【摘要】 针对三维流动效应下非轴对称自然层流（NLF）短舱设计困难的问题，发展了一种基于机器学习技术的压力分布反设计方法。短舱参数化建模使用自由曲面变形技术（FFD），通过求解RANS方程和基于SST(Shear Stress Trans‐port）湍流模型的■转捩模型实现自然转捩预测，利用生成拓扑映射（GTM）模型建立短舱外形及其压力分布数据集与低维隐空间变量的映射关系，全局优化算法在隐空间高效寻优，获得与目标压力分布匹配的短舱气动外形，实现自然层流短舱的反设计。GTM模型建立了数据集在高低维度间的高精度映射关系，优化设计过程中无需反复调用CFD求解器，设计效率大幅提升。利用该方法对通气短舱进行三维优化设计，设计所得非轴对称短舱外表面的自然层流区最大长度达当地弦长的40.5%，比优化前延长了12.2%，验证了该方法在考虑三维流动效应下NLF短舱的优化设计能力。更多还原

【Abstract】 An inverse design method of pressure distribution based on machine learning technology is developed aiming at the design difficulty of non-axisymmetric Natural Laminar Flow (NLF) nacelles under the three-dimensional flow effect.The Free Form Deformation (FFD) technology is used for the parametric modeling of the nacelle.Natural transition prediction is realized by solving the RANS equation and transition model ■based on the SST (Shear Stress Transport) turbulence model.The mapping relationship between the high dimensional data set and the low dimensional hidden space variables is established with the Generative Topological Mapping (GTM) model.The global optimization algorithm efficiently optimizes in the hidden space,obtaining the aerodynamic shape of the nacelle matching the target pressure distribution,thereby realizing the inverse design of the natural laminar flow nacelle.The GTM model establishes a highprecision mapping relationship between the high and low dimensions of the data set,hence requiring no repeated calling of the CFD solver in the optimization design process,significantly improving the design efficiency.The three-dimensional optimization of the ventilation nacelle is then conducted with this method.The maximum length of the natural laminar flow area on the outer surface is 40.5%of the local chord length with the extension of 12.2%,verifying the optimization design ability of the NLF nacelle considering the three-dimensional flow effect.更多还原