Airfoil optimization design based on Gaussian process regression and genetic algorithm

In view of the problems that the leading edge curvature of the wind turbine airfoil with high lift⁃to⁃drag ratio has large radius, the traditional airfoil parameterization method has insufficient leading edge control ability, and there exists poor prediction accuracy based on the panel method XFOIL, an enhanced class function/shape function transformation (CST) parameterized method was used to control the shape change of airfoil, Latin hypercube experimental design, computational fluid dynamics (CFD) flow field calculation module, Gaussian process regression model and genetic algorithm, based on high⁃confidence Reynolds average Navier ⁃Stocks (RANS) and Gaussian regression model⁃assisted genetic algorithm for airfoil optimization design method.The results showed that the airfoil optimization method based on the Gaussian regression model can reduce the number of CFD calculations for optimization by one order, thereby greatly improving the optimization design efficiency.The resistance reduction design of the supercritical airfoil RAE2822 of the standard calculation example showed that the resistance of the CFD frequency in the order of hundreds of times was reduced by 43.16%, the shock wave was weakened and the lift, moment and area could strictly meet the constraints.The maximum lift⁃to⁃drag ratio of the wind turbine airfoil NACA64618 showed that the designed airfoil not only greatly increased the lift⁃to⁃drag ratio at the design angle of attack and the secondary design angle of attack, but also improved its aerodynamic performance in the entire range of the small angle of attack.And there were two main design points, without bad resistance. © 2021, Editorial Department of Journal of Aerospace Power. All right reserved.