In simple terms, it is a computer model of something physical, with a live connection between the two. The computer model changes to reflect changes to the physical asset. When digital twins of different assets are combined it can provide a “bird’s-eye view” of a city or a town that enables us to see how different systems interact.
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Why does UK infrastructure need digital twins?
Organisations need to understand where their assets are and in what condition they are in to minimise expenditure related to repair and maintenance. A digital twin can harness data science and predictive analytics to give a powerful understanding of network performance and what is required to serve future demand, which helps firms better manage their resources and plan new infrastructure.
Private sector companies need to know where each others’ assets are located, for example the locations of pipes and energy cables, because drilling into them would cause massive disruption. This builds the case for an eco-system of digital twins that share data, as per our plan for a National Digital Twin.
Infrastructure firms are already building twins for their own use, but we see a greater public benefit from bringing together models from across the built environment to improve the way infrastructure works in an area, whether that’s in a town or a city, or nationally.
How far away are we from having a complete National Digital Twin?
It’s a long-term vision, but there are building blocks being put in place. A key challenge is the need for a common data framework, there is lots of data about but it is often in different formats and not interoperable.
The NIC recommended the development of an Information Management Framework needed to establish a common digital standard for sharing data in digital twin models, and ultimately a National Digital Twin. The Centre for Digital Built Britain’s Digital Framework Task Group is working on this, the first landmark was a roadmap for implementation, published earlier this month.
What role does BIM play in all of this?
Infrastructure projects are working increasingly in BIM, which will help lay the groundwork for a National Digital Twin. The Information Management Framework will build upon the common data format and standards used in BIM and also apply it to existing infrastructure so that data generated by new and older systems work integrally and interoperatively.
Isn’t the idea of sharing data going to put off a lot of firms?
Companies need to see the value of coordinating their data and how they will benefit from being part of an infrastructure system. It can help them understand the location of other assets, better manage their own assets, and share different innovations and cost saving approaches.
In terms of data security, we’re looking at pulling information together in the National Digital Twin with different levels of access. The idea is it would not be completely open to everyone, only to the people who need to know that information and with the appropriately layers of security. For example, when Network Rail is building that new line it could coordinate exclusively with water or power utilities along the route.
How much will a National Digital Twin cost?
We haven’t costed it, what’s important is that different organisations see the importance of investing in it and the wider benefits to business and the public. It is likely to require some public coordination, at a national level through central government and through local government, which suggests that it would require public funding to ensure success.
What is the importance of AI and machine learning to digital twins?
Digital twins offer early wins in terms of knowing where assets are and sharing data, but the future vision is very much around what AI can do. Once you have twins that stream data from sensors in assets, the data can start to learn from itself and predict where problems are going to occur in the system.
For example, electricity networks are expecting huge changes over the next 30 years, with more distributed power supply and power storage challenges related to the use of electric vehicles. If the data can learn for itself it can assess where the bottlenecks are and where energy needs to be directed to.
A digital twin in the future might be able to predict a range of solutions to address different problems, like water shortages, or how to decarbonise electricity consumption.
No one really knows where this will take us, but it helps to keep an open mind about what might be possible.
