VCE身份证动态模型部件特性快速自动修正方法

邹泽龙; 黄金泉; 周鑫; 周文祥; 鲁峰

doi:10.13224/j.cnki.jasp.20220680

VCE身份证动态模型部件特性快速自动修正方法

doi: 10.13224/j.cnki.jasp.20220680

南京航空航天大学能源与动力学院，南京 210016

基金项目: 中央高校基本科研业务费项目（NP2022418）

详细信息

作者简介:
邹泽龙（1997-），男，博士生，主要从事航空发动机模型修正和健康监测研究

通讯作者:
鲁峰（1981-），男，教授，博士，主要从事航空发动机建模、控制和故障诊断融合方法的研究。E-mail：lufengnuaa@126.com

中图分类号: V233.7
计量
- 文章访问数: 41
- HTML浏览量: 23
- PDF量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-13
- 网络出版日期: 2023-11-20

Fast automatic correction method for component characteristics of the identification dynamic model of VCE

College of Energy and Power Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China

摘要

摘要:
为实现模型部件特性快速自动修正的工程需求，提出一种针对身份证模型部件特性的增强型自动修正策略，以稳态试车数据为输入，依据可测传感器组合，分析选取合适的特性修正系数组合，并耦合个体试车数据对共同工作方程进行设计，利用多点修模的双环策略快速自动修正部件特性，实现某型变循环发动机身份证模型的快速自动修正。采取逆流路扰动及Newton-Raphson迭代阻尼系数自调整法和特性图外插保护逻辑等方法提高算法的运行速率和稳定性。仿真结果表明：修正后模型输出最大误差小于0.1%，在2.10 GHz处理器的计算机上单、双涵道模式与常规部件级模型相比，耗时减少98.6%以上，所修正后模型可用于控制律设计以及为确定发动机当前真实状态提供参考。
- 变循环发动机 /
- 增强型模型快速自动修正策略 /
- 多点修模的双环策略 /
- Newton-Raphson迭代阻尼系数自调整 /
- 身份证动态模型
Abstract:
To realize the engineering requirement for fast automatic correction of model component characteristics, an enhanced automatic correction strategy of the identification model component characteristics was proposed. Taking steady-state test data as input, the designed correction strategy allowed to analyze and select suitable characteristic correction coefficient combinations based on sensor measurements. The proposed method also coupled individual rig test data of engine to design the equilibrium equations, and used the double-loop strategy of multi-point model correction to quickly and automatically correct component characteristics. Finally, the fast automatic correction of the identification model of a certain variable cycle engine was realized. The inverse flow path disturbance, damping coefficient self-adjustment method of Newton-Raphson method and characteristic map interpolation protection logic were adopted to improve the operation rate and stability of the algorithm. The simulation results showed that the maximum output error of the corrected model was less than 0.1%, and the consuming-time was reduced by more than 98.6% compared with the common component-level model, which was simulated in single and double bypass modes on a computation with 2.10 GHz processor. The corrected model can be used for control law design and also provide a reference for determining the current real state of the engine.

HTML

图 1 VCE气路部件图

Figure 1. Gas path components of VCE

下载: 全尺寸图片幻灯片

图 2 模型输出变化均值图

Figure 2. Averages of model output change

下载: 全尺寸图片幻灯片

图 3 双层NR迭代结构图

Figure 3. Structure of double-loop NR method

下载: 全尺寸图片幻灯片

图 4 单层NR迭代结构图

Figure 4. Structure of single-loop NR method

下载: 全尺寸图片幻灯片

图 5 基于NR迭代法的多点模型自动修正方法

Figure 5. Automatic multi-point correction method based on NR method

下载: 全尺寸图片幻灯片

图 6 压气机压比-流量局部特性图

Figure 6. Part pressure-flow characteristic map of compressor

下载: 全尺寸图片幻灯片

图 7 旋转部件耗时占比图

Figure 7. Time consumption proportion of rotating parts

下载: 全尺寸图片幻灯片

图 8 待求解参数在气路计算中参与工作位置图

Figure 8. Working position of the parameters to be solved in the gas path calculation

下载: 全尺寸图片幻灯片

图 9 NR迭代阻尼系数自调整法调整量变化图

Figure 9. Change of self adjustment of NR method damping coefficient

下载: 全尺寸图片幻灯片

图 10 修正前后压缩部件部分特性图比较图

Figure 10. Comparison of partial characteristic maps of compressor before and after correction

下载: 全尺寸图片幻灯片

图 11 单涵模式特性图修正后共同工作点分布图

Figure 11. Distribution of cooperating points after correction of characteristic maps of single culvert mode

下载: 全尺寸图片幻灯片

图 12 双涵模式特性图修正后共同工作点分布图

Figure 12. Distribution of cooperating points after correction of characteristic maps of double culvert mode

下载: 全尺寸图片幻灯片

图 13 常规双层NR不同外层收敛精度迭代耗时比较图

Figure 13. Time-consuming comparison of conventional double-loop NR method with different outer convergence accuracy

