Data-Driven blowing suction control in a turbulent channel flow

We use an adaptive data-driven technique to modulate coherent motions within a turbulent channel flow. A Direct Numerical Simulation (DNS) is used with actuators placed along the channel wall, which interact with the flow field via blowing and suction based control. The actuators' actions are determined by an adaptive deep learning technique, which allows for model-free control despite the high dimensional and nonlinear nature of the turbulent flow field. Upon successfully attaining the specified high-level goal, the data-driven controller's decision-making is analyzed to understand its impact on the flow field.