In the present study, we perform an active proportional (P) feedback control of laminar and turbulent flows over a circular cylinder with an averaged velocity sensor to reduce its drag and lift fluctuations. As a sensor for the feedback control, we adopt an averaged transverse velocity on the centerline of the wake. For the averaging of the transverse sensing velocity, we consider both temporal averaging over a finite period and spatial averaging in the spanwise direction. As a control input, the blowing/suction actuation is provided on the cylinder surface near the flow separation, and its magnitude is linearly proportional to the averaged transverse sensing velocity. With the control, the fluctuations of the transverse sensing velocity are significantly reduced and the vortices right after the cylinder and the K\'{a}rm\'{a}n vortex shedding in the wake are weakened, resulting in substantial reductions of the mean drag and lift fluctuations. Furthermore, it is shown that the adoption of the averaged sensing velocity makes the P control successful for a wider range of sensing locations in laminar flow at $Re=100$, and is essential for the success of the P control in turbulent flow at $Re=3900$. With the optimal control parameters, the reductions of the mean drag and lift fluctuations for turbulent flow at $Re=3900$ are about 11\% and 61\%, respectively. The present P control maintains the magnitude of the blowing/suction actuation less than $1\%$ of the free-stream velocity, and thus the control input power is very small, leading to an excellent control efficiency.

In this study, we investigate tabs applied to turbulent flow over a circular cylinder for the reductions of the mean drag and lift fluctuations. Tabs are small and thin passive devices attached to the upper and lower surfaces of a circular cylinder near the flow separation. The Reynolds number considered is Re= 3900, based on the free-stream velocity and cylinder diameter. Large eddy simulations are performed using a dynamic global subgrid-scale eddy-viscosity model. A parametric study is carried out to find the optimal tab configuration for minimizing the mean drag and lift fluctuations. Parameters considered are the height (ly) and width (lz) of the tabs, and spanwise spacing (λz) between them. With the optimal parameters, the spanwise coherence of the vortex shedding behind the cylinder is effectively disrupted, resulting in three-dimensional vortical structures varying in the spanwise direction. As a result, the strength of the vortex shedding in the wake is successfully weakened, and the mean drag and lift fluctuations are significantly reduced by 14% and 95%, respectively, with the optimal tab configuration of ly/d=0.2, lz/d=0.3, and λz/d=4, where d is the cylinder diameter.

In the present study, to predict the transverse velocity field in the near-wake of laminar flow over a circular cylinder at the Reynolds numbers of 60 and 300, we construct neural networks with instantaneous wall pressures on the cylinder surface as the input variables. For the two-dimensional unsteady flow at Re=60, a fully connected neural network (FCNN) is considered. On the other hand, for a three-dimensional unsteady flow at Re=300 having spanwise variations, we employ two different convolutional neural networks based on an encoder–FCNN (CNN-F) or an encoder–decoder (CNN-D) structure. Numerical simulations are carried out for both Reynolds numbers to obtain instantaneous flow fields, from which the input and output datasets are generated for training these neural networks. At the Reynolds numbers considered, the neural networks constructed accurately predict the transverse velocity fields in the near-wake over the cylinder using the information of instantaneous wall pressures as the input variables. In addition, at Re=300, it is observed that CNN-D shows a better prediction ability than CNN-F.

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