Shape optimization of airfoil in ground effect based on free-form deformation utilizing sensitivity analysis and surrogate model of artificial neural network

Abstract

Wings operate in proximity to surfaces for using ground effect to enhance lift-to-drag ratio, but the stability meets challenges. Changing airfoil shape could satisfy the requirement of stability and maximize lift-to-drag ratio, like performing single-objective optimization under a constraint. This research uses free-form deformation technique to adjust the airfoil curve by control points, and reduces the dimension of control variables by sensitivity analysis. Sampling airfoils and corresponding aerodynamics feeds an artificial neural network. Then, the neural network plays as a surrogate to predict fitness value for genetic algorithm execution. It is found that lift-to-drag ratio and static stability height are sensitive to vertical adjustments near the leading edge, one quarter chord point and trailing edge. The trained two-hidden-layer MLP generalizes well. The deformed optimum airfoil with S-shape camber line reduces lift-to-drag ratio to gain adequate static stability. The cause is that the aerodynamic center of pitch and that of altitude are moved upstream together, while the former’s interval is longer than the latter’s. The procedure provides reference for the optimization of airfoil under more states in ground effect zone, and the S-type deformation offers guidance for refinement on wing-in-ground stability.