Machine-learning flow control with few sensor feedback and measurement noise

A comparative assessment of machine-learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Karman vortex street past a circular cylinder at a low Reynolds number (Re = 100). The flow is manipulated with two blowing/suction actuators on the upper and lower side of a cylinder. The feedback employs several velocity sensors. Two probe configurations are evaluated: 5 and 11 velocity probes located at different points around the cylinder and in the wake. The control laws are optimized with Deep Reinforcement Learning (DRL) and Linear Genetic Programming Control (LGPC). By interacting with the unsteady wake, both methods successfully stabilize the vortex alley and effectively reduce drag while using small mass flow rates for the actuation. DRL has shown higher robustness with respect to different initial conditions and to noise contamination of the sensor data; on the other hand, LGPC is able to identify compact and interpretable control laws, which only use a subset of sensors, thus allowing for the reduction of the system complexity with reasonably good results. Our study points at directions of future machine-learning control combining desirable features of different approaches.& nbsp;Published under an exclusive license by AIP Publishing.

Automated summary

This paper presents a comparative assessment of machine-learning methods for active flow control. The study focuses on drag reduction of a two-dimensional Karman vortex street past a circular cylinder at a low Reynolds number. The results show that both Deep Reinforcement Learning (DRL) and Linear Genetic Programming Control (LGPC) successfully reduce the drag and stabilize the vortex alley. DRL demonstrates higher robustness, while LGPC identifies compact and interpretable control laws using only a subset of sensors.