Digital twins are a way of modelling an object or system in a digital space. Rather than just the system itself, a digital twin is able to replicate that system throughout its lifecycle, changing to correspond with real-time data and using complex simulation, machine learning, and logic to guide decision making. Sensors attached to the physical system can then transfer operating data in real-time to the digital twin, providing users with a host of insights regarding how the system is operating.
Digital twin technology is becoming critical across a wide range of applications, with the users and designers of many complex, multi-vector systems now turning to the technology to allow them more insight into their deployed systems. Digital twins are particularly valuable as they provide insight not only into how a system is currently operating, but how it will continue to operate in the future. Analysis of this data allows accurate predictions of future performance and requirements to be made.
The remote nature of a digital twin also allows the designers of a system to access, observe and interact with a full, digital replica of a customer’s system without the need to be physically on-site, allowing troubleshooting, optimisation and management to be achieved far more efficiently.
By creating a full digital replica, manufacturers can test how a system will react to a wide range of real-world scenarios that would be difficult or problematic to re-enact in the real world. For example, a digital twin of a smart microgrid can be tested against a range of different power disruption events, from minor surges in voltage to full, long-term blackouts, without risking the power resilience of the system itself.
A huge range of different sensors, detecting light, sound, vibration, altitude, power, and many more, can be combined to twin almost any physical object, anywhere in the world, to allow engineers and operators to monitor and optimise its performance in real-time, even from thousands of miles away.