轴流压气机旋转失速建模与检测：基于确定学习理论与高阶Moore-Greitzer模型的研究

王聪; 文彬鹤; 司文杰; 彭滔; 袁成志; 陈填锐; 林文愉; 王勇; 侯安平

doi:10.3724/SP.J.1004.2014.01265

轴流压气机旋转失速建模与检测：基于确定学习理论与高阶Moore-Greitzer模型的研究

doi: 10.3724/SP.J.1004.2014.01265

1.
华南理工大学自动化科学与工程学院广州 510640;
2.
中航工业航空动力控制系统研究所无锡 214063;
3.
重庆理工大学电子信息与自动化学院重庆 400054;
4.
广东工业大学自动化学院广州 510006;
5.
北京大学力学与工程科学系北京 100871;
6.
北京航空航天大学能源与动力工程学院北京 100191

基金项目:

国家杰出青年科学基金（61225014），国家自然科学基金重点项目（60934001），国家自然科学基金（61304084），重庆理工大学科研启动基金（2013ZD01）资助

详细信息

作者简介:
文彬鹤中航工业航空动力系统研究所系统工程师. 主要研究方向为航空发动机全权限数字电子控制研究，确定学习理论，动态模式识别.E-mail：bhwen5516521@126.com

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出版历程
- 收稿日期: 2013-06-19
- 修回日期: 2014-01-27
- 刊出日期: 2014-07-20

Modeling and Detection of Rotating Stall in Axial Flow Compressors：Part I——Investigation on High-order M-G Models via Deterministic Learning

1.
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640;
2.
AVIC Aviation Motor Control System Institute, Wuxi 214063;
3.
College of Electronic Information and Automation, Chongqing University of Technology, Chongqing 400054;
4.
School of Automation, Guangdong University of Technology, Guangzhou 510006;
5.
Department of Mechanics and Engineering Science, Peking University, Beijing 100871;
6.
School of Energy and Power Engineering, Beihang University, Beijing 100191

Funds:

Supported by National Science Fund for Distinguished Young Scholars (61225014), State Key Program of National Natural Science Foundation of China (60934001), National Natural Science Foundation of China (61304084), and Startup Fund of Chongqing University of Technology (2013ZD01)

摘要

摘要: 喘振和旋转失速是轴流压气机研究领域中重要而困难的问题. 本文基于确定学习理论及动态模式识别方法提出一个旋转失速初始扰动近似准确建模和快速检测的方法. 首先，基于高阶Moore-Greitzer模型（Mansoux模型），利用确定学习理论提出一个对旋转失速初始扰动的内在系统动态的近似准确建模方法；其次，基于以上近似准确建模，利用动态模式识别方法提出一个对旋转失速初始扰动的快速检测方法. 基于MIT的Mansoux-C2模型仿真研究验证了所提方法的有效性. 最后，在北京航空航天大学航空发动机重点实验室的低速轴流压气机试验台上开展了试验研究. 通过对低速轴流压气机试验台参数进行测量，得到基于北航低速轴流压气机试验台的Mansoux模型. 通过对基于北航试验台Mansoux模型进行仿真研究，验证了所提方法的有效性.
- 涡扇发动机 /
- 轴流压气机 /
- 旋转失速 /
- 喘振 /
- 故障检测 /
- 确定学习 /
- 动态模式识别
Abstract: Rotating stall and surge are important and challenging problems in the area of axial compressors. This paper presents an approach for approximately accurate modeling and rapid detection of stall precursors based on deterministic learning. Firstly, based on the high-order compressor model (Mansoux model), a method for modeling the system dynamics corresponding to stall precursors is presented by employing deterministic learning algorithm; Secondly, a scheme for rapid detection of stall precursors is proposed by using dynamical pattern recognition algorithm; Thirdly, experiments are conducted on a low-speed research compressor of Beihang University. By measuring relevant parameters of the compressor, the Mansoux-Beihang model is obtained. Simulation studies on the Mansoux-C2 model and the Mansoux-Beihang model are included to show the effectiveness of the approach.
- Turbofan engine /
- axial compressor /
- rotating stall /
- surge /
- fault detection /
- deterministic learning /
- dynamic pattern recognition

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参考文献(43)

