Genetic Algorithm Based Optimization of Wing Rotation in Hover

The pitching kinematics of an experimental hovering flapping wing setup are optimized by means of a genetic algorithm. The pitching kinematics of the setup are parameterized with seven degrees of freedom to allow for complex non-linear and non-harmonic pitching motions. Two optimization objectives are considered. The first objective is maximum stroke average efficiency, and the second objective is maximum stroke average lift. The solutions for both optimization scenarios converge within less than 30 generations based on the evaluation of their fitness. The pitching kinematics of the best individual of the initial and final population closely resemble each other for both optimization scenarios, but the optimal kinematics differ substantially between the two scenarios. The most efficient pitching motion is smoother and closer to a sinusoidal pitching motion, whereas the highest lift-generating pitching motion has sharper edges and is closer to a trapezoidal motion. In both solutions, the rotation or pitching motion is advanced with respect to the sinusoidal stroke motion. Velocity field measurements at selected phases during the flapping motions highlight why the obtained solutions are optimal for the two different optimization objectives. The most efficient pitching motion is characterized by a nearly constant and relatively low effective angle of attack at the start of the half stroke, which supports the formation of a leading edge vortex close to the airfoil surface, which remains bound for most of the half stroke. The highest lift-generating pitching motion has a larger effective angle of attack, which leads to the generation of a stronger leading edge vortex and higher lift coefficient than in the efficiency optimized scenario.