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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.

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.