基于熵的航空发动机压气机失速与喘振仿真

doi:10.16182/j.issn1004731x.joss.201802023

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (2): 551-559.doi: 10.16182/j.issn1004731x.joss.201802023

基于熵的航空发动机压气机失速与喘振仿真

皮骏, 邱永超, 胡智伟, 武威

中国民航大学通用航空学院,天津 300300

收稿日期:2015-12-30 出版日期:2018-02-08 发布日期:2019-01-02
作者简介:皮骏(1973-), 男, 湖南, 博士, 副教授, 研究方向为流体机械, 航空发动机; 邱永超(1990-), 男, 河南, 研究生, 研究方向为流体机械。
基金资助:
中央高校基本科研业务费中国民航大学专项(3122013H001)

Simulation of Surge and Stall for Aero Engine Compressor Based on Entropy

Pi Jun, Qiu Yongchao, Hu Zhiwei, Wu Wei

General Aviation College, Civil Aviation University of China, Tianjin 300300, China

Received:2015-12-30 Online:2018-02-08 Published:2019-01-02

摘要/Abstract

摘要： 采用NASA Rotor37模型并用CFX仿真软件模拟压气机从稳定工作状态到失速与喘振的非稳定工作状态的转变过程,用热力学熵和样本熵的相关理论对这一过程进行分析研究。以实现为压气机失速与喘振的故障诊断提供依据。通过分析,发现平均比熵值和等熵效率这两个热力学熵参数的变化情况在发生失速时出现异常;由机匣处静压序列信号计算所得的样本熵值在近失速点附近达到最小值。这些结果可为压气机失速与喘振的故障诊断提供重要依据。

关键词: 压气机, 热力学熵, 样本熵, 失速, 喘振, 故障诊断

Abstract: The mature model NASA Rotor37 is used to simulate the transition of the compressor’s operation from the steady state to the non-steady state of stall and surge using the simulation software CFX. The transition is analyzed and researched based on the theory of thermodynamics entropy and sample entropy to provide a basis for the fault diagnosis of stall and surge. The two thermodynamic entropy parameters of average specific entropy value and the isentropic efficiency are found by the analysis changing abnormally in the event of stall. The values of the sample entropy obtained by calculation using the static pressure sequence signal at the receiver reach the minimum near the operation point of stall. The goal of providing an important basis for the fault diagnosis of the compressor stall and surge is achieved.

Key words: compressor, thermodynamic entropy, sample entropy, stall, surge, fault diagnosis

中图分类号:

TP391.9

皮骏, 邱永超, 胡智伟, 武威. 基于熵的航空发动机压气机失速与喘振仿真[J]. 系统仿真学报, 2018, 30(2): 551-559.

Pi Jun, Qiu Yongchao, Hu Zhiwei, Wu Wei. Simulation of Surge and Stall for Aero Engine Compressor Based on Entropy[J]. Journal of System Simulation, 2018, 30(2): 551-559.

参考文献 12

[1]	徐可君, 江龙平. 基于Lyapunov指数谱的航空发动机故障诊断研究[J]. 应用力学学报, 2006, 23(3): 488-492.Xu Kejun, Jiang Longping. Lyapunov Exponent Spectrum with Application to Aero Engine Fault Diagnosis[J]. Chinese Journal of Applied Mechanic, 2006, 23(3): 488-492.
[2]	刘秉正, 彭建华. 非线性动力学[M]. 北京:高等教育出版社, 2004.Liu Bingzheng, Peng Jianhua. Nonlinear Dynamics[M]. Beijing: Higher Education Press, 2004.
[3]	刘志刚, 杨荣菲, 向宏辉. 基于模糊熵的压气机失速信号分析研究[J]. 测控技术, 2014, 33(2): 53-56.Liu Zhigang, Yang Rongfei, Xiang Honghui. Analytical Investigation of Compressor Stall Signal Based on FuzzyEn[J]. Measurement and Control Technology, 2014, 33(2): 53-56.
[4]	李明高. ANSYS 13.0流场分析技术及应用实例[M]. 北京:机械工业出版社, 2012.Li Minggao. ANSYS 13.0 Flow Field Analysis Technolgy and Examples[M]. Beijing: China Machine Press, 2012.
[5]	谢龙汉. ANSYS CFX流体分析及仿真[M]. 北京:电子工业出版社, 2012. Xie Longhan. ANSYS CFX Fluid Analysis and Simulation[M]. Beijing: Electronic Industry Press, 2012.
[6]	Reid L, Moore R D.Design and overall performance of four highly loaded, high speed inlet stages for an advanced high-pressure-ratio core compressor[R]. NASA-TP-1337, 1978.
[7]	吕金虎, 陆君安, 陈士华. 混沌时间序列分析及其应用[M]. 武汉:武汉大学出版社, 2002.Lu Jinhu,Lu Junan, Chen Shihua. Chaotic Time Series Analysis and Its Application[M]. Wuhan: Wuhan University Press, 2002.
[8]	胡骏, 吴铁鹰, 曹人靖. 航空叶片机原理[M]. 北京:国防工业出版社, 2006.Hu Jun, Wu Tieying, Cao Renjing. Aerofoil Principles[M]. Beijing: National Defense Industry Press, 2006.
[9]	张继国,辛格. 信息熵:理论与应用[M]. 北京:中国水利水电出版社, 2012.Zhang Jiguo, Vijay P Singh. Entropy of Information: Theory and Application[M]. Beijing: China Water&Power Press, 2012.
[10]	Richman J S, Moorman J R.Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039.
[11]	Lake D E, Richman J S, Griffin M P, et al.Sample entropy analysis of neonatal heart rate variability[J]. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2002, 283(3): R789-R797.
[12]	白冬梅. 脑电信号的特性分析与特征提取[D]. 大连:大连理工大学, 2006.Bai Dongmei. The Extraction And Analysis of EEG Features[D]. Dalian: Dalian University of Technology, 2006.

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基于熵的航空发动机压气机失速与喘振仿真

Simulation of Surge and Stall for Aero Engine Compressor Based on Entropy

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