Vortex-induced vibrations of a bridge in non-stationary and non-uniform wind fields

XiaoLong Deng , Hao Hong , Pengfei Lin , Gang Hu , Wenli Chen , Bernd R. Noack

Abstract

This study experimentally investigates the vortex-induced vibration (VIV) of a 1:50 scale box girder bridge model in a multi-fan wind tunnel. We generated complex wind fields to reflect realistic conditions, including non-stationary (linearly increasing/decreasing, sinusoidal, and random) and spanwise non-uniform (linear and parabolic) velocity profiles. The effect of turbulence was also examined using active control blades at the tunnel inlet. Results show that all non-stationary and non uniform conditions reduce VIV displacement compared to stationary and uniform flow. Notably, a parabolic wind profile reduced the VIV RMS displacement by 34.4%, proving more effective in suppressing the VIV amplitude than a linear profile (7.8% reduction) due to its greater disruption of spanwise vortex correlation. In the non-stationary flow, the VIV response is governed by the time available for energy accumulation. Furthermore, a significant asymmetry was observed, with gradually increasing wind velocities inducing substantially larger VIV amplitudes than decreasing velocities, suggesting a hysteresis effect. For the random wind fields, VIV was significant when the velocity range (Max-Min=1 m/s) was close to the stationary VIV range (Max-Min=0.8 m/s), but became negligible when the range was larger (Max-Min=2 m/s). Activating the blades intensified turbulence (e.g., from 4.0% to 14.7%), which consistently suppressed VIV by disrupting periodic vortex shedding. These findings underscore the importance of considering spatio-temporal wind variations in bridge aerodynamics, as traditional uniform flow tests may be overly conservative.

Key Words

long-span bridge; multi-fan wind tunnel; non-stationary wind field; non-uniform wind field; vortex-induced vibration

Address

XiaoLong Deng — Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, 518055, China
Hao Hong — Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, 518055, China
Pengfei Lin — Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, 518055, China
Gang Hu — 1)Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, 518055, China 2)Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Harbin Institute of Technology, Shenzhen, 518055, China
Wenli Chen — 1)Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, 518055, China 2)3Laboratory of Intelligent Civil Infrastructure (LiCi), Harbin Institute of Technology, Harbin, 150090, China
Bernd R. Noack — 1)Chair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Room 313, Building C, University Town, Xili, Shenzhen, 518055, China 2)Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Harbin Institute of Technology, Shenzhen, 518055, China

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