下载: 全尺寸图片幻灯片

图 14 3种方法耗时比较图

Figure 14. Time consuming comparison of three methods

下载: 全尺寸图片幻灯片

表 1 特性修正系数编号表

Table 1. Identifiers of characteristic correction coefficients

编号	物理意义	符号
1	风扇效率修正系数	h₁
2	风扇流量修正系数	h₂
3	风扇压比修正系数	h₃
4	CDFS效率修正系数	h₄
5	CDFS流量修正系数	h₅
6	CDFS压比修正系数	h₆
7	压气机效率修正系数	h₇
8	压气机流量修正系数	h₈
9	压气机压比修正系数	h₉
10	高压涡轮效率修正系数	h₁₀
11	高压涡轮流量修正系数	h₁₁
12	高压涡轮压比修正系数	h₁₂
13	低压涡轮效率修正系数	h₁₃
14	低压涡轮流量修正系数	h₁₄
15	低压涡轮压比修正系数	h₁₅

下载: 导出CSV

表 2 VCE模型各主要部件耗时

Table 2. Time consumption of main components of VCE

部件	耗时/ms	占比/%	部件	耗时/ms	占比/%
进气道	19	0.2	高压涡轮	1417	14.83
风扇	1760	18.43	低压涡轮	1623	16.99
CDFS	1762	18.45	外涵道	184	1.93
压气机	2164	22.65	尾喷管	581	6.08
燃烧室	42	0.44

下载: 导出CSV

表 3 基于双层NR迭代法模型修正后输出平均误差表

Table 3. Average output error after model correction based on double-loop NR method %

发动机模式	外层收敛精度	n_l	n_h	T₂₁	p₂₁	T₂₄	p₂₄	T₃	p₃	T₅	p₅
单涵	0.1	0.010	0.036	0.033	0.012	0.020	0.049	0.031	0.082	0.117	0.096
	0.5	0.048	0.178	0.161	0.061	0.101	0.241	0.156	0.400	0.569	0.472
	1.0	0.094	0.348	0.306	0.116	0.182	0.475	0.279	0.787	1.112	0.928
	2.0	0.177	0.679	0.602	0.235	0.360	0.924	0.551	1.533	2.152	1.811
双涵	0.1	0.048	0.035	0.005	0.033	0.001	0.065	0.014	0.092	0.140	0.089
	0.5	0.214	0.170	0.030	0.130	0.012	0.285	0.086	0.404	0.776	0.468
	1.0	0.418	0.358	0.062	0.283	0.023	0.625	0.173	0.878	1.475	0.929
	2.0	0.971	0.724	0.124	0.616	0.029	1.330	0.323	1.822	2.608	1.749

下载: 导出CSV

表 4 基于单层NR迭代法模型修正后输出平均误差表

Table 4. Average output error after model correction based on single-loop NR method %

发动机模式	n_l	n_h	T₂₁	p₂₁	T₂₄	p₂₄	T₃	p₃	T₅	p₅
单涵	0	0	0.001	0	0.001	0	0.003	0	0.051	0
双涵	0	0	0.008	0	0.011	0	0.020	0	0.088	0

下载: 导出CSV

表 5 风扇部件部分特性修正系数

Table 5. Partial characteristic correction coefficients of fan components

转速	流量修正系数	效率修正系数
0.70	0.947	0.951
0.80	0.973	0.969
0.90	0.981	0.980
0.95	0.981	0.980
1.00	0.981	0.980

下载: 导出CSV

表 6 增强型与常规自动修正双环策略耗时比较表

Table 6. Comparison of time consumption between enhanced and conventional automatic correction double-loop strategies

修正方法	发动机模式	外层收敛精度	耗时/s
身份证模型自动修正双环策略	单涵	0.001	0.08
身份证模型自动修正双环策略	双涵	0.001	0.11
增强型身份证模型自动修正双环策略	单涵	0.001	0.05
增强型身份证模型自动修正双环策略	双涵	0.001	0.07

下载: 导出CSV

参考文献(25)

