The use of data and ability to perform “digital asset management” can vary widely in different sectors and the application depends on the experience, maturity and capability of the organisation. Some assets are only at the beginning of their journey to implement digital solutions, still relying on manual processes, while others will have made large investments with advanced technological capabilities deeply ingrained in the organisation. One example of digitalisation that is being increasingly applied in the infrastructure sector is digital twins, which can help assets to understand, predict and optimise the performance of both their assets and broader business.
Firstly, it’s not a new concept, although the way digital twins are used today has certainly advanced into new realms of capability.
When Apollo 13 was launched in 1970, no one foresaw that one of the oxygen tanks would explode two days into the mission. However, a key to the rescue operation was that NASA kept a mirrored system of Apollo 13 on earth –a physical model of the spacecraft and its components. This allowed engineers on the ground to test possible solutions, simulating the conditions on board Apollo 13. While this was a physical twin rather than a digital one, the concept is the same.
Digital twins are a realistic digital representation of an infrastructure asset. A digital twin consists of a digital model of the physical structure and its behaviour, combined with data from sensors attached to the real-world asset. This model can start with an abstract linkage of systems or progress to a realistic, visual, three-dimensional model of an asset, or whole system that incorporates all of those systems into the visual model, giving the operators a real-time, virtual copy of their business. The ‘fourth dimension’ of time can be added, which allows the capture and use of historic data, or even projections for the future.
Digital twins are increasingly being used in large-scale infrastructure projects at airports, railways, electricity grids, wind farms and ports. They are used to optimise assets, improve maintenance and safety, increase valuation, and create new data-driven business models.
As a digital twin provides the organisation with a virtual copy of its physical asset, it’s able to plug all the operational systems into the model, including the heating, lighting, security, waste, energy, telecoms, and more. Using this virtual world, the asset can test better maintenance practices, carry out scenario modelling, provide training to employees and review security measures. It’s also able to carry out predictive analytics and maintenance.
While in the past higher costs meant using a digital twin only made sense on very high-value, discrete assets, as with many technology examples the capability of hardware and software has been steadily increasing while costs have been coming down. This means we’re seeing cost-effective applications today that would not have made sense five years ago, and we expect this trend to continue.
Leading research company Gartner has predicted that 50% of large industrial companies will use digital twins by 2021 and that those organisations will gain a 10% improvement in the effectiveness of the asset.
Almost any industrial or physical infrastructure has the capability to be digitalised in one form or another, from the automation of discrete processes, all the way through a 4D digital twin. Governments in Europe, Asia and Australia are investing in digital twins for urban planning and real-time integrated monitoring of infrastructure. The UK has an ambitious initiative to create a national digital twin that will simulate the interaction of infrastructure.
Endeavour Energy, which a MIRA fund has invested in as part of a consortium under a 99-year lease, has built a three-dimensional model of its entire power grid and the physical items surrounding it, including trees and other objects that may cause obstructions.
Endeavour uses that model, combined with input data from aerial surveys to understand the health and status of the vegetation around the power grid, enabling it to better identify, for example, areas of potential bushfire risk. The information gathered allows Endeavour to carry out pre-emptive vegetation management, understanding the health and growth of the plants and trimming back trees where risk exists.
The Goethals Bridge, linking New York City and New Jersey, whilst not a full digital twin, is well along that spectrum, using multiple sensors along the bridge that track the condition of the road, the traffic status and the condition of the physical asset. This allows for better maintenance of the bridge over time and improved monitoring and dynamic operations of the bridge itself.
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