Active Control of Flow Over Spinning Cylinder by Flow Injection Using Deep Reinforcement Learning

In this work, the deep reinforcement learning algorithm will be combined with cylinder rotation and multiple controlled jets to achieve maximum drag suppression. This work will cover the DRL code's parameters, limitations, and optimization for the case with cylinder rotation. The focus will be on optimizing the number and positions of the jets, the sensor’s location and number, and the maximum allowed flow rate of jets in the form of the maximum allowed flow rate of each actuation and the total number of them per episode. Combining rotation with DRL is effective as it can suppress vortex shedding, stabilize the Karman vortex street, and reduce the drag coefficient by up to ۴۹.۷۵%. Also, it will be shown that having more sensors at more locations is not always ideal as it can have negative effects. It is found that in most cases, giving the agent access to higher flow rates has a negative impact on performance, except when the cylinder is rotating. However, regardless of the situation, the agent can maintain the lift coefficient at or near zero, or stabilize it at a smaller value.

کلیدواژه ها:

Reinforced Learning ، Fluid Mechanics ، Artificial Neural Network ، Flow Control ، Injection/suction Jet ، Rotating Cylinder ، Drag Suppression