Active control of flow past an elliptic cylinder using an artificial neural network trained by deep reinforcement learning

The active control of flow past an elliptical cylinder using the deep reinforcement learning (DRL) method is conducted. The axis ratio of the elliptical cylinder Γ varies from 1.2 to 2.0, and four angles of attack α = 0°, 15°, 30°, and 45° are taken into consideration for a fixed Reynolds number Re = 100. The mass flow rates of two synthetic jets imposed on different positions of the cylinder θ1 and θ2 are trained to control the flow. The optimal jet placement that achieves the highest drag reduction is determined for each case. For a low axis ratio ellipse, i.e., Γ = 1.2, the controlled results at α = 0° are similar to those for a circular cylinder with control jets applied at θ1 = 90° and θ2 = 270°. It is found that either applying the jets asymmetrically or increasing the angle of attack can achieve a higher drag reduction rate, which, however, is accompanied by increased fluctuation. The control jets elongate the vortex shedding, and reduce the pressure drop. Meanwhile, the flow topology is modified at a high angle of attack. For an ellipse with a relatively higher axis ratio, i.e., Γ ⩾ 1.6, the drag reduction is achieved for all the angles of attack studied. The larger the angle of attack is, the higher the drag reduction ratio is. The increased fluctuation in the drag coefficient under control is encountered, regardless of the position of the control jets. The control jets modify the flow topology by inducing an external vortex near the wall, causing the drag reduction. The results suggest that the DRL can learn an active control strategy for the present configuration.