic) behaviour. The key building blocks required to create a simulation digital twin will be discussed. A key characteristic of a digital twin is the ability to bring together models and data, in order to give more accurate predictions. To demonstrate one approach to achieving this, the process of data-augmented modelling is considered in detail. To illustrate the concepts described an engineering based example is presented. A companion paper to this one presents a mathematical framework for the digital twin paradigm [15]. D. J.Wagg ( ) · P. Gardner · R. J. Barthorpe · K. Worden Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK e-mail: david.wagg@sheffield.ac.uk © Society for Experimental Mechanics, Inc. 2020 R. Barthorpe (ed.), Model Validation and Uncertainty Quantification, Volume 3, Conference Proceedings of the Society for Experimental Mechanics Series, https://doi.org/10.1007/978-3-030-12075-7_30 267
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