摘要
提出一种基于正反问题耦合的压气机特性快速预测方法,根据压气机一维设计理论,通过对满足流量及压比设计指标的反问题求解快速得出压气机的气动布局方案,在此基础上结合非设计点损失和落后角模型通过正问题求解得出全工况压气机特性,再根据预测特性与目标的偏差调整气动布局方案,以此通过对正反问题的耦合求解实现对压气机特性的合理快速预测。为了提高压气机特性预测精度,发展了基于遗传算法的压气机损失和落后角模型参数优化校准方法,该方法利用优化理论及实验数据对传统的压气机损失和落后角模型进行改进。利用三台压气机的实验结果对模型进行了校准与验证,验证结果表明:设计点效率预测误差为0.23%,非设计点为1.34%,满足发动机需求分析及概念设计阶段对压气机效率的预测精度要求。
A rapid method of compressor characteristics prediction with coupling inverse de⁃sign and direct problems was proposed.According to one⁃dimensional meanline design theory of the compressor,the aerodynamics layout of the compressor was quickly obtained by solving the inverse problem to satisfy the design targets such as mass flow rate and pressure ratio.Then,by adopting the models of loss and deviation angle at off⁃design conditions,the compressor characteris⁃tics of the whole working condition were obtained by solving the direct problem.The aerodynamics layout was adjusted according to the deviation angle between the predicted characteristics and the targets,and the reasonable and rapid prediction of the compressor characteristics was realized through the coupling of inverse and direct problems.Moreover,a parameter calibration method of compressor loss and deviation angle model based on genetic algorithm was created.This meth⁃od used optimization theory and experiment data of compressors to improve the traditional models of loss and deviation angle for enhancing the prediction accuracy of compressor characteristics.Ex⁃perimental data of three compressors were used for calibration and validation of the proposal meth⁃od.The results showed that the efficiency prediction errors at design and off⁃design conditions were 0.23%and 1.34%,which satisfied the requirement in the stage of requirement analysis and concept design for aero⁃engine.
作者
宋召运
王宝潼
范腾博
郑新前
冯旭栋
SONG Zhaoyun;WANG Baotong;FAN Tengbo;ZHENG Xinqian;FENG Xudong(Institute for Aero Engine,Tsinghua University,Beijing 100084,China;State Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,China;Sichuan Gas Turbine Establishment,Aero Engine Corporation of China,Mianyang Sichuan 621000,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2022年第3期619-628,共10页
Journal of Aerospace Power
基金
国家科技重大专项(2017⁃Ⅱ⁃0004⁃0016)。
关键词
压气机特性预测
正反问题耦合
优化校准方法
遗传算法
损失和落后角模型
compressor characteristics prediction
coupling direct and inverse problems
calibration method based on parameter optimization
genetic algorithm
loss and deviation angle model
