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大跨度桥梁在中低风速作用下易发生涡激共振（涡振）.该类振动源于桥梁断面尾流中旋涡的周期性脱落，虽不同于高风速条件下可能导致灾变失稳的颤振，但仍可能引发行车舒适性降低、构件疲劳累积及服役耐久性风险.随着桥梁向更大跨径与更高柔性发展，系统揭示涡振机理并建立可靠的预测与控制方法已成为桥梁抗风工程的关键议题.本文从物理机理、理论模型、参数识别与工程控制等方面系统综述大跨度桥梁涡振研究进展.重点阐述卡门涡街形成与锁定机理、振幅自限的非线性动力学特征，以及非定常气动力与结构模态耦合的作用机制；总结经验与半经验涡激力模型、计算流体力学方法及基于试验与监测数据的参数识别技术；评述截面气动优化、调谐质量阻尼器等被动措施及主动/半主动控制探索的工程效果与适用性.结合超长跨桥梁的发展趋势，讨论不确定性量化、数字孪生与智能监测预警等未来研究方向.总体而言，尽管涡振机理认知不断深化、分析与控制技术日趋成熟，但仍需多学科协同以进一步提升预测精度与工程可靠性.
Abstract：Vortex-induced vibration(VIV)frequently occurs in long-span bridges under low to moderate wind speeds. Originating from periodic vortex shedding in the wake of bridge decks,VIV differs from flutter-induced collapse at high wind speeds, yet it may lead to reduced ride comfort, cumulative fatigue damage, and serviceability concerns. With the ongoing trend toward longer spans and increased structural flexibility, a mechanistic understanding of VIV and the development of reliable prediction and control strategies have become critical in bridge wind engineering. This paper provides a comprehensive review of VIV in long-span bridges, covering physical mechanisms, theoretical modeling, parameter identification, and engineering control measures. Key topics include Kármán vortex formation and lock-in phenomena, nonlinear amplitude self-limiting dynamics, and the coupling between unsteady aerodynamic forces and structural modes. Analytical and numerical approaches, including empirical and semi-empirical force models, computational fluid dynamics, and data-driven identification techniques based on experimental and field measurements, are summarized. Passive mitigation measures such as aerodynamic optimization and tuned mass dampers, as well as emerging active and semi-active controls, are critically assessed. Finally, future research directions are discussed with an emphasis on uncertainty quantification, digital twins, and intelligent monitoring for next-generation ultralong-span bridges.
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基本信息:
中图分类号:U441.3
引用信息:
[1]周奇,周健,包瑞.大跨度桥梁涡激共振机理认知与理论模型：研究进展与挑战[J].汕头大学学报(自然科学版),2026,41(01):3-31+2.
基金信息:
国家自然科学基金资助项目(52278508); 广东省科技创新战略专项基金资助项目(STKJ202209084)
2026-02-15
2026-02-15