随着现代飞行器性能需求的不断提高，飞行器精细化气动优化设计要求更高可信度的CFD数值分析及更多的独立设计变量，使得基于代理模型的全局优化算法在超过一定的设计变量后显著降低了效率，难以满足复杂工程的设计需求。而目前的高维代理模型过程复杂、时间花费高，缺乏对工程问题的广泛适应性。针对以上难题，提出了利用监督式非线性降维代理建模方法来缓解代理优化过程中的高维变量设计难题。该方法将核主成分分析（非线性）降维与高斯回归过程模型统一训练，自适应构建新型高维代理模型，并随着优化过程不断学习改进模型，建立了从高维输入到输出的准确映射，有效解决了传统高维代理模型训练时间花费高和适应性差等难题。然后基于该新型代理模型发展了适用于飞行器复杂气动设计的高维全局优化设计方法，并将其应用到美国航空航天学会（AIAA）优化小组发布的2个复杂跨声速优化算例中。通过与传统代理优化方法全面比较，验证了所提的方法能大幅提高飞行器高维变量全局优化效率和全局寻优能力。

With the ever-increasing demands for the performance of modern aircraft，the refined aerodynamic shape design optimization of aircraft requires higher-fidelity CFD numerical analysis and more independent design variables，thus significantly reducing the efficiency of surrogate-based global optimization algorithm，particularly with an excessive number of design variables，Therefore，meeting the advanced demands for complex engineering problems becomes challenging. Furthermore，with complex modeling process and prohibitive computational costs，popular high-dimensional surrogate models，lack good adaptability to a wide range of engineering problems，This paper proposes a Supervised Nonlinear Dimension-Reduction Surrogate Modeling（SN-DRSM）method to alleviate the problem of high-dimensional variables in the process of surrogate-based design optimization. This method， integrates and trains the Kernel Principal Component Analysis（KPCA）nonlinear dimension-reduction model and the Gaussian regression process model as a whole，A new high-dimensional surrogate model is adaptively constructed，continuously studied in depth and improved during the optimization process，to establish an accurate mapping from high-dimensional inputs to outputs，thereby effectively solving the problems of high training cost and poor adaptability of traditional high-dimensional surrogate models. Then，an efficient high-dimensional global design optimization platform for complex aerodynamic configuration of aircraft is developed based on this novel surrogate model，and applied to two standard transonic optimization cases defined by AIAA aerodynamic optimization group. A comprehensive comparison with the traditional surrogate optimization methods， proves that the new method can significantly improve the global optimization efficiency and ability of high-dimensional aircraft variables.