Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder

DENG Xiaolong; HU Gang; CHEN Wenli

doi:10.7638/kqdlxxb-2025.0016

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Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder

更新时间：2026-05-28

- Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder
- Vol. 43, Issue 5, Pages: 92-100(2025)
- 作者机构：
  
  1. 哈尔滨工业大学(深圳) 智能土木与海洋工程学院,深圳,518055
  2. 哈尔滨工业大学土木工程学院,哈尔滨,150096
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0016
  CLC： V214.3;TP181;TU311.3
- Published：2025
- 稿件说明：
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邓晓龙, 胡钢, 陈文礼. 基于深度强化学习的箱梁涡激振动智能流动控制[J]. 空气动力学学报, 2025, 43(5): 92−100.

DENG Xiaolong, HU Gang, CHEN Wenli. Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder[J]. 2025, 43(5): 92-100.
邓晓龙, 胡钢, 陈文礼. 基于深度强化学习的箱梁涡激振动智能流动控制[J]. 空气动力学学报, 2025, 43(5): 92−100. DOI： 10.7638/kqdlxxb-2025.0016

DENG Xiaolong, HU Gang, CHEN Wenli. Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder[J]. 2025, 43(5): 92-100. DOI： 10.7638/kqdlxxb-2025.0016.

摘要

为抑制桥梁涡激振动从而提升桥梁抗风性能，提出了一种基于深度强化学习的智能主动流动控制方法。研究利用合成射流对桥梁节段气动弹性模型尾流场的旋涡脱落进行扰动，从而实现对涡激振动的有效抑制。风洞试验结果验证了桥梁涡激振动在均匀稳定风场中的气动性能，并建立了合成射流激励器控制电压与射流流量的映射关系。通过对控制电压的系统性分析发现，控制电压与射流平均吹气速度近似线性正相关，且更高的控制电压能够显著提升抑制效果。随后，结合软演员-评论家（soft actor-critic

SAC）算法对合成射流的控制策略进行了优化，快速训练得出最优控制电压，并将振幅减少了83%。研究结果表明，合成射流结合深度强化学习算法能够高效抑制桥梁涡激振动，为桥梁风工程的抗风设计提供智能化解决方案。

Abstract

An intelligent active flow control method based on deep reinforcement learning (DRL) was proposed to suppress vortex-induced vibrations and enhance the wind resistance of bridges. This method employed synthetic jets to disturb the wake vortex shedding of an aeroelastic bridge section model

effectively suppressing vortex-induced vibrations. Wind tunnel tests were conducted to validate the aerodynamic performance of the model under uniform and steady wind conditions. Additionally

the relationship between the control voltage and synthetic jet flow rate was established. A systematic analysis showed that the control voltage was approximately linearly and positively correlated with the average jet velocity

with higher control voltages significantly improving the suppression effect. Subsequently

the synthetic jet control strategy was optimized using the Soft Actor-Critic (SAC) algorithm

which converged rapidly to the optimal control voltage

resulting in a maximum reduction of 83% in vibration amplitude. These findings demonstrate that combining synthetic jet technology with DRL algorithms provides an efficient and intelligent solution for suppressing bridge vortex-induced vibrations and offers an intelligent approach for wind-resistant bridge design.

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Keywords

references

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