Airfoil optimization using a machine learning-based optimization algorithm

Abstract

Abstract For the design of wind turbines, airfoil optimization is widely required as the operation efficiency of wind turbines is closely dependent on the airfoil aerodynamic performance, where the accuracy of the optimization method is of great importance. In this paper, a machine learning-based optimization algorithm is proposed to improve the airfoil performance. A low-speed airfoil of NACA0012 is selected as the original airfoil for the optimization. The class-shape-transformation (CST) method is used to construct the geometry for the airfoil, and the aerodynamic performance is calculated using the panel code XFOIL. The validation of the simulation is carried out by comparing the predicted aerodynamic performance with the experimental data. In order to improve aerodynamic performance of the airfoil, maximizing the lift-to-drag ratio is selected as the main objective of the optimization problem while maintaining the lift coefficient not smaller than the original values. The results show that the present machine learning-based algorithm has fairly good convergence, and can obtain much higher lift-to-drag ratio for the optimized airfoil compared with the original one. Compared with the traditional genetic algorithm method, the machine learning based method can achieve much better aerodynamic performance and much shorter simulation time for the same airfoil optimization problem. In the future, the machine learning-based method is very promising for fluid machinery design optimization.