Genetic-algorithm-based control of a turbulent boundary layer

This work aims to investigate experimentally the control of a turbulent boundary layer over a flat plate based on a genetic-algorithm (GA)-based control system. A novel actuator is developed in house, consisting of one array of 11 spanwise-aligned synthetic minijets through longitudinal miniature slits, each independently controlled in terms of its exit momentum coefficient C _μ , frequency f _e and phase ϕ , while nine wall wires are placed downstream of the actuator to measure a variation in wall friction. A GA is employed for the unsupervised learning of near-optimal control strategies, i.e. the optimal ϕ of the synthetic jets, with f _e and C _μ optimized first. The learning process unveils three typical control strategies that may attain substantial drag reduction (DR) in both actuation and downstream regions, i.e. conventional uniform forcing (CUF), GA-I and GA-II. The latter two are characterized by the triangle- and trapezoid-like distributions of ϕ , respectively, thus each producing a spanwise motion. The three strategies achieve spanwise-averaged local DR by 45 %, 52 % and 52 %, respectively. The downstream drag-reducing areas exhibit an appreciable difference between the three cases, with CUF recovering drag more rapidly than the other two. It has been found that the three cases are associated with different DR mechanisms and flow physics, which account for the distinct control performances.