WHAT IS A DIGITAL TWIN AND HOW ARE THEY USED?

A digital twin is a virtual environment that mirrors a physical environment in every way - dimensions, material characteristics and the laws of physics. These virtual environments enable you to streamline the development lifecycle of physical products or experiences, enabling various roles to collaborate and share their expertise. A digital twin is based within a cloud platform such as NVIDIA Omniverse, allowing real-time collaboration and application-agnostic working - you can learn more about this technology by reading our Omniverse blog post.

There are multiple uses cases for digital twins, including:

For example, when planning a new town, traditionally you’d have blueprints and plans as to how the buildings and roads connecting them would fit together, along with designs for how the town would be connected to the wider major road infrastructure. These plans would be based on current best practice and construction would commence with multiple contractors building the houses and offices and roads, with little communication or collaboration as their work progressed.

If you could model this entire town in a virtual world - before anything was built - the key stakeholders and contractors could plan and collaborate in real-time, to create cost savings by seeing commonalities across the entire project. Furthermore, you could upload traffic data from current similar towns to see if the proposed road plans allowed for best traffic flow or would cause bottleneck problems - any alterations to the plans could then be made prior to any expensive construction was started. Similarly, buildings could be analysed to see if they were likely to generate external wind or internal overheating problems once built. Public transport links and routes could be pre-planned for maximum efficiency, by foot-fall and heat-map analysis of the main shopping areas, and telecommunications antenna locations could be simulated and then optimised for best coverage.

The benefits of a digital twin can be seen at any scale - from stress testing individual components prior to manufacture to reduce returns; analysing aerodynamics and drag for a new electric car to maximise battery life and range; optimising floor plans in retail spaces to retain customers longer; minimising human handling in warehouse facilities for faster processing; or even training AI-enabled robots in a virtual hospital before they ever carry out a real operation. Digital twins allow mistakes to be made, errors spotted or efficiencies realised prior to real-world expense, or dangerous or unforeseen consequences.

Furthermore, once the digital twin and its real-world physical version exist side by side, data points from the real version can be fed back into the digital model to enable testing improvements prior to roll-out, or to better understand the impacts of upgrades. This feedback loop also helps to analyse how small changes may affect the wider system - for instance, how city-wide traffic flow may change if EV charger numbers were doubled at every public parking area.