Digital twins are helping scientists run the world’s most complex experiments
In January 2022, NASA’s $10 billion James Webb Space Telescope was approaching the end of its one-million-mile trip from Earth. But reaching its orbital spot would be just one part of its treacherous journey. To ready itself for observations, the spacecraft had to unfold itself in a complicated choreography that, according to its engineers’ calculations, had 344 different ways to fail. A sunshield the size of a tennis court had to deploy exactly right, ending up like a giant shiny kite beneath the telescope. A secondary mirror had to swing down into the perfect position, relying on three legs to hold it nearly 25 feet from the main mirror.
Finally, that main mirror—its 18 hexagonal pieces nestled together as in a honeycomb—had to assemble itself. Three golden mirror segments had to unfold from each side of the telescope, notching their edges against the 12 already fitted together. The sequence had to go perfectly for the telescope to work as intended.
“That was a scary time,” says Karen Casey, a technical director for Raytheon’s Air and Space Defense Systems business, which built the software that controls JWST’s movements and is now in charge of its flight operations.
Over the multiple days of choreography, engineers at Raytheon watched the events unfold as the telescope did. The telescope, beyond the moon’s orbit, was way too distant to be visible, even with powerful instruments. But the telescope was feeding data back to Earth in real time, and software near-simultaneously used that data to render a 3D video of how the process was going, as it was going. It was like watching a very nerve-racking movie.
The 3D video represented a “digital twin” of the complex telescope: a computer-based model of the actual instrument, based on information that the instrument provided. “This was just transformative—to be able to see it,” Casey says.
The team watched tensely, during JWST’s early days, as the 344 potential problems failed to make their appearance. At last, JWST was in its final shape and looked as it should—in space and onscreen. The digital twin has been updating itself ever since.
The concept of building a full-scale replica of such a complicated bit of kit wasn’t new to Raytheon, in part because of the company’s work in defense and intelligence, where digital twins are more popular than they are in astronomy.
JWST, though, was actually more complicated than many of those systems, so the advances its twin made possible will now feed back into that military side of the business. It’s the reverse of a more typical story, where national security pursuits push science forward. Space is where non-defense and defense technologies converge, says Dan Isaacs, chief technology officer for the Digital Twin Consortium, a professional working group, and digital twins are “at the very heart of these collaborative efforts.”
As the technology becomes more common, researchers are increasingly finding these twins to be productive members of scientific society—helping humans run the world’s most complicated instruments, while also revealing more about the world itself and the universe beyond.
800 million data points
The concept of digital twins was introduced in 2002 by Michael Grieves, a researcher whose work focused on business and manufacturing. He suggested that a digital model of a product, constantly updated with information from the real world, should accompany the physical item through its development.
But the term “digital twin” actually came from a NASA employee named John Vickers, who first used it in 2010 as part of a technology road map report for the space agency. Today, perhaps unsurprisingly, Grieves is head of the Digital Twins Institute, and Vickers is still with NASA, as its principal technologist.
Since those early days, technology has advanced, as it is wont to do. The Internet of Things has proliferated, hooking real-world sensors stuck to physical objects into the ethereal internet. Today, those devices number more than 15 billion, compared with mere millions in 2010. Computing power has continued to increase, and the cloud—more popular and powerful than it was in the previous decade—allows the makers of digital twins to scale their models up or down, or create more clones for experimentation, without investing in obscene amounts of hardware. Now, too, digital twins can incorporate artificial intelligence and machine learning to help make sense of the deluge of data points pouring in every second.
Out of those ingredients, Raytheon decided to build its JWST twin for the same reason it also works on defense twins: there was little room for error. “This was a no-fail mission,” says Casey. The twin tracks 800 million data points about its real-world sibling every day, using all those 0s and 1s to create a real-time video that’s easier for humans to monitor than many columns of numbers.
The JWST team uses the twin to monitor the observatory and also to predict the effects of changes like software updates. When testing these, engineers use an offline copy of the twin, upload hypothetical changes, and then watch what happens next. The group also uses an offline version to train operators and to troubleshoot IRL issues—the nature of which Casey declines to identify. “We call them anomalies,” she says.
Science, defense, and beyond
JWST’s digital twin is not the first space-science instrument to have a simulated sibling. A digital twin of the Curiosity rover helped NASA solve the robot’s heat issues. At CERN, the European particle accelerator, digital twins help with detector development and more mundane tasks like monitoring cranes and ventilation systems. The European Space Agency wants to use Earth observation data to create a digital twin of the planet itself.
At the Gran Telescopio Canarias, the world’s largest single-mirror telescope, the scientific team started building a twin about two years ago—before they’d even heard the term. Back then, Luis Rodríguez, head of engineering, came to Romano Corradi, the observatory’s director. “He said that we should start to interconnect things,” says Corradi. They could snag principles from industry, suggested Rodríguez, where machines regularly communicate with each other and with computers, monitor their own states, and automate responses to those states.
The team started adding sensors that relayed information about the telescope and its environment. Understanding the environmental conditions around an observatory is “fundamental in order to operate a telescope,” says Corradi. Is it going to rain, for instance, and how is temperature affecting the scope’s focus?
After they had the sensors feeding data online, they created a 3D model of the telescope that rendered those facts visually. “The advantage is very clear for the workers,” says Rodríguez, referring to those operating the telescope. “It’s more easy to manage the telescope. The telescope in the past was really, really hard because it’s very complex.”
Right now, the Gran Telescopio twin just ingests the data, but the team is working toward a more interpretive approach, using AI to predict the instrument’s behavior. “With information you get in the digital twin, you do something in the real entity,” Corradi says. Eventually, they hope to have a “smart telescope” that responds automatically to its situation.
Corradi says the team didn’t find out that what they were building had a name until they went to an Internet of Things conference last year. “We saw that there was a growing community in industry—and not in science, in industry—where everybody now is doing these digital twins,” he says.
The concept is, of course, creeping into science—as the particle accelerators and space agencies show. But it’s still got a firmer foothold at corporations. “Always the interest in industry precedes what happens in science,” says Corradi. But he thinks projects like theirs will continue to proliferate in the broader astronomy community. For instance, the group planning the proposed Thirty Meter Telescope, which would have a primary mirror made up of hundreds of segments, called to request a presentation on the technology. “We just anticipated a bit of what was already happening in the industry,” says Corradi.
The defense industry really loves digital twins. The Space Force, for instance, used one to plan Tetra 5, an experiment to refuel satellites. In 2022, the Space Force also gave Slingshot Aerospace a contract to create a digital twin of space itself, showing what’s going on in orbit to prepare for incidents like collisions.
Isaacs cites an example in which the Air Force sent a retired plane to a university so researchers could develop a “fatigue profile”—a kind of map of how the aircraft’s stresses, strains, and loads add up over time. A twin, made from that map, can help identify parts that could be replaced to extend the plane’s life, or to design a better plane in the future. Companies that work in both defense and science—common in the space industry in particular—thus have an advantage, in that they can port innovations from one department to another.
JWST’s twin, for instance, will have some relevance for projects on Raytheon’s defense side, where the company already works on digital twins of missile defense radars, air-launched cruise missiles, and aircraft. “We can reuse parts of it in other places,” Casey says. Any satellite the company tracks or sends commands to “could benefit from piece-parts of what we’ve done here.”
Some of the tools and processes Raytheon developed for the telescope, she continues, “can copy-paste to other programs.” And in that way, the JWST digital twin will probably have twins of its own.
Sarah Scoles is a Colorado-based science journalist and the author, most recently, of the book Countdown: The Blinding Future of Nuclear Weapons.
