UK’s DigiTwin seeks better wind energy through better data

Some of the biggest names in global engineering industries – think Airbus and Siemens here – are backing a new technology initiative to support wind energy in the UK that’s called DigiTwin.

The $6.4 million project aims to fill a gap in offshore wind for the UK, according to the University of Sheffield. The UK is getting about 5 percent of its power from wind and that’s set to double by 2020, just a year from now. It remains the global leader, with more than 7,500mW of installed capacity in 2017, says the International Renewable Energy Agency (IRENA).

Yet there’s a problem in managing and maintaining the offshore turbine structures, which decreases how much and how cost-effective the power is. A “digital twin” of the real, three-dimensional working turbine is expected to help resolve that data and modeling gap, and Sheffield is leading a collaboration with five British universities and their industry partners to deliver it.

“Wind turbines – as with other dynamic systems such as aircraft and rotors – typically operate in harsh or even extreme conditions,” says project leader Dr. David Wagg in an interview for the university’s mechanical engineering school.

“Due to the nature of where they operate, it can be particularly difficult to design, monitor and maintain them,” Wagg explains. “This is because the dynamic behavior is very sensitive to changes in the operating environment – such as temperature or humidity – which makes it very difficult to monitor for damage or other operational performance loss. It is also very hard to capture these effects in a computer model for effective design or asset management purposes.”

These challenges make it difficult to develop models that can be fully trusted, which in turn limits efficiency. Most people will never think about this behind-the-scenes work when they plug in their electric vehicles for a charge, or power up a smartphone, but getting the models right is critical to achieving the UK’s CO2 emissions reduction goals.

Wind and related industries know they need improved “virtual duplicates” of existing systems in a connected era of big data, but they don’t quite have them yet. One problem, for example, is the highly complex nature of modern systems. Wagg notes that it’s common for there to be multiple teams of engineers who work on components, with their own models and data assumptions, but their excellent work is still incompatible with that of other teams.

“A common scenario is that the subsystem models cannot be unified into a model of the complete system,” he explains in Benchmark, an industry journal. “In addition, there are typically multiple sources (and formats) of data from existing systems, users, control systems, or test results that could potentially be included into the design or operation process.”

Instead, DigiTwin aims to deliver a complete, theory-of-everything virtual version that works in real-time tandem with the real wind turbines. “Just like weather forecast models, it will continually update from the latest observations, and then make predictions of the short-term future behavior to be expected,” Wagg said.

Beyond wind power, DigiTwin says its modeling technology will have applications in automotive, aerospace and other industries too.