A wing is an important part of the aircraft to improve aerodynamic performance. The current study is focused on an adaptive surrogate algorithm for airfoil aerodynamic optimization, which is based on a multi-output Gaussian process model. The conventional design method seriously relies on wind tunnel experiments and expensive computational simulations. The metamodels can significantly improve design efficiency and hence reduce the overall design costs. An active learning algorithm is proposed to improve the effectiveness of the multi-output Gaussian process model. The NSGA-II algorithm is adopted to obtain the optimal Pareto set with the optimization objectives of lift and drag coefficients for adaptive airfoil shapes. Besides, the Bezier curve and radial basis function are utilized in this study for airfoil mesh deformation. The results show that the airfoil shape can be obtained effectively by integrating the metamodel, active learning algorithm, and multi-objective optimization algorithm. The optimized results are of great engineering applications.

机翼是飞机提高空气动力学性能的重要组成部分。当前的研究集中在基于多输出高斯过程模型的翼型空气动力学优化的自适应替代算法上。传统的设计方法严重依赖于风洞实验和昂贵的计算模拟。元模型可以显着提高设计效率，从而降低总体设计成本。为了提高多输出高斯过程模型的有效性，提出了一种主动学习算法。采用NSGA-II算法获得了最优Pareto集合，该集合具有针对自适应翼型形状的升力和阻力系数的优化目标。此外，本研究利用贝塞尔曲线和径向基函数对机翼网格变形进行了研究。结果表明，通过集成元模型，主动学习算法和多目标优化算法，可以有效地获得机翼形状。优化的结果具有很好的工程应用价值。