Spanwise flow control of bridge deck using Bayesian optimization technique

This study introduces a novel framework in bridge wind engineering, merging Bayesian optimization (BO) with computational fluid dynamics (CFD) to optimize spanwise control parameters for the Great Belt bridge deck. This study leverages the BO framework for an automated, data-driven adjustment of the blow-suction sinusoidal spanwise perturbation (SSP) parameters at the leading and trailing edges of bridge decks. The primary aim is to finely tune the SSP control, stimulating the secondary instability in the spanwise vortices in the wake flow field. This process effectively generates streamwise vortices to suppress the spanwise ones, significantly mitigating fluctuating aerodynamic forces and vortex-induced vibration of the bridge deck, improving its aerodynamic stability. The results demonstrate that the BO framework-driven SSP control method can efficiently reduce the aerodynamic forces while finding the optimal SSP wavelength. Furthermore, through the optimization of multi-parameter variables in SSP control, the optimal combination of amplitudes and wavelengths for the SSP are achieved. Additionally, it was found that blow-suction at the trailing edge of the bridge deck is more effective than at the leading edge.