Discovering Drag Reduction Strategies in Wall-Bounded Turbulent Flows Using Deep Reinforcement Learning
Abstract
The control of turbulent fluid flows represents a problem in several engineering applications. The chaotic, high-dimensional, non-linear nature of turbulence hinders the possibility to design robust and effective control strategies. In this work, we apply deep reinforcement learning to a three-dimensional turbulent open-channel flow, a canonical flow example that is often used as a study case in turbulence, aiming to reduce the friction drag in the flow. By casting the fluid-dynamics problem as a multi-agent reinforcement-learning environment and by training the agents using a location-invariant deep deterministic policy gradient algorithm, we are able to obtain a control strategy that achieves a remarkable 30\% drag reduction, improving over previously known strategies by about 10 percentage points.
Cite
Text
Guastoni et al. "Discovering Drag Reduction Strategies in Wall-Bounded Turbulent Flows Using Deep Reinforcement Learning." ICLR 2023 Workshops: Physics4ML, 2023.Markdown
[Guastoni et al. "Discovering Drag Reduction Strategies in Wall-Bounded Turbulent Flows Using Deep Reinforcement Learning." ICLR 2023 Workshops: Physics4ML, 2023.](https://mlanthology.org/iclrw/2023/guastoni2023iclrw-discovering/)BibTeX
@inproceedings{guastoni2023iclrw-discovering,
title = {{Discovering Drag Reduction Strategies in Wall-Bounded Turbulent Flows Using Deep Reinforcement Learning}},
author = {Guastoni, Luca and Rabault, Jean and Schlatter, Philipp and Vinuesa, Ricardo and Azizpour, Hossein},
booktitle = {ICLR 2023 Workshops: Physics4ML},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/guastoni2023iclrw-discovering/}
}