This work studies the application of a reinforcement learning (RL)-based flow control strategy to the flow past a cylinder confined between two walls to suppress vortex shedding. The control action is blowing and suction of two synthetic jets on the cylinder. The theme of this study is to investigate how to use and embed physical information of the flow in the RL-based control. First, global linear stability and sensitivity analyses based on the time-mean flow and the steady flow (which is a solution to the Navier–Stokes equations) are conducted in a range of blockage ratios and Reynolds numbers. It is found that the most sensitive region in the wake extends itself when either parameter increases in the parameter range we investigated here. Then, we use these physical results to help design RL-based control policies. We find that the controlled wake converges to the unstable steady base flow, where the vortex shedding can be successfully suppressed. A persistent oscillating control seems necessary to maintain this unstable state. The RL algorithm is able to outperform a gradient-based optimisation method (optimised in a certain period of time) in the long run. Furthermore, when the flow stability information is embedded in the reward function to penalise the instability, the controlled flow may become more stable. Finally, according to the sensitivity analyses, the control is most efficient when the probes are placed in the most sensitive region. The control can be successful even when few probes are properly placed in this manner.

This work reports the application of a model-free deep reinforcement learning (DRL) based flow control strategy to suppress perturbations evolving in the one-dimensional linearised Kuramoto–Sivashinsky (KS) equation and two-dimensional boundary layer flows. The former is commonly used to model the disturbance developing in flat-plate boundary layer flows. These flow systems are convectively unstable, being able to amplify the upstream disturbance, and are thus difficult to control. The control action is implemented through a volumetric force at a fixed position, and the control performance is evaluated by the reduction of perturbation amplitude downstream. We first demonstrate the effectiveness of the DRL-based control in the KS system subjected to a random upstream noise. The amplitude of perturbation monitored downstream is reduced significantly, and the learnt policy is shown to be robust to both measurement and external noise. One of our focuses is to place sensors optimally in the DRL control using the gradient-free particle swarm optimisation algorithm. After the optimisation process for different numbers of sensors, a specific eight-sensor placement is found to yield the best control performance. The optimised sensor placement in the KS equation is applied directly to control two-dimensional Blasius boundary layer flows, and can efficiently reduce the downstream perturbation energy. Via flow analyses, the control mechanism found by DRL is the opposition control. Besides, it is found that when the flow instability information is embedded in the reward function of DRL to penalise the instability, the control performance can be further improved in this convectively unstable flow.

The violent hydrodynamic impact in water entry is a well-known phenomenon. Various methods have been applied to mitigate this “destructive force.” We experimentally investigate a method for reducing the impact force of water entry, which uses a gas jet to impinge on the liquid surface prior to the object entry. The blowing of the gas jet flow forms a cavity in the water and the penetration of the object into the water is thus less impactful. This method also significantly alters the flow field structure in the impact region, resulting in a reduction of the added fluid mass, the counterthrust of the gas jet that can further attenuate the impact intensity. We reported four cavity regimes and six impact modes formed in the gas-liquid interaction for a range of the modified Froude numbers. In particular, the regime of jet cavity only has a jet-assisted water entry with almost no impact event, and the maximum reduction in impact acceleration can reach nearly 90% with the reduction in entry velocity being about 10% relative to the impact without gas jet. The impact force reduction mechanism is considered to be a combination of the reduction of the added fluid mass and the presence of gas jet momentum.