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Discovering drag reduction strategies in wall-bounded turbulent flows using deep reinforcement learning

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DDEW03 - Computational Challenges and Emerging Tools

Deep reinforcement learning (DRL) is a mathematical framework that has been used to design and learn control policies in different domains, and several applications in physics research have been proposed, as well. Here we introduce a reinforcement learning (RL) environment to design control strategies for drag reduction in turbulent fluid flows enclosed in a channel. The control is applied in the form of blowing and suction at the wall, while the observable state is the velocity in the streamwise and wall-normal directions, at a given distance from the wall.Given the complex nonlinear nature of turbulent flows, the control strategies proposed so far in the literature are physically grounded, but too simple. DRL , by contrast, enables leveraging the high-dimensional data that can be sampled from flow simulations to design advanced control strategies.In an effort to establish a benchmark for testing data-driven control strategies, we compare opposition control, the state-of-the-art turbulence-control strategy from the literature, and a commonly-used DRL algorithm, deep deterministic policy gradient. Our results show that DRL leads to 43% and 30% drag reduction in a minimal and a larger channel (at a friction Reynolds number of 180), respectively, outperforming the classical opposition control by around 20 and 10 percentage points, respectively. Co-authors: Jean Rabault (Norwegian Meteorological Institute), Philipp Schlatter (KTH – Royal Institute of Technology), Hossein Azizpour (KTH – Royal Institute of Technology) and Ricardo Vinuesa (KTH – Royal Institute of Technology)

This talk is part of the Isaac Newton Institute Seminar Series series.

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