Active Control of Flow over Rotating Cylinder by Multiple Jets using Deep Reinforcement Learning

Active flow control is a long-standing topic in fluid mechanics. By using different kinds of actuators, it alters the flow behavior to improve the aerodynamics/hydrodynamic performance of a flow system. One such usage is for drag reduction, which is of importance because annually a large amount of energy is being consumed to overcome the drag forces encountered in many engineering applications such as ground, air, and sea vehicles [1], For example, in ground vehicles, the aerodynamics drag has a part of approximately 27% in the total fuel consumption [2]. Thus, achieving effective and stable flow control for drag force reduction is important. Unfortunately, due to the combination of non-linearity, time-dependence, and high-dimensionality intrinsic to the Navier-Stokes equations and the computational power needed for calculations, finding efficient strategies for performing active flow control is highly sophisticated [3, 4]. Active flow control has been discussed in simulations using reduced-order models and harmonic forcing [5], direct simulations coupled with the adjoint method [6], or linearized models [7], and mode tracking methods [8]. Realistic actuation mechanisms such as acoustic excitation [9], synthetic jets [9], plasma actuators [10, 11], suction mechanisms [12], transverse motion [13], periodic oscillations [14], oscillating foils [15], air jets [16], and the Lorentz forces in conductive media [16], are also discussed in detail, as well as limitations imposed by real-world systems[17]. Similar experimental work has been performed, to control either the cavitation instability [18], the vortex flow behind a conical fore-body [19], or the flow separation over a circular cylinder [20].