基于CFD的叶轮机优化设计将成为研究热点并为工程设计提供先进的工具。目前基于CFD的优化设计仍需解决两个问题：如何准确求解全局最优解；如何缩短设计周期。目前的非梯度方法能够实现全局最优，基于伴随方程的梯度方法能够实现高效率优化。对每个优化目标，采用伴随方法只需求解流场和伴随流场各一次即可确定所有梯度信息。本项目采用伴随方法求解梯度并用于构造响应面模型，能显著减少构造响应面所需的样本数目，再采用遗传算法进行叶轮机气动优化设计，以期在保证优化解全局最优的同时提高优化效率。首先完善基于梯度的响应面模型构造方法，并采取相关措施提高响应面模型的精度。之后采用遗传算法和伴随响应面模型进行叶轮机反设计，以验证本项目所发展的优化方法的有效性和可靠性；同时开展多维设计参数的气动优化设计研究，验证所发展的优化方法的全局最优。最后研究叶轮机复杂气动优化设计问题，包括多目标、多排叶片的气动优化设计。

Research on aerodynamic design optimization of turbomachinery blade rows based on CFD is becoming a hot area and will provide advanced tools for engineering design. However, at present it still requires solution of two problems (1) how to obtain accurate global optimal and (2) how to increase efficiency for the CFD-based design optimization. The widely used non-gradient-based optimization methods may be used to obtain the global optimal and the gradient-based optimization methods using the adjoint equations offer high efficiency. By using the adjoint method, the complete gradient information can be obtained by solving the governing flow equations and the corresponding adjoint equations only once for each cost function, regardless of the number of design parameters. In the present research, a response surface (RS) model will be constructed by using the gradient information and consequently the number of the required samples can be significantly reduced. Genetic algorithm is selected to obtain the global optimal. Firstly the implementation of the RS model constructed by using gradient information is introduced and the number of the samples required for constructing the gradient-based RS model can be improved through (1) an excellent parameterization method and (2) optimized design of experiment. Then inverse design cases will be studied by using the gradient-based RS model and genetic algorithm to validate the effectiveness and feasibility of the optimization method; and meanwhile the optimization deisgns with multiple deisgn parameters will be studied and compared with that by using only the adjoint method to verify the ability of the introduced method on capturing the global optimal. Finally some complex aerodynamic design optimizations of turbomachinery including multiple objectives design optimizaiton and multi-stages design optimization will be stuided by using the proposed optimization method.