摘要
未来航空发动机的发展要求其压缩系统级负荷不断增大,由此将使得压气机内部出现较强的角区分离、附面层流动分离等二次流。提出了一种新型的自适应康达喷气流动控制(ACJC)方法,更加智能且高效地抑制压气机内部流动分离并提升压气机的扩压能力,进而拓宽高负荷压气机稳定、高效运行范围。为构建自适应康达喷气流动控制系统并在高负荷压气机上验证其控制效果,首先,选取了扩压因子为0.66的压气机静叶叶栅为研究对象,并优化设计了单缝康达喷气静叶叶栅;然后,基于数值计算结果采用方差分析法、主成分分析法和神经网络算法建立了单缝康达喷气静叶叶栅来流攻角预测模型和最佳喷气量预测模型;最后,搭建了基于自适应康达喷气流动控制系统的试验平台,验证了其对高负荷叶栅流动分离控制的有效性和准确性。试验结果表明:在不同攻角和不同来流马赫数条件下,自适应康达喷气流动控制系统能够实时准确地预测来流攻角,并瞬间做出喷气量实时调节与反馈。此外,在5°来流攻角下,当来流马赫数为0.4、0.5和0.6时,相比于无康达喷气叶栅,康达喷气的引入使得总压损失系数分别降低了11.5%、9.8%和8.0%。
Future development of aeroengines requires continuous increase in the compressor stage load,which will result in strong secondary flows such as corner separation and boundary layer flow separation in the compressor.In this paper,a new Adaptive Coanda Jet Control(ACJC)technology is proposed to intelligently and efficiently restrain the flow separation in the compressor and improve the diffusion capacity of the compressor,and the stable and effi⁃cient operation range of the highly loaded compressor is dramatically broadened.To construct the ACJC system and verify its control effect in a highly loaded compressor,we first employ a highly loaded compressor stator cascade con⁃structed based on the Zierke&Deutsch airfoil to investigate the ACJC system,with the diffusion factor of 0.66 at the design point.Then,the variance analysis method,principal component analysis method and neural network algorithm are adopted to establish the incidence angle prediction model and the optimal injection mass flow rate prediction model of the Coanda jet flap.Finally,an experimental platform based on the ACJC system is built to verify the effectiveness and accuracy of flow separation control for the highly loaded cascade.The experimental results indicate that the ACJC system can accurately predict the incidence angle and adjust the Coanda jet mass flow rate in real time at different inci⁃dence angles and different incoming Mach numbers.In addition,compared to the cascade without the ACJC system at the incidence angel of 5°,the total pressure loss coefficient is reduced by 11.5%,9.8%and 8.0%for incoming Mach numbers of 0.4,0.5 and 0.6,respectively.
作者
张健
张敏
杜娟
黄伟亮
聂超群
ZHANG Jian;ZHANG Min;DU Juan;HUANG Weiliang;NIE Chaoqun(School of Energy Power and Mechanical Engineering,North China Electric Power University,Beijing 100096,China;Advanced Gas Turbine Laboratory,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China;Key Laboratory of Advanced Energy and Power,Chinese Academy of Sciences,Beijing 100190,China;Innovation Academy for Light-Duty Gas Turbine,Chinese Academy of Sciences,Beijing 100190,China;School of Engineering Science,University of Chinese Academy of Sciences,Beijing 100049,China;School of Energy and Power Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2023年第22期182-195,共14页
Acta Aeronautica et Astronautica Sinica
基金
国家科技重大专项(2017-II-0004-0017,J2019-II-0020-0041)
中国科学院战略性先导科技专项(XDA29050000)。
