Numerical investigation and modeling of sweep effects on inlet flow field of axial compressor cascades  

作　　者：Jiancheng ZHANG Donghai JIN Xingmin GUI 

机构地区：[1]School of Energy and Power Engineering,Beihang University,Beijing 100191,China [2]Jiangxi Research Institue of Beihang University,Nanchang 330096,China

出　　处：《Chinese Journal of Aeronautics》2024年第2期296-308,共13页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.52376021)。

摘　　要：Swept blades are widely utilized in transonic compressors/fans and provide high load,high through-flow,high efficiency,and adequate stall margin.However,there is limited quantitative research on the mechanism of the effect of swept blades on the flow field,resulting in a lack of direct quantitative guidance for the design and analysis of swept blades in fans/compressors.To better understand this mechanism,this study employs a reduced-dimensional force equilibrium method to analyze more than 1500 swept cascades data.Results verify that circumferential fluctuation terms are responsible for inducing radial migration in the inlet airflow field of the swept blade,resulting in variations in the incidence angle and consequently leading to changes in the characteristics of the swept blade.Thus,a combination of simple functions and machine learning is utilized to model the circumferential fluctuation terms and quantify the sweep mechanism.The prediction accuracy of the model is high,with coefficient of determination greater than 0.95 on the test set.When the model is applied in a meridional flow analysis program,the calculation accuracy of the program for the incidence angle is improved by 0.4°and 0.6°at the design and off-design conditions respectively,compensating for the program’s original deficiencies.Meanwhile,the model can also provide quantitative guidance for the design of swept blades,thereby reducing the number of design iterations and improving design efficiency.

关 键 词：TURBOMACHINERY Sweep aerodynamics Quasi-3D method Incidence angle Circumferential fluctuation Machine learning 

分 类 号：V231.3[航空宇航科学与技术—航空宇航推进理论与工程]