Compressor geometric uncertainty quantification under conditions from near choke to near stall  

作　　者：Junying WANG Baotong WANG Heli YANG Zhenzhong SUN Kai ZHOU Xinqian ZHENG 

机构地区：[1]State Key Laboratory of Automotive Safety and Energy,School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China [2]Department of Aerodynamics and Thermodynamics,Institute for Aero Engine,Tsinghua University,Beijing 100084,China

出　　处：《Chinese Journal of Aeronautics》2023年第3期16-29,共14页中国航空学报（英文版）

基　　金：supported by the National Science and Technology Major Project,China(No.2017-II-0004-0016)。

摘　　要：Geometric and working condition uncertainties are inevitable in a compressor,deviating the compressor performance from the design value.It’s necessary to explore the influence of geometric uncertainty on performance deviation under different working conditions.In this paper,the geometric uncertainty influences at near stall,peak efficiency,and near choke conditions under design speed and low speed are investigated.Firstly,manufacturing geometric uncertainties are analyzed.Next,correlation models between geometry and performance under different working conditions are constructed based on a neural network.Then the Shapley additive explanations(SHAP)method is introduced to explain the output of the neural network.Results show that under real manufacturing uncertainty,the efficiency deviation range is small under the near stall and peak efficiency conditions.However,under the near choke conditions,efficiency is highly sensitive to flow capacity changes caused by geometric uncertainty,leading to a significant increase in the efficiency deviation amplitude,up to a magnitude of-3.6%.Moreover,the tip leading-edge radius and tip thickness are two main factors affecting efficiency deviation.Therefore,to reduce efficiency uncertainty,a compressor should be avoided working near the choke condition,and the tolerances of the tip leading-edge radius and tip thickness should be strictly controlled.

关 键 词：COMPRESSOR Geometric uncertainty quantification Interpretable machine learning Multiple conditions Neural network 

分 类 号：V233[航空宇航科学与技术—航空宇航推进理论与工程]