Another Good Intro to Digital Twins

Well done, overview that is only minimally technical.

Virtual Duplicates, By Neil Savage,   Communications of the ACM, February 2022, Vol. 65 No. 2, Pages 14-16  10.1145/3503798

Back in 1970 during the seventh crewed mission of the Apollo space program (the third intended to land on the Moon), the three astronauts aboard Apollo 13 were calmly going about their duties when an explosion in an oxygen tank rocked the spacecraft, spilling precious air into space and damaging the main engine. Personnel in Mission Control suddenly had to devise a plan to get the crew home, and to do that they had to understand what condition the damaged ship was in and the materials available for repairs, and then test what the astronauts might be able to accomplish.

To figure it out, they turned to the flight simulators used to plan and rehearse the mission. They updated the simulators with current information about the physical state of Apollo 13 and tried various scenarios, eventually coming up with the plan that safely returned the astronauts to Earth. This was, some argue, the first use of a digital twin, a model that simulated the state of a physical system with real-time data and made predictions about its performance under varied conditions.

Digital twins are growing in popularity, especially as the Internet of Things provides data from sensors in all sorts of places. The concept is being applied in a range of areas, from buildings to bridges, from wind turbines to aircraft, from weather systems to the human heart.

A digital twin is more than just a simulation of some arbitrary object or system. "It's not a generic model of an airplane or a car or wind turbine or a generic person," says Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (UT Austin). "It's a personalized model of one particular aircraft or one particular person."

To qualify as a digital twin, Willcox says, the model needs to take into account current information about the state of the system and evolve over time as it is updated with new data about the system. Another distinguishing feature is that the model and the data help people make decisions about the system, which in turn can change the data and require the model to be updated again.

Perhaps the most obvious use of digital twins is to monitor the long-term health of expensive or complex equipment, such as engines, manufacturing equipment, or industrial heating, ventilation, and air conditioning (HVAC) systems. That sort of use is increasingly being touted as part of Industry 4.0, which incorporates digital technology, machine learning, and big data to improve industrial processes.

IBM, for instance, is combining sensors with its Watson artificial intelligence technology to help large companies make decisions about what maintenance to perform and when, to extend the lifetime of equipment and cut costs. In one example, the company created a digital twin of an engine blade in a 777 aircraft to monitor when it begins to degrade and requires upgrade or replacement. Similarly, GE created digital twins of its wind turbines to predict when the equipment will need maintenance and builds that into a schedule, so wind farm operators can address issues before a turbine breaks, avoiding costly downtime. Researchers at Siemens are applying a similar approach to the human body, developing digital twins of individual human hearts they hope could predict the effectiveness of a specific therapy for a particular patient, instead of merely relying on statistics about hearts in general. .... '