268 D. J. Wagg et al. 30.2 Building a Digital Twin The primary aim of creating a digital twin is to enable the user to have as much information as possible about the current status and future behaviour of the physical twin. To set the context for this, a schematic hierarchy of possible capabilities for a digital twin is shown in Fig. 30.1. Here it can be seen that there are currently five levels of sophistication for a digital twin, starting at the lowest level of sophistication with Level 1, and increasing to Level 5, with each level incorporating the functionality of all previous levels. In fact three key requirements of a digital twin, namely supervision, learning and management are captured by Levels 3 to 5 respectively. To capture the historical time evolution, Levels 1 and 2 are included, but not considered further. An key distinguishing feature of a digital twin (and hence the dividing line between Levels 2 and 3 in Fig. 30.1) is that it can be used as a predictive tool. Furthermore, despite the focus on asset management tasks, all types of digital twin should evolve over the life-time of the physical twin. As a result they can be used in different contexts, depending on the life stage of the physical twin, whilst remaining a close one-to-one mapping from physical to digital. For example, if required, a digital twin can be used in the design phase of the physical twin, as described in Tuegel et al. [2]. Following that, the digital twin can be used in the manufacture and commissioning stage. Then, the digital twin can be used for asset management through operation and maintenance of the physical twin right through to end of life and decommissioning. Finally it is noted that the optimum final embodiment of the digital twin is in the form of a piece of software with highly informative graphical outputs. 30.2.1 Objectives of a Digital Twin The precise objectives of the digital twin will depend on the context that is required, but a typical simulation-twin should allow the user to: • understand the outputs quickly, in real-time if required, with visualisation of results; • incorporate and update the geometry of the digital twin through integrated computer-aided-design (CAD) and data processes with a clear measure of fidelity; • tunnel through the full-system CAD to specific components or sub-assemblies of interest and perform isolated tasks; • navigate a hierarchical representation of physical behaviour at different length scales; • interrogate the current state of the structure, whether in real-time or historically and perform data analysis (diagnosis); • test multiple scenarios to predict likely future outcomes (prognosis and decision support); • design controllers, perform hardware-in-the-loop simulation and/or set control processes for the physical twin; • quantify a level of confidence (trust) that the user should ascribe to given outputs; • generate test strategies if the digital twin needs additional data in order to increase the confidence level of a particular task. Note that the ability to predict future outcomes, and quantify the level of confidence in these predictions are particularly important features. This is now considered by using an example layout for a simulation digital twin. Fig. 30.1 A capabilities hierarchy for digital twins, where each level incorporates all the previous capabilities of the levels below
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