State-augmented deep reinforcement learning for active flow control around an elliptical cylinder
Abstract
We employ reinforcement learning algorithms to develop control strategies for manipulating synthetic jets on an elliptical cylinder, targeting robust flow control across varying aspect ratios (Ar). Our results show that for high aspect ratios (Ar = 1 and 0.75), the learned strategies effectively mitigate vortex shedding, achieving drag reductions of 13.4% and 20.1%, respectively. As the Ar decreases, more complex wake dynamics challenge flow control. For Ar = 0.5 and 0.25, drag reductions of 26.88% and 30.37% are achieved, though vortex suppression becomes increasingly difficult. For extremely slender cylinders (Ar = 0.1), we enhance control performance by enriching dynamic feature representation and using Kalman filtering for state estimation. Unlike conventional methods, our approach successfully achieves complete vortex shedding suppression, a maximum drag reduction of 32.2%, and eliminates periodic oscillations, stabilizing the flow. These results demonstrate the efficacy of state-enhanced RL in controlling highly slender geometries.
Versions
Related
- Quantum control of solid state qubits via Reinforcement Learning
- Toward fully stabilized flow control using deep reinforcement learning
- Optimizing synthetic jet placement for control stability in increasing Reynolds number flows based on deep reinforcement learning
- Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system
- Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning
BibTeX
@misc{jia2026stateaugmented,
title = {{State-augmented deep reinforcement learning for active flow control around an elliptical cylinder}},
author = {Wang Jia and Hang Xu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35731},
eprint = {2610.35731},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35731},
}
RIS
TY - UNPB
TI - State-augmented deep reinforcement learning for active flow control around an elliptical cylinder
AU - Wang Jia
AU - Hang Xu
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35731
UR - https://arxiv.org/abs/2610.35731
AB - We employ reinforcement learning algorithms to develop control strategies for manipulating synthetic jets on an elliptical cylinder, targeting robust flow control across varying aspect ratios (Ar). Our results show that for high aspect ratios (Ar = 1 and 0.75), the learned strategies effectively mitigate vortex shedding, achieving drag reductions of 13.4% and 20.1%, respectively. As the Ar decreases, more complex wake dynamics challenge flow control. For Ar = 0.5 and 0.25, drag reductions of 26.88% and 30.37% are achieved, though vortex suppression becomes increasingly difficult. For extremely slender cylinders (Ar = 0.1), we enhance control performance by enriching dynamic feature representation and using Kalman filtering for state estimation. Unlike conventional methods, our approach successfully achieves complete vortex shedding suppression, a maximum drag reduction of 32.2%, and eliminates periodic oscillations, stabilizing the flow. These results demonstrate the efficacy of state-enhanced RL in controlling highly slender geometries.
ER -