Deep learning used to simulate aircraft turbulence

By Paddy Smith
University of Illinois researchers develop physics AI to shake up understanding of in-flight turbulence and other physical phenomena...

Scientists from the University of Illinois have made a breakthrough in representing physics through AI.

The researchers focused on aircraft turbulence to test their model, which was able to successfully simulate the physics of the phenomenon without knowing its precise mathematics.

They hope their method will help aerospace engineers to design aircraft and spacecraft prototypes which respond to wind currents more effectively. It may also have further applications in industry for models with uncertain mathematical details.

The study – DPM: a deep learning PDE augmentation method with application to large-eddy simulation – was published in the Journal of Computational Physics. It is part of the Blue Waters supercomputer project.


Jonathan Freund, head of the department of aerospace engineering at the University of Illinois, said, “We don’t know how to mathematically write down all of turbulence in a useful way. There are unknowns that cannot be represented on the computer, so we used a machine learning model to figure out the unknowns. We trained it on both what it sees and the physical governing equations at the same time as a part of the learning process. That’s what makes it magic and it works.

“It’s an old problem. People have been struggling to simulate turbulence and to model the unrepresented parts of it for a long time.

“We learned that if you try to do the machine learning without considering the known governing equations of the physics, it didn’t work. We combined them and it worked.

‘Simulations for any physical phenomena’

“Anyone who wants to do simulations of physical phenomena might use this new method. They would take our approach and load data into their own software. It’s a method that would admit other unknown physics. And the observed results of that unknown physics could be loaded in for training.

“The turbulent flow we used to demonstrate the method is a very simple configuration. Real flows are more complex. I’d also like to use the method for turbulence with flames in it – a whole additional type of physics. It’s something we plan to continue to develop in the new Center for Exascale-enabled Scramjet Design, housed in NCSA.

“Universities were very active in the first turbulence simulations, then industry picked them up. The first university-based large-eddy simulations looked incredibly expensive in the 80s and 90s. But now companies do large-eddy simulations. We expect this prediction capability will follow a similar path. I can see a day in the future with better techniques and faster computers that companies will begin using.”


Featured Articles

AI in SOC: Where Should Security Teams Look to Apply It?

As threats evolve, AI's continuous learning ensures robust protection that can prove invaluable for security operations centres

Swiss Re: Pharma, Not IT, to See Most Adverse Effects of AI

Swiss Re' AI report revealed surprising results showing pharmaceuticals stands to be the most adversely effected industry from the applications of AI

AI Safety Summit Seoul: Did it Meet Industry Expectations?

Before the summit, there were high hopes for meaningful outcomes - we see if industry leaders like EY's Beatriz Sanz Saiz thinks so

IBM's Salesforce Partnering Shows watsonx's Enterprise Reach

AI Strategy

Are Business and Government Diverging on AI Safety?

AI Strategy

Alteryx Industry-First AI Copilot Sees New Era of Analytics

AI Applications