[1]	Wu Yan-Hui, Chu Wu-Li, Zhang Hao-Guang. A review of studies on stall precursors in axial-flow compressor. Advances in Mechanics, 2008, 38(5): 571-584(吴艳辉, 楚武利, 张皓光. 轴流压气机失速初始扰动的研究进展. 力学进展, 2008, 38(5): 571-584)
[2]	Greitzer E M. The stability of pumping systems——the 1980 freeman scholar lecture. ASME Journal of Fluid Engineering, 1981, 103(2): 193-242
[3]	Moore F K, Greitzer E M. A theory of post-stall transients in axial compression systems, Part I: Development of equations. Journal of Engines for Gas Turbines and Power, 1986, 108(1): 68-76
[4]	Moore F K, Greitzer E M. A theory of post-stall transients in axial compression systems, Part II: Application. Journal of Engines for Gas Turbines and Power, 1986, 108(2): 231-239
[5]	Park H G. Unsteady Disturbance Structures in Axial Flow Compressor Stall Inception [Master dissertation], Massachusetts Institute of Technology, USA, 1994
[6]	Gong Y F. A Computational Model for Rotating Stall and Inlet Distortions in Multistage Compressors [Ph.D. dissertation], Massachusetts Institute of Technology, USA, 1999
[7]	Vo H D, Tan C S, Greitzer E M. Criteria for spike initiated rotating stall. ASME Journal of Turbomachinery, 2008, 130(1): 1-9
[8]	Day I J, Breuer T, Escuret J, Cherrett M, Wilson A. Stall inception and the prospects for active control in four high-speed compressors. Journal of Turbomachinery, 1999, 121(1): 18-27
[9]	McCaughan F E. Application of bifurcation theory to axial flow compressor instability. Journal of Turbomachinery, 1989, 111(4): 426-433
[10]	McCaughan F E. Bifurcation analysis of axial flow compressor stability. SIAM Journal on Applied Mathematics, 1990, 50(5): 1232-1253
[11]	Abed E H, Houpt P K, Hosny W M. Bifurcation analysis of surge and rotating stall in axial flow compressors. AMSE Journal of Turbomachinery, 1993, 115(4): 817-824
[12]	Adomaitis R A, Abed E H. Bifurcation analysis of nonuniform flow patterns in axial-flow gas compressors. In: Proceedings of the 1st World Congress on Nonlinear Analysis. NJ, USA: Walter de Gruyter and Co. Hawthorne, 1992
[13]	Mansoux C A, Gysling D L, Setiawan J D, Paduano J D. Distributed nonlinear modeling and stability analysis of axial compressor stall and surge. In: Proceedings of the 1994 American Control Conference. Baltimore, MD, USA: IEEE, 1994. 2305-2316.
[14]	Paduano J D, Valavani L, Epstein A H. Parameter identification of compressor dynamics during closed-loop operation. In: Proceedings of the 1991 American Control Conference. Boston, MA, USA: IEEE, 1991. 2379-2385
[15]	Paduano J D, Valavani L, Epstein A H, Greitzer E M, Guenette G R. Modeling for control of rotating stall. Automatic, 1994, 30(9): 1357-1373
[16]	Epstein A H. "Smart" engine components: a micro in every blade. Aerospace America, 1986, 24(1): 1290-1315
[17]	Mcdougal N M, Cumpsty N A, Hynes T P. Stall inception in axial compressors. ASME Journal of Turbomachinery, 1990, 112(1): 116-125
[18]	Garnier V H, Epstein A H, Greitzer E M. Rotating waves as a stall inception indication in axial compressors. ASME Journal of Turbomachinery, 1991, 113(2): 290-301
[19]	Day I J. Stall inception in axial flow compressor. ASME Journal of Turbomachinery, 1993, 115(1): 1-9
[20]	Tryfonidis M, Etchevers O, Paduano J D, Epstein A H, Hendricks G J. Prestall behavior of several high-speed compressors. ASME Journal of Turbomachinery, 1995, 117(1): 62-80
[21]	Hoss B, Leinhos D, Fottner L. Stall inceptions in the compressor systme of a turbofan engine. ASME Journal of Turbomachinery, 1998, 122(1): 32-44
[22]	Liao S F, Chen J Y. Time-frequency analysis of compressor rotating stall by means of wavelet transform. In: Proceedings of the 1996 ASME, 1996. DOI: 96-GT-57
[23]	Xie Feng, Xie Shou-Sheng. Detection of rotating stall in aircraft engine based on wavelet analysis. Journal of Aerospace Power, 2006, 21(4): 754-758(谢峰, 谢寿生. 基于小波分析的航空发动机旋转失速检测方法. 航空动力学报, 2006, 21(4): 754-758)
[24]	Tan C S, Day I J, Morris S, Wadia A. Spike-type compressor stall inception, detection, and control. Annual Review of Fluid Mechanics, 2010, 42(1): 275-300
[25]	Deteriou M A, Rosen I G. On the persistence of excitation in the adaptive extimation of distributed parameter systems. IEEE Transactions on Automatic Control, 1994, 39(5): 1117-1123
[26]	Deteriou M A, Rosen I G. Adaptive identification of second-order distributed parameter systems. Inverse Problems, 1994, 10(2): 261-294
[27]	Hong K S, Bentsman J. Direct adaptive control of parabolic systems: Algorithm synthesis and convergence and stability analysis. IEEE Transactions on Automatic Control, 1994, 39(10): 2018-2033
[28]	Orlov Y, Bentsman J. Adaptive distributed parameter systems identification with enforceable identifiability condition and reduced-order spatial differentiation. IEEE Transactions on Automatic Control, 2000, 45(2): 203-216
[29]	Baumeister J, Scondo W, Demetriout M A, Rosen I G. On-line parameter estimation for infinite-demensional dynamical systems. SIAM Journal on Control and Optimization, 1997, 35(2): 678-713
[30]	Qu Shou-De, Li Song-Shi, Dai He-Ping. The application of Galerkin's method to parameter identification of one-dimensional parabolic partial differential equation and its boundary conditions. Acta Automatica Sinica, 1989, 15(2): 156-160(瞿寿德, 李松石, 戴和平. 应用Galerkin方法辨识一维抛物型偏微分方程及其边界条件中的参数. 自动化学报, 1989, 15(2): 156-160)
[31]	Narendra K S, Annaswamy A M. Stable Adaptive Systems. Englewood Cliffs, NJ: Prentice-Hall, 1989
[32]	Sastry S S, Bodson M. Adaptive Control: Stability, Convergence, and Robustness. Englewood Cliffs, NJ: Prentice-Hall, 1989
[33]	Lu S, Basar T. Robust nonlinear system identification using neural-network models. IEEE Transactions on Neural Networks, 1998, 9(3): 407-429
[34]	Gevers M. A personal view of the development of system identification. IEEE Control Systems Magazine, 2006, 26(6): 93-105
[35]	Wang Le-Yi, Zhao Wen-Xiao. System identification: new paradigms, challenges, and opportunities. Acta Automatica Sinica, 2013, 39(7): 933-942(王乐一, 赵文虓. 系统辨识: 新的模式、挑战及机遇. 自动化学报, 2013, 39(7): 933-942)
[36]	Wang C, Hill D J. Deterministic learning and rapid dynamical pattern recognition. IEEE Transactions on Neural Networks, 2007, 18(3): 617-630
[37]	Wang C, Chen T R, Chen G R, Hill D J. Deterministic learning of nonlinear dynamical systems. International Journal of Bifurcation and Chaos, 2009, 19(4): 1307-1328
[38]	Wang Cong, Chen Tian-Rui, Liu Teng-Fei. Deterministic learning and data-based modeling and control. Acta Automatica Sinica, 2009, 35(6): 693-706(王聪, 陈填锐, 刘腾飞. 确定学习与基于数据的建模及控制. 自动化学报, 2009, 35(6): 693-706)
[39]	Wang C, Hill D J. Deterministic Learning Theory for Identification, Recognition and Control. Boca Raton, FL: CRC Press, 2009
[40]	Wang C, Chen T R. Rapid detection of small oscillation faults via deterministic learning. IEEE Transactions on Neural Networks, 2011, 22(8): 1284-1296
[41]	Zhang X, Polycarpou M M, Parisini T. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Transactions on Automatic Control, 2002, 47(4): 576-593.
[42]	Wang Y, Murray R M. Bifurcation control of rotating stall with actuator magnitude and rate limits: Part I——model reduction and qualitative dynamics. Automatica, 2002, 38(4): 597-610
[43]	Robinson J C. All possible chaotic dynamics can be approximated in three dimensions. Nonlinearity, 1998, 11(3): 529-545

