mlsanityData to 14 Oct 2026

Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations

arXiv 2509.24556 ↗First posted 29 Sep 2025cs.LG cs.AI physics.flu-dyncited by 0

On the map: Deep reinforcement learning methods → RL with diffusion and flow models

Abstract (arXiv)

This study showcases an experimental deployment of deep reinforcement learning (DRL) for active flow control (AFC) of vortex-induced vibrations (VIV) in a circular cylinder at a high Reynolds number (Re = 3000) using rotary actuation. Departing from prior work that relied on low-Reynolds-number numerical simulations, this research demonstrates real-time control in a challenging experimental setting, successfully addressing practical constraints such as actuator delay. When the learning algorithm is provided with state feedback alone (displacement and velocity of the oscillating cylinder), the DRL agent learns a low-frequency rotary control strategy that achieves up to 80% vibration suppression which leverages the traditional lock-on phenomenon. While this level of suppression is significant, it remains below the performance achieved using high-frequency rotary actuation. The reduction in performance is attributed to actuation delays and can be mitigated by augmenting the learning algorithm with past control actions. This enables the agent to learn a high-frequency rotary control strategy that effectively modifies vortex shedding and achieves over 95% vibration attenuation. These results demonstrate the adaptability of DRL for AFC in real-world experiments and its ability to overcome instrumental limitations such as actuation lag.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 54 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

4 of 54 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Efficient collective swimming by harnessing vortices through deep reinforcement learningcited by 73
  3. 2024Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convectioncited by 8
  4. 2025Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000cited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Active Flow Control Strategies Through Deep Reinforcement Learning
  2. 2024Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
  3. 2026Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
  4. 2024Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
  5. 2024Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control
  6. 2024Model-based deep reinforcement learning for accelerated learning from flow simulations
  7. 2026Heuristic Learning for Active Flow Control Using Coding Agents
  8. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics