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 Kármán 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.Work produced with the support of a 2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation, grant n. IN[20]_ING_ING_0163. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. Funding of the National Natural Science Foundation China (NSFC) under grants 12172109 and 12172111 and a Natural Science & Engineering grant of the Guangdong province, China, is gratefully acknowledged