Why use them?
Having a virtual copy of a physical asset allows for testing improved maintenance practices, carrying out scenario modelling, providing training to employees and reviewing security measures. It also allows for predictive analytics and maintenance.
Though they emerged from aerospace, oil and gas and motorsports, where their capabilities in the design, development and ongoing maintenance of assets led to improved performance, safety, and efficiency, their use now spans industries.
High costs once meant using a digital twin only made sense on very high-value, discrete assets, but, as with many technology examples, the capability of hardware and software has been steadily increasing while costs have been coming down. That means we see cost-effective applications today that would not have made sense five years ago, and we expect this trend to continue as the deployment of cloud and edge computing, Internet of Things (IoT) and machine learning accelerates.
Digital twins are increasingly being used in airports, data centres, railways, electricity grids, wind farms and ports. And their use has grown to include the simulation of complex operating scenarios, creation of new data-driven business models, advanced virtual sales tours for customers, and modelling of alternative designs for future infrastructure developments.
How are digital twins being applied?
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 national infrastructure and has recruited over 700 organisations to be part of its ecosystem of connected digital twins that securely share information.1
The Port of Rotterdam, Europe’s largest and busiest, is developing a digital twin that to use real-time information on ship movements, infrastructure, water and weather conditions to optimise the use of its facility.2 Construction of the Genoa-Saint George bridge in northern Italy – which replaced the partially collapsed Morandi bridge – used a digital twin of the viaduct it crosses to streamline construction, improve collaboration between multidisciplinary teams and enable a fast-tracked build schedule. Using digital twin blueprints, General Electric monitors over 7,000 assets around the world, using 250,000 real-time data points to remotely manage and diagnose issues and plan predictive maintenance that has saved customers $US1.6 billion in replacement and rectification costs. 3
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 its surrounding habitat, including trees and other objects that may cause obstructions. Combined with input data from aerial surveys, it provides an understanding of the health and status of the vegetation around the power grid. And by better identifying, for example, areas of potential bushfire risk it allows Endeavour to carry out pre-emptive vegetation management such as trimming back trees.
Digital twins are also being put to use in tackling climate change by helping organisations assess the environmental impact of ‘aged infrastructure’ (e.g. older buildings) as part of their drive to manage and reduce emissions. Using IoT and detailed analytics, companies can better target advanced energy efficiency measures. When combining those with onsite renewables, storage and fleet electrification, they can help guide investment and deliver on net zero commitments.
Data and digitalisation are already proving their ability to drive value in infrastructure assets and improve the services they can provide to the communities using them. Many open up new revenue streams and investment opportunities; some, like digital twins, can transform the way existing assets are managed and used.
As enterprise-grade computing power becomes more accessible and more affordable, it will enable greater use of digital twins by more organisations, across more sectors and in a more advanced way. Faster processing times, the ability to include higher numbers of data points and the growing scale of machine learning will, allow digital twins to continuously self-learn and evolve.
Perhaps it’s no wonder then that research suggests the digital twins market is set to grow by 58 per cent a year and will be valued at $US48.2 billion by 2026.4