What is a digital twin?

The advent of supercomputers, machine learning algorithms and artificial intelligence means that our ability to simulate and recreate real life scenarios is becoming ever more precise. And with that comes the question: can we save time, money and effort on improving our current infrastructure by simulating changes on near perfect digital copies, and then taking what worked best and applying it to their real world counterparts? That’s the thinking behind digital twins.

Will - To make a digital twin, you need a few things. First, a physical object or system that you want to replicate and monitor. That could be a building, an engine, a patch of woodland, pretty much anything. Then you need a way of monitoring that object. If it's an engine that could be checking changes in temperature. If it was a building, it could be measuring the number of people walking through it. Finally, you need a computer model that takes this sensory data and shows how it affects the physical object. If you've got all three, you've got yourself a digital twin. Here to talk us through more is the University of Cambridge's Richard Mortier.

Richard - People have obviously done modelling of complex systems, complex environments for decades. Building engines, building bridges, you will have a model of that. You're then going to engineer and build and deploy. And people have deployed monitoring infrastructure in physical systems for decades where you will have a heating system or something. You'll have various sensors in there that will tell you when something's going wrong or when maintenance needs to be done. But I think it's this bringing those two things together and coupling them together so that you can be continually feeding off the data that's coming out of the physical system and feeding that into the model, but then also feeding insights from the model into how you're managing and maintaining indeed through the whole life cycle. So it might be how, not just how you're managing and maintaining the system, but indeed how you're developing new instances of the system, perhaps how you're prototyping technologies before they're deployed. But that's the essence of it. It's this coupling behaviour between the statistical model and data and the physical system is what I would think of as additional to it.

Will - So already then you can imagine that creating any useful form of digital twin is going to require a lot of data, but obviously with such a wide array of possibilities, the amount of data involved heavily depends on what you are twinning.

Richard - Digital twins can be built for a very wide range of scales of things if you like. So you can have a digital twin for a bridge, or an engine, or the air traffic control system across the UK. So there's a very different set of scales that you can apply this kind of idea to. And kind of along with that, you can apply it at different points in the life cycle of those things. So you might have an additional twin that you start out with before you've actually even built the real thing. Or you may have only built components of the thing. You may be using a previous version of the thing that you're building a digital twin of, in order to feed data into the twin to see how the next version might be built and see how it might behave. You then may have digital twins that you build for particular instances of that thing. So you might have one for each of the engines that you have, you've sold around the planet, for example. And then you may also try to aggregate information from those digital twins because you'll obviously be obtaining information from the different instances as they are being used in practice and as they are evolving in their use in practice. And as those systems are degrading or changing behaviour as time passes as they get used. And so you can sort of look at the aggregate across those to try and build some idea of what would happen on average or what would happen, what the range of variation might be in the way that these physical systems behave.

Will - And this idea, whilst cutting edge, has been around for many years. In fact, the first digital twin may well have been that of the stranded Apollo 13 space mission, as the ground team created a twin of the tools that the astronauts had and found a way of getting them back to Earth. It's a great story if true, but whatever the case, the concept is certainly not new.

Richard - I think that it's probably one of those things which has kind of snuck up on people to an extent. In the sense that people have run simulations of large scale systems for, as I said, for decades and decades. The Met Office has been trying to predict weather for a very long time. And in some sense you can view that as a kind of digital twin for the weather system. But I suppose people didn't call it a digital twin until relatively speaking, relatively recently.

Will - So nowadays with all of our fancy AI and computer modelling, what does a digital twin look like? Are engineers congregating around holographic 3D representations of engines and moving parts around in virtual reality?

Richard - I suppose ultimately, at least in my head, a digital twin looks like, in most cases it'll be the physical thing itself and the sensing attached to that so that you've got the data feeds coming out of it, and then it looks like some software running on a computer.

Will - Oh… Well that's fine. I, you, you know, that's, that's not the point. The point is they're capable of taking data from a system and seeing where it can be maintained and improved.

Richard - Yes. That, or equally it's important that the line that comes out of the computer that says this wind turbine is going to fail in three days time and it's something that you can act upon and it doesn't need to have a fancy graphic, but you do need to see that information coming out of it.

Will - Okay, the visuals might need to work, but the technology is still here and very impressive. So where might we be headed?

Richard - So one of the things I'm interested in is seeing how, as usual with some of these technologies, I think how they migrate from the very kind of big ticket, high value projects where it's worthwhile people really, really doing the work and putting the investment in to understand these things. And as that technology starts to trickle down into more kinds of everyday environments, so the energy grid for example, you really, it's, it's worthwhile spending quite a lot of time and money to really understand how that's going to work and how that's going to function. But if some of that technology might be deployed into sort of the, you might start out looking at power stations or something really big and important and expensive like that. See what that technology could then be moved into a more domestic context, for example, particularly as we start to move to more microgeneration of energy with solar panels on people's houses and things like that. The degree to which those sorts of approaches will kind of become more pervasive in everyday life.