View a satellite image of a hurricane and you’ll quickly realize how pervasive and powerful turbulence can be.
Beyond superstorms, understanding the dynamics of swirling fluids could help engineers improve everything from airplane wings to artificial organs—and yes, even predicting the weather.
Michael Graham, a professor of chemical and biological engineering at the University of Wisconsin-Madison, is a leader in this type of research, and a recently awarded U.S. Department of Defense Vannevar Bush Faculty Fellowship is enabling this potentially transformative turbulence research.
“This is a big opportunity to integrate fundamental ideas with applications,” says Graham, who is a Vilas Distinguished Achievement Professor and Harvey D. Spangler Professor at UW-Madison.
Given that turbulence causes drag, which is a major concern for airplane design, Graham’s work could lead to faster jets—and insights into how different geometries shape turbulence patterns might also help usher in more fuel-efficient cars. Turbulence also plays a role in global climate, as a major mixer for the air that shuttles heat between the Earth’s surface and the upper layers of the atmosphere.
Yet even though turbulence is present in almost every fluid moving near a surface, scientists still don’t understand many basic features about what causes flows to develop dramatic swirls and whirls.
Graham’s ambitious project attempts to increase that understanding. It and runs the gamut—from basic theoretical research to applied problems.
With $3 million of support spanning five years, Graham and colleagues will take a deep dive into the dynamic forces that control turbulence, which roils almost every moving fluid on the planet, from air in the atmosphere to blood circulating in human veins.
The team will work out new mathematical models for describing flow dynamics. The researchers also will train machine-learning algorithms to predict turbulent behavior. Armed with new theoretical knowledge, the researchers will develop turbulence manipulation strategies to fine-tune fluid flow.
The motion of turbulent fluids seems entirely random, at first glance, but structured repeating patterns do emerge over time. The interplay between predictable structure and random motion has long made turbulence one of science’s greatest mysteries.
“We still don’t know many things, like how to manipulate flow to reduce drag,” says Graham.
Graham’s research builds on his expertise in using advanced computational and theoretical approaches to understand turbulence. He will make those computational models even more formidable by leveraging machine learning, which is a powerful tool for parsing patterns to predict future outcomes.
“I’m excited because the Department of Defense is really encouraging scientists to follow their curiosity,” says Graham, who was selected as one of 11 new fellows for 2018, “Because our work is very fundamental, it could be applicable in many different situations.”
Author: Sam Million-Weaver