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轴流压气机旋转失速建模与检测：基于确定学习理论与高阶Moore-Greitzer模型的研究

doi: 10.3724/SP.J.1004.2014.01265

作者简介:
文彬鹤中航工业航空动力系统研究所系统工程师. 主要研究方向为航空发动机全权限数字电子控制研究，确定学习理论，动态模式识别.E-mail：bhwen5516521@126.com

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Modeling and Detection of Rotating Stall in Axial Flow Compressors：Part I——Investigation on High-order M-G Models via Deterministic Learning

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轴流压气机旋转失速建模与检测：基于确定学习理论与高阶Moore-Greitzer模型的研究

doi: 10.3724/SP.J.1004.2014.01265

作者简介: 文彬鹤 中航工业航空动力系统研究所系统工程师. 主要研究方向为航空发动机全权限数字电子控制研究，确定学习理论，动态模式识别.E-mail：bhwen5516521@126.com

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Modeling and Detection of Rotating Stall in Axial Flow Compressors：Part I——Investigation on High-order M-G Models via Deterministic Learning

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出版历程

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作者简介:
文彬鹤中航工业航空动力系统研究所系统工程师. 主要研究方向为航空发动机全权限数字电子控制研究，确定学习理论，动态模式识别.E-mail：bhwen5516521@126.com