为了在具有不同负荷的压气机叶栅的初始设计过程中选取最大厚度位置，采用数值方法对在不同折转角的高亚音来流条件下对扩压叶栅进行了大量的系统性研究，分析了最大厚度位置、折转角以及稠度3个叶栅几何参数对叶栅变冲角特性以及对最小损失冲角下的叶栅气动性能的影响规律。基于大量叶栅样本建立数学模型，用来定量描述最小损失冲角，以及最小损失冲角下的总压损失系数和扩压因子等气动性能参数与叶栅几何参数之间的依变规律；基于该数学模型，绘制了性能参数依变关系图谱，并分析改变最大厚度位置带来的损失收益；给出不同设计条件下最大厚度位置的最优选择图谱，为高负荷叶栅设计提供可靠的叶型参考数据。结果显示最大厚度位置的选择对高负荷扩压叶栅叶型设计来说影响显著，最优的最大厚度位置位于20%~35%相对弦长位置。随着折转角和叶栅扩压程度升高，最优最大厚度位置提前，且带来的损失收益显著提高。

In order to select the maximum thickness position in the initial design process of compressor cascade at different loads, a large number of systematic studies for compressor cascades with different turning angles under high subsonic inlet flow conditions were carried out by numerical method,the influence rules of three cascade geometric parameters of the maximum thickness position, turning angle and consistency on the characteristics of cascade varying angle of attack and the cascade aerodynamic performance at minimum loss angle of attack were analyzed.Based on lots of cascade samples, the mathematical model was established to quantitatively describe the dependence rule between the aerodynamic performance parameters, such as the minimum loss angle of attack, the total pressure loss coefficient at the minimum loss angle of attack and the diffusion factor, and the geometric parameters of the cascade.Based on the mathematical model,the dependence relationship graphs of performance parameters were drawn, and the loss and gain caused by changing the maximum thickness position were analyzed.The optimal selection maps of the maximum thickness position under different design conditions were given, so as to provide reliable profile reference data for highload cascade design. The results show that the selection of maximum thickness position has significant impact on the blade profile design of highload compressor cascade, the optimal maximum thickness position is located in 20% to 35% relative chord length. With the increase of turning angle and cascade diffusion degree, the optimal position of maximum thickness moves forward, and the induced loss gain is increased significantly.

TK471

国家自然科学基金(51906049,52006011)；国家科技重大专项(2017-II-0007-0021)；中国博士后科学基金(2020M670158,2020TQ0037,2020M671672)