Advanced Technique for Developing Digital Twins Makes Tech Universally Applicable

AUSTIN, Texas – A universally applicable digital twin mathematical model has been co-developed by researchers at The University of Texas at Austin that could be used for systems as diverse as a spacecraft, a person or even an entire city.

In 1970 NASA’s Apollo 13 mission to the moon had to be abandoned after one of the oxygen tanks on board the spacecraft exploded, redirecting the crew’s attention to survival.

Although the term was not coined for another 40 years, it is now understood that Mission Control’s use of Apollo spacecraft simulators to help guide the astronauts safely back to Earth was perhaps the first time “digital twin” technology had been used.

Now, the technology has improved significantly through advanced mathematical modeling techniques, better sensors and more powerful supercomputers. New research conducted by experts from the Oden Institute for Computational Engineering and Sciences, Massachusetts Institute of Technology (MIT) and industry partner The Jessara Group was published in the latest edition of Nature Computational Science. The paper outlines the foundations for a mathematical model that could be used to enable predictive digital twins at various scales and for various situations.

“Digital twins have already been developed for use in specific contexts – like that of a particular engine component or a particular spacecraft mission, but missing has been the foundational mathematical framework that would enable digital twins at scale,” said Karen Willcox, director of the Oden Institute and senior author on the paper.

A digital twin is a computational model that evolves over time and continuously represents the structure, behavior and context of a unique physical “asset” such as a spacecraft, a person or even an entire city.

Tailored computational models that reflect the unique characteristics of individual assets enable decision-making that is optimized to the individual rather than based on averages across populations. When is the right time to bring an unmanned aerial vehicle in for servicing? How should your house optimize its energy usage today? Is it time for you to go to the doctor for a thorough check-up?

On the macro scale, smart cities enabled by digital twins and Internet of Things (IoT) devices promise to revolutionize urban planning, resource allocation, sustainability and traffic optimization.

It is difficult to grasp the idea that the same mathematical model could be applied in situations as seemingly disparate as the human body, a space rocket or a building. However, according to Michael Kapteyn, lead author and a doctoral student at MIT, they all share similarities that can be exploited when reduced to mathematical models.

“This is where the power of mathematical abstraction comes into play. Using probabilistic graphical models, we create a mathematical model of the digital twin that applies broadly across application domains,” Kapteyn said.

These applications have their own unique requirements, challenges and desired outcomes. But they share commonalities too. There is a set of parameters that describe the state of the system – the structural health of an aircraft wing or the current capacity of an urban roadway. There are data provided by in situ sensors, inspections and other observations of the system. And there are control actions that a decision maker can take. It is the interactions between these quantities – the state, the observational data and the control actions – that the new digital twin model represents mathematically.

The researchers used the new approach to create a structural digital twin of a custom-built unmanned aerial vehicle instrumented with state-of-the-art sensing capabilities. “The value of integrated sensing solutions has been recognized for some time, but combining them with the digital twin concept takes that to a new level,” said Jacob Pretorius, chief technology officer of The Jessara Group and co-author on the Nature paper. “We are on the cusp of an exciting future for intelligent engineering systems.”

The study was funded by the Air Force Office of Scientific Research, the SUTD-MIT International Design Centre, and the Department of Energy Advanced Scientific Computing Research program.

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