Machine learning-based optimization of a pitching airfoil performance in dynamic stall conditions using a suction controller

Abstract

Flow separation control on oscillating airfoils is crucial for enhancing the efficiency of turbine blades. In this study, a genetic algorithm was employed to optimize the configuration of a pure suction jet actuator on an oscillating airfoil at a Reynolds number of 1.35×105. Neural networks based on multilayer perceptrons were used to train the aerodynamic coefficients as functions of the control parameters and reduce the number of simulations. The objective function was the mean performance coefficient, defined as the ratio of the average lift to the average drag during an oscillation period. The control parameters were location, velocity, opening length, and suction jet angle relative to the airfoil surface. The optimal jet had the maximum velocity and opening length and was normal to the airfoil surface. The optimal jet location was near the leading edge vortex (LEV) (between 3% and 6% of the chord). The optimum jet can increase the average performance coefficient (average ratio of lift to drag during a period) by about 24 times. The major part of this improvement is related to reducing drag force. The average lift coefficient increases from about 0.58 to about 0.92 using this jet, while the average drag coefficient decreases from about 0.23 to about 0.02. The optimal jet suppressed the dynamic stall vortex, which resulted from the combination of two clockwise vortices: LEV and turbulent separation vortex. Suppressing this vortex prevented the counterclockwise trailing edge vortex from growing at the end of the airfoil.