Active flow control of the dynamic wake behind a square cylinder using combined jets at the front and rear stagnation points

Control of flows around bluff bodies mediated by porous materials

This paper applies deep reinforcement learning (DRL) on the synthetic jet control of flows over an NACA (National Advisory Committee for Aeronautics) 0012 airfoil under weak turbulent condition. Based on the proximal policy optimization method, the appropriate strategy for controlling the mass rate of a synthetic jet is successfully obtained at [Formula: see text]. The effectiveness of the DRL based active flow control (AFC) method is first demonstrated by studying the problem with constant inlet velocity, where a remarkable drag reduction of 27.0% and lift enhancement of 27.7% are achieved, accompanied by an elimination of vortex shedding. Then, the complexity of the problem is increased by changing the inlet velocity condition and reward function of the DRL algorithm. In particular, the inlet velocity conditions pulsating at two different frequencies and their combination are further applied, where the airfoil wake becomes more difficult to suppress dynamically and precisely; and the reward function additionally contains the goal of saving the energy consumed by the synergetic jets. After training, the DRL agent still has the ability to find a proper control strategy, where significant drag reduction and lift stabilization are achieved, and the agent with considerable energy saving is able to save the energy consumption of the synergetic jets for 83%. The performance of the DRL based AFC proves the strong ability of DRL to deal with fluid dynamics problems usually showing high nonlinearity and also serves to encourage further investigations on DRL based AFC.

Deep reinforcement learning based synthetic jet control on disturbed flow over airfoil

In the current paper, the active flow control for enhancing vortex induced vibration (VIV) of a circular cylinder, which can be potentially applied in ocean energy harvesting, is achieved by an artificial neural network (ANN) trained through deep reinforcement learning (DRL). The flow past a circular cylinder with and without jet control located on the cylinder is numerically investigated using OpenFOAM, and the ANN is applied to learn an active flow control strategy through experimenting with different mass flow rates of the jets. According to our results, the jets on the cylinder are able to dramatically destabilize the periodic shedding of the cylinder wake, which leads to a much larger VIV and work capability of the cylinder. Through controlling the flow rate of the jets based on the observation of the instantaneous flow field, the ANN successfully increases the drag by 30.78%, and the magnitude of the fluctuation of the drag and lift coefficient by 785.71% and 139.62%, respectively, while the energy consumption of the jets is almost negligible. Furthermore, the net energy output by VIV with jet control increases by 357.63% (case of water) compared with the uncontrolled situation. The results demonstrate that the performance of the active jet control strategy established by DRL for enhancing VIV is outstanding and promising for realizing the transformation from the ocean energy to electrical energy. Therefore, it is encouraged to perform further investigations on VIV enhancement using active flow control based on DRL.

Active control for enhancing vortex induced vibration of a circular cylinder based on deep reinforcement learning

Attenuation of vortex street by suction through the structured porous surface

Dynamic wake of a square cylinder controlled with steady jet positioned at the rear stagnation point

Passive suction jet control of flow regime around a rectangular column with a low side ratio

We experimentally investigate the characteristics of a dynamic wake and of flow separation for a square cylinder with steady suction at its leading-edge corners. The wind tunnel experiments were conducted at a Reynolds number of 5946, and suction slots were manufactured symmetrically at the leading corners of the square cylinder. Steady suction was characterized with a suction momentum coefficient Cμ varying from 0.0227 to 0.3182. A time-resolved particle image velocimetry system was used to evaluate the control of leading-edge suction at different Cμ. Next, the measurements were analyzed by applying a proper orthogonal decomposition (POD) to study the control effectiveness. The POD results suggest that the first four modes of wake vortex shedding are transformed in controlled cases and that periodic Karman vortex shedding is suppressed. The results also show that, even with a very small momentum coefficient, the steady suction at the leading-edge corners stabilizes the cylinder wake. The wake region becomes longer and narrower in comparison with the baseline case. In addition, modifications of separation flow were visualized. At quite small Cμ, flow separation at the leading-edge corners is considerably suppressed. Upon increasing the suction momentum coefficient to 0.1364, flow separation at the leading edges is almost eliminated. Finally, we estimate the effect of drag reduction due to the leading-edge suction. 

Hao Meng

Papers

Active flow control of the dynamic wake behind a square cylinder using combined jets at the front and rear stagnation points

Dynamic wake of a square cylinder controlled with steady jet positioned at the rear stagnation point

Passive suction jet control of flow regime around a rectangular column with a low side ratio

Characteristics of forced flow past a square cylinder with steady suction at leading-edge corners

Wake-induced vibration of a suspender cable in the rear of a bridge tower