考虑几何设计参数不确定性影响的涡轮叶栅稳健性气动设计优化

外形偏差是典型的叶片气动不确定性影响因素，考虑几何设计参数不确定性影响的叶片稳健性气动设计优化（RADO）有助于提高叶片平均气动性能及气动稳健性。首先，介绍RADO的基本原理及实现方法，采用基于灵敏度分析的叶片气动不确定性量化方法计算叶片目标气动函数的统计值，并实现目标函数对设计参数的梯度计算。然后，开展考虑设计参数不确定性影响的HS1A跨声速涡轮叶栅RADO研究，降低总压损失系数的统计均值及方差；通过与确定性气动设计优化（DADO）对比，揭示RADO在改善优化叶片气动稳健性方面的有效性和优越性。最后，对叶片进行流场统计分析，进一步揭示气动外形优化设计对降低总压损失系数敏感度的影响机理。

Robust aerodynamic design optimization of turbine cascades considering uncertainty of geometric design parameters

Geometric deviation is a principal source of aerodynamic uncertainty for turbomachinery blades. The aero-dynamic shape optimization considering uncertainty effects of geometric design parameters, also named Robust Aerodynamic Design Optimization (RADO), is suggested to improve both the mean aerodynamic performance and aerodynamic robustness. The basic principles and implementations of RADO are firstly introduced, followed by the evaluation of statistic mean and variance of aerodynamic performance changes by using the sensitivity-based uncertainty quantification method, from which the gradients of RADO cost function to the design parameters can be calculated. Then the study of RADO on a transonic turbine cascade, HS1A, considering the uncertainty of geometric design parameters is performed to reduce the mean total pressure loss and the corresponding variance. The optimization results compared with those of Deterministic Aerodynamic Design Optimization (DADO) demonstrate the effectiveness and superiority of RADO in improving the aerodynamic robustness. Finally, the statistical flow solutions of the original, DADO and RADO cascades are compared and presented to illustrate the mechanisms of reducing the sensitivity of total pressure loss through aerodynamic shape optimization by RADO.