用深度强化学习实时控制高雷诺数下涡激振动，抑制率超95%。
Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
- 基于位移与速度反馈，学习低频旋转控制策略。
- 结合历史控制动作，实现高频控制，振动抑制率达95%以上。
- 突破执行延迟限制，适合实际流体控制场景应用。
本研究展示了深度强化学习（DRL）在高雷诺数（Re = 3000）条件下，通过旋转执行器对圆柱体涡激振动（VIV）进行主动流动控制（AFC）的实验部署。区别于以往依赖低雷诺数数值模拟的研究，该工作在具有挑战性的实验环境中实现了实时控制，并成功应对了执行器延迟等实际约束。当仅使用振荡圆柱体的位移和速度作为状态反馈时，DRL代理学习到一种低频旋转控制策略，利用传统锁相现象，实现最高达80%的振动抑制。尽管如此，其性能仍低于高频旋转执行器的效果。性能差异归因于执行延迟，可通过向学习算法引入过去控制动作加以缓解。这使代理能够学习高频旋转控制策略，有效调控涡脱附，实现超过95%的振动衰减。结果表明，DRL在真实实验中具备自适应能力，可克服如执行滞后等仪器限制。
原文摘要 · Abstract (English)
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.
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