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Video: One important step to solve turbulence

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Go to the profile of Ricardo Vinuesa
Ricardo Vinuesa
Associate Professor, University of Michigan
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Nature
Nature Nature

Classically studied coherent structures only paint a partial picture of wall-bounded turbulence - Nature Communications

The mechanisms governing energy transport and dissipation in turbulent flows have remained only partially understood. Here, the authors introduce a data-driven framework based on explainable deep learning to assess the relative importance of different flow regions.

AI is transforming almost every scientific field, and turbulence is no exception. Many AI methods have been applied to turbulent flows; however, our approach is distinctly different. The goal in turbulence research is not simply to “solve” the Navier-Stokes equations. The ultimate goal is to control turbulent flows in order to reduce drag on vehicles, minimize energy losses in pipelines, enhance turbine efficiency, and design quieter and more efficient aircraft. This is not a million-dollar problem. This is a trillion-dollar problem.

Our method identifies the most important coherent structures in a turbulent flow, the parts of the flow that truly matter for what happens next. This opens the door to understanding the flow and eventually controlling it.

Cremades, Hoyas and Vinuesa: https://www.nature.com/articles/s41467-025-65199-9

Go to the profile of Ricardo Vinuesa
Ricardo Vinuesa
Associate Professor, University of Michigan

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Applied and Technical Physics
Physical Sciences > Physics and Astronomy > Applied and Technical Physics

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