Digital twins

ion. However, there is no doubt that some models are closer to the real thing than others, with the whole panoply of models ranging from ‘thought experiments’ which are entirely conceptual to closely tailored digital representations that attempt to mirror as many features of the real system as possible. It is even possible to conceive of a transformation of a digital model from an entirely abstract conception to a full mirror image of the system in question. If, however, the model is a complete mirror image, which is the assumed definition of a digital twin, then it might be argued that the digital twin is no longer separate from the system, but in fact is the system itself. In this sense, all physical systems can have a digital equivalent which converges and merges with the system in question. In this sense, a true digital twin running in real time is no different from the system itself and this poses the question as to how the digital twin can be used to learn about the system and used to explore, simulate and test new designs if it is the system itself. For the digital twin to be used in this way, then presumably it has to be disconnected from the real system. Then, there is a logical difficulty in doing this in that both systems will run alongside one another and if they are mirror images of one another, the question becomes ‘how can the digital twin be used to explore and inform the original twin’. Let us make this problem a little clearer with respect to cities. The idea of the digital twin in this context has emerged from the representation of the city in terms of its physical assets. Geographic information systems, their scaling down to the level of buildings and their extension to deal with the operation of buildings in terms of energy, materials use, and maintenance using building information models software, are providing the context for extensive digital representations that scale to the level of all the physical assets in the city. Quite clearly, such systems are models of one kind in that they represent the city in digital rather than material form and may be very close to the basic physical equivalents that make up the city. But they rarely include any of the processes that determine how the city works in terms of its social and economic functions. 3D virtual models – even if they have embedded within them real-time processes, such as traffic and energy flow – are only representations that function over short periods of time and are often simply representations of the city at a cross-section in time. In this sense, a digital twin is much more like a conventional computer model in that it abstracts only a limited set of variables and processes. Wildfire (2018) makes the very useful distinction between models (or digital twins) that pertain to what we might call the high-frequency city in contrast to the low frequency. Highfrequency cities operate in real time at the level of our own personal time frames, second by second, minute by minute up to cycles of days and months, while low-frequency cities operate over years, decades, centuries, eons even. In this sense, we build different models to explore very short time horizons – what Wildfire (2018) calls ‘reactive’ models where ‘feedback and visualisations enhance real-time or near real-time interventions and improve the smooth day-to-day running of the city or asset’ and ‘predictive’ models where ‘accurate input data is used to improve longer term scenario planning to steer appropriate (and equitable) investment decisions’. In fact, I have used the term model rather than digital twin because in both contexts, the model of a digital twin needs to be decoupled from the original system if we are to use the model to inform our maintenance and/or design for the future operation of the real system. In short, we need to run the digital twin offline in some way so that we can use it to explore how to improve the real system. It is most unlikely that any of these kinds of models can be run in real time, thus matching exactly the processes operating the real system. The digital twin must always receive input from the real system if only to provide some sort of diagnosis of faults in the original system, and in this sense, there is latency involved. In the case of the high and low frequency time horizons noted here, 818 Environment and Planning B: Urban Analytics and City Science 45(5)