A deep reinforcement learning controller achieves 27.7% drag reduction at Re_tau about 1000 in DNS of turbulent channel flow, surpassing opposition control and pointing to a virtual-wall mechanism.
Applied Mechanics Reviews 67 (5), 050801
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.flu-dyn 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Reinforcement-learning-based control of turbulent channel flows at high Reynolds numbers
A deep reinforcement learning controller achieves 27.7% drag reduction at Re_tau about 1000 in DNS of turbulent channel flow, surpassing opposition control and pointing to a virtual-wall mechanism.