Airfoil Aerodynamic Optimization Design Using Ensemble Learning Surrogate Model

The conventional manual design process and data acquisition for aerodynamic parameters can be time-consuming. Consequently, this study proposes a solution to address this issue. The main contributions of this study are as follows. (1) A surrogate model based on ensemble learning (Ensemble) is proposed, which utilizes a two-layer learner combining the Cokriging model (Cokriging) with the neural network model based on transfer learning (TL). A numerical example is conducted to evaluate its performance, comparing it with single-surrogate methods. (2) A criterion—the mp-cvvor hybrid sampling strategy—is proposed for application to any surrogate model. The efficiency of the mp-cvvor method compared to that of other sampling strategies is validated by a synthetic benchmark. (3) The aerodynamic optimization framework is applied to the RAE2822 airfoil. Our method reduces average computation fluid dynamics (CFD) calls by more than 48.2% than do the nondominated sorting genetic algorithm-II (NSGA-II) and particle swarm optimization (PSO), and the lift-drag ratio increases by 3.087% compared to the increase from the single-surrogate-based Cokriging approach.