In January 2022, NASA’s $10 billion James Webb Space Telescope (JWST) was nearing the end of its million-mile journey ( approximately 1.61 million kilometers) from Earth. But reaching its orbital point would be only part of its treacherous journey. To prepare for observations, the spacecraft had to be deployed in a complicated choreography that, according to the calculations of its engineers, could fail in 344 different ways. A parasol the size of a tennis court had to unfold in exactly the perfect way, ending up like a giant, glowing kite under the telescope. A secondary mirror had to be positioned precisely, relying on three legs to stay nearly 25 feet (about 7.6 meters) from the primary mirror.
Finally, the main mirror—with its 18 hexagonal pieces nested together like a honeycomb—had to be assembled. Three segments of gold mirror had to unfold from each side of the telescope, fitting their edges against the twelve already assembled. The sequence had to be perfect for the telescope to work as planned.
“It was a scary moment,” says Karen Casey, technical director of Raytheon’s Air and Space Defense Systems division, which built the software which controls the movements of the JWST and is now in charge of its flight operations.
Over multiple days of choreography, Raytheon engineers watched events unfold as the telescope did. The telescope, beyond lunar orbit, was too far away to be visible, even with powerful instruments. But the telescope sent data to Earth in real time and a computer program used them almost simultaneously to create a 3D video about the development of the process. It was like watching a very distressing movie.
The 3D video represented a “digital twin” of the complex telescope: a computer model of the real instrument, based on the information it provided. “Being able to see it was transformative,” Casey says.
The team watched with tension during the first days of JWST as the 344 potential problems did not appear. Finally, the JWST was in its final form and looked the way it should, both in space and on the screen. Since then, the digital twin has not stopped updating.
The concept of building a full-scale replica of such complicated equipment was not new to Raytheon, in part because of the company’s work in defense and intelligence, where digital twins are more popular than in astronomy.
However, the JWST was actually more complicated than many of those systems, so The advances made possible by its twin now revert to the military side of the business. It is the reverse of a more typical story, in which national security objectives advance science. Defense and non-defense technologies are converging in space, says Dan Isaacs, chief technology officer of the Digital Twin Consortium, a professional working group, and digital twins are “at the very center of these collaborative efforts.”
As technology becomes more common, researchers are finding that these twins are increasingly productive members of scientific society: helping humans operate the world’s most complicated instruments, while also revealing more about the world. own world 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 real-world informationwill accompany the physical item throughout its development.
But the term “digital twin” actually comes from a NASA employee named John Vickers, who first used it in 2010 as part of a report on the space agency’s technology roadmap. Today, as expected, Grieves heads the Digital Twin Institute and Vickers remains at NASA as its chief technologist.
Since those early days, technology has advanced, as usual. The Internet of Things has proliferated, connecting real-world sensors placed on physical objects to the ethereal Internet. Nowadays, These devices total more than 15,000 millionup from a mere million in 2010. Computing power has continued to increase, and the cloud—more popular and powerful than in the previous decade—allows digital twin creators to scale up or down their models, or create more clones to experiment with, without investing in obscene amounts of hardware. Now, furthermore, digital twins can incorporate artificial intelligence and machine learning to help make sense of the avalanche of data arriving every second.
From these ingredients, Raytheon decided to build its JWST twin for the same reason it also works on defense twins: there was little room for error. “It was a no-fail mission,” says Casey. The twin records 800 million pieces of data about his brother in the real world every dayusing all those 0s and 1s to create a real-time video that is easier for humans to control than many columns of numbers.
The JWST team uses the twin to monitor the observatory and predict the effects of changes such as software. To test them, engineers use a copy offline of the twin, upload hypothetical changes and see what happens next. The group also uses a version offline to train operators and solve real-life problems, the nature of which Casey does not want 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 rover Curiosity helped NASA solve the robot’s heat problems. At CERN, Europe’s particle accelerator, digital twins help in the development of detectors and in more mundane tasks such as 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 largest single-mirror telescope in the world, the scientific team began building a twin about two years ago, before even having heard the term. At that time, Luis Rodríguez, head of engineering, went to Romano Corradi, director of the observatory. “He said we should start interconnecting things,” says Corradi. Rodríguez suggested that they could take advantage of the principles of industry, where machines regularly communicate with each other and with computersthey control their own states and automate responses to those states.
The team began adding sensors that transmitted information about the telescope and its surroundings. Knowing the environmental conditions surrounding an observatory is “fundamental to operating a telescope,” says Corradi. Is it going to rain, for example, and how does temperature affect the telescope’s focus?
Once the sensors had the data online, they created a 3D model of the telescope that displayed this data visually. “The advantage is very clear for the workers,” says Rodríguez, referring to those who operate the telescope. “It is easier to handle the telescope. Before the telescope was very, very difficult because it is very complex.”
Right now, the Large Telescope’s twin is limited to ingesting the data, but the team is working toward a more interpretive approach, using AI to predict instrument behavior. “With the 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 automatically responds to its situation.
Corradi says the team didn’t find out that what they were building had a name until they attended an Internet of Things conference last year. “We saw that there was a growing community in the industry —and not in science, but in industry—where everyone is doing these digital twins,” he says.
The concept is being introduced into science, as demonstrated by particle accelerators and space agencies. But in companies it is more deeply rooted. “The interest of industry always precedes that of science,” says Corradi. But he believes projects like his will continue to proliferate in the broader astronomical community. For example, 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 were a little bit ahead of what was already happening in the industry,” Corradi says.
The defense industry loves digital twins. The Space Force, for example, used one to plan Tetra 5, an experiment to refuel satellites. In 2022, the Space Force also awarded Slingshot Aerospace a contract to create a digital twin of space itself, showing what happens in orbit to prepare for incidents such as collisions.
Isaacs cites an example in which the Air Force sent a retired aircraft to a university so researchers could develop a “fatigue profile,” a kind of map of how the stresses, stresses and loads on the aircraft build up over the course of the year. weather. A twin, made from that map, can help identify parts that could be replaced to extend the life of the plane, or to design a better plane in the future. Companies that are dedicated to both defense and science – common in the space industry in particular – thus have an advantage, since they can transfer innovations from one department to another.
The JWST twin, for example, will have some relevance to Raytheon’s defense projects., where the company is already working on digital twins of missile defense radars, air-launched cruise missiles and aircraft. “We can reuse parts in other places,” Casey says. Any satellites the company tracks or sends orders to “could benefit from parts of what we’ve done here.”
Some of the tools and processes Raytheon developed for the telescope, he continues, “can be copied and pasted into other programs.” And just like that, the JWST digital twin will likely have twins of its own.
Sarah Scoles is a science journalist based in Colorado and author, most recently, of the book ‘Countdown: The Blinding Future of Nuclear Weapons’.
