In the 1997 disaster movie Volcano, when Tommy Lee Jones wants to find out why some Los Angeles Department of Public Works employees were scalded to death in a storm drain near MacArthur Park, he goes to the site with a batch of rolled-up drawings under his arm.
Today, Jones wouldn’t have to visit the site or carry physical drawings. He’d be able to access drawings of Los Angeles’ storm sewer and steam pipe systems from any place he could get online. And, if he could access digital twins of the systems, he could simulate puncturing a steam pipe and, when the simulation showed that it wouldn’t release enough thermal energy to kill the workers, realize they must have been killed by something that could, such as an imminent volcanic eruption.
That scenario may sound as far-fetched as Volcano itself, but the types of digital twins that Jones would have needed to foresee that Los Angeles was on the verge of becoming Pompei West are real. In fact, they’re being deployed across the globe by organizations in industries that Casne serves, such as power generation, water and wastewater treatment, aerospace manufacturing and transportation.
Digital twins are, as their name suggests, digital replicas of physical objects, systems, processes and places. For example, there could be digital twins of individual wind turbines; the transmission systems to which they’re connected; the process of generating wind power; and places where wind farms are, or might be, located.
The uses to which digital twins can be put range from simple to complex depending, among other things, on the amount of data they’re based on and the timeliness of that data. For example, in Volcano, if the digital twins Jones could access were based only on data that was input when L.A.’s storm sewer and steam pipe systems were built and updated, the simulation he could run might be able to tell him that a punctured steam pipe couldn’t have killed workers in a storm drain by MacArthur Park but little else. If, however, they were being fed data in real time from a raft of sensors deployed throughout L.A.’s storm sewer and steam pipe systems, they also could let him know that something very hot was heading in his direction posthaste.
Bringing that analogy back to the real world, a digital twin of a wind turbine could be used to show how the wind turbine would behave in various conditions, such as high winds and freezing rain, thereby giving its operator a better idea of how to manage it safely when those conditions occur. Additionally, if it were being updated with real-time data from the wind turbine it’s digitally representing, it could enable someone to remotely diagnose a problem with that wind turbine (although the person likely would have to go to the wind turbine to fix the problem).
Digital twins enable us to go virtually where we can’t easily go in the actual universe, from atop a mountain to outer space, so we can better accomplish what we’re trying to do, regardless of whether that’s generating power or journeying to the moon. In fact, the virtual models of space capsules that NASA made in the 1970s are considered by some to be the first digital twins. The idea of using digital twins in manufacturing was first suggested at a 2002 Society of Manufacturing Engineers conference by Michael Grieves, then a professor at the University of Michigan, and NASA’s John Vickers coined the term “digital twin” in a 2010 report.
Grieves, who is now the Florida Institute of Technology’s chief scientist of advanced manufacturing and executive vice president of operations, gave the keynote speech at the American Society of Manufacturing Engineers’ Digital Twin Summit, which was held virtually in November 2020. His topic was intelligent digital twins, which use artificial intelligence and machine learning to grow smarter, and which he thinks are just starting to come into existence.
“Right now, my perception is we’re in the conceptual stage of digital twins,” Grieves said in a January 2021 article for ASME’s website. “We have this information that we can bring together to create this virtual version of real-world environments based on models and behavioral aspects and modeling and simulation. The next step is to have all this information be pulled together automatically and intelligently, and we’re starting to see that occur as the software capabilities begin to arise.”
As that occurs, digital twins that can enable and improve predictive maintenance for everything from individual parts to entire systems will be more widely adopted. And more futuristic concepts, such as digital twins of substations that workers wearing virtual reality headsets can train in and perform some tasks in, will become reality.
Intelligent digital twins are possible due not just to advances in machine learning and artificial intelligence, but also to the growth in connected equipment, often referred to as the Industrial Internet of Things or IIoT. At the same time, the growth in demand for intelligent digital twins is spurring the growth of the IIoT, as industries deploy sensors so they can create smarter virtual versions of their equipment, buildings and systems, thereby enabling themselves to operate more efficiently and effectively.
This confluence of technological phenomena is resulting in digital transformations of the energy sector, the manufacturing sector and industry in general that have been dubbed Energy 4.0, Manufacturing 4.0 and Industry 4.0 because they’re considered to be part of a fourth industrial revolution, following the replacement of manual labor with machine labor; the electrification of mechanical processes; and the automation and computerization of many of those processes.
Regardless of what they’re called, these transformations are occurring and digital twins are an integral part of them. In the remaining blogs in this series, Casne will look at how the industries we serve are using digital twins; how existing systems in those industries, such as SCADA and BMS can work with digital twins; and how digital twins can enhance productivity, profitability and worker safety, among many other things.
What’s more, we promise we won’t reference any more 1990s disaster films in doing it.