[1]	唐海龙. 面向对象的航空发动机性能仿真系统及其应用[D]. 北京: 北京航空航天大学,2000. TANG Hailong. Object-oriented aero-engine performance simulation system and its application[D]. Beijing: Beihang University,2000. (in Chinese TANG Hailong. Object-oriented aero-engine performance simulation system and its application[D]. Beijing: Beihang University, 2000. (in Chinese)
[2]	周红,王占学,刘增文,等. 双外涵变循环发动机可变几何特性研究[J]. 航空学报,2014,35(8): 2126-2135. ZHOU Hong,WANG Zhanxue,LIU Zengwen,et al. Variable geometry characteristics research of double bypass variable cycle engine[J]. Acta Aeronautica et Astronautica Sinica,2014,35(8): 2126-2135. (in Chinese ZHOU Hong, WANG Zhanxue, LIU Zengwen, et al. Variable geometry characteristics research of double bypass variable cycle engine[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(8): 2126-2135. (in Chinese)
[3]	DU Xian,GUO Yingqing,SUN Hao. An adaptive model predictive controller for turbofan engines[J]. American Journal of Engineering Research,2015,4(12): 170-176.
[4]	郑铁军,王曦,罗秀芹,等. 建立航空发动机状态空间模型的修正方法[J]. 推进技术,2005,26(1): 46-49. ZHENG Tiejun,WANG Xi,LUO Xiuqin,et al. Modified method of establishing the state space model of aeroengine[J]. Journal of Propulsion Technology,2005,26(1): 46-49. (in Chinese ZHENG Tiejun, WANG Xi, LUO Xiuqin, et al. Modified method of establishing the state space model of aeroengine[J]. Journal of Propulsion Technology, 2005, 26(1): 46-49. (in Chinese)
[5]	KURZKE J. How to get component maps for aircraft gas turbine performance calculations[R]. ASME Paper 96-GT-164,1996.
[6]	贾琳渊,程荣辉,张志舒,等. 研发阶段涡扇发动机模型自适应方法[J]. 推进技术,2020,41(9): 1935-1945. JIA Linyuan,CHENG Ronghui,ZHANG Zhishu,et al. Adaptive modelling for turbofan engine in development stage[J]. Journal of Propulsion Technology,2020,41(9): 1935-1945. (in Chinese JIA Linyuan, CHENG Ronghui, ZHANG Zhishu, et al. Adaptive modelling for turbofan engine in development stage[J]. Journal of Propulsion Technology, 2020, 41(9): 1935-1945. (in Chinese)
[7]	周文祥. 航空发动机及控制系统建模与面向对象的仿真研究[D]. 南京: 南京航空航天大学,2006. ZHOU Wenxiang. Research on object-oriented modeling and simulation for aeroengine and control system[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2006. (in Chinese ZHOU Wenxiang. Research on object-oriented modeling and simulation for aeroengine and control system[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006. (in Chinese)
[8]	姚华. 航空发动机全权限数字电子控制系统[M]. 北京: 航空工业出版社,2014: 22-32.
[9]	俞明帅. 航空发动机模型组态与修正技术研究[D]. 南京: 南京航空航天大学,2012. YU Mingshuai. Research on configuration and correction technology for aero-engines modeling[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2012. (in Chinese YU Mingshuai. Research on configuration and correction technology for aero-engines modeling[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012. (in Chinese)
[10]	钟文城,汪勇,宋劼,等. 一种面向航空发动机数学模型的新型修正方法[J]. 航空动力学报, 2023, 38(11):2776-2784. ZHONG Wencheng,WANG Yong,SONG Jie,et al. A new correction method for aero-engine mathematical model[J]. Journal of Aerospace Power, 2023, 38(11):2776-2784. ZHONG Wencheng, WANG Yong, SONG Jie, et al. A new correction method for aero-engine mathematical model[J]. Journal of Aerospace Power, 2023, 38（11）: 2776-2784.
[11]	郑斐华. 基于系统辨识的航空发动机建模研究[D]. 北京: 中国科学院大学,2018. ZHENG Feihua. Aeroengine modeling research based on system identification[D]. Beijing: Chinese Academy of Sciences,2018. (in Chinese ZHENG Feihua. Aeroengine modeling research based on system identification[D]. Beijing: Chinese Academy of Sciences, 2018. (in Chinese)
[12]	潘鹏飞. 高精度航空发动机机载自适应实时模型研究[D]. 南京: 南京航空航天大学,2014. PAN Pengfei. Research on high-accuracy on-board real-time adaptive model of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2014. (in Chinese PAN Pengfei. Research on high-accuracy on-board real-time adaptive model of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014. (in Chinese)
[13]	LI Y G,ABDUL GHAFIR M F,WANG L,et al. Nonlinear multiple points gas turbine off-design performance adaptation using a genetic algorithm[J]. Journal of Engineering for Gas Turbines and Power,2011,133(7): 42-50.
[14]	TSOUTSANIS E,MESKIN N,BENAMMAR M,et al. A component map tuning method for performance prediction and diagnostics of gas turbine compressors[J]. Applied Energy,2014,135: 572-585.
[15]	TSOUTSANIS E,MESKIN N,BENAMMAR M,et al. Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps[J]. Journal of Engineering for Gas Turbines and Power,2015,137(9): 091201.
[16]	王军,隋岩峰. 整机条件下涡扇发动机部件特征参数辨识[J]. 航空动力学报,2013,28(3): 666-672. WANG Jun,SUI Yanfeng. Identification of component characteristic parameter for whole turbofan engine[J]. Journal of Aerospace Power,2013,28(3): 666-672. (in Chinese WANG Jun, SUI Yanfeng. Identification of component characteristic parameter for whole turbofan engine[J]. Journal of Aerospace Power, 2013, 28(3): 666-672. (in Chinese)
[17]	VISSER W P J,KOGENHOP O,OOSTVEEN M. A generic approach for gas turbine adaptive modeling[J]. Journal of Engineering for Gas Turbines and Power,2006,128(1): 13-19.
[18]	潘阳. 涡轴发动机控制系统传感器故障诊断与容错控制[D]. 南京: 南京航空航天大学,2016. PAN Yang. Research on turbo-shaft engine control system sensor fault diagnosis and fault tolerant control[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2016. (in Chinese PAN Yang. Research on turbo-shaft engine control system sensor fault diagnosis and fault tolerant control[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2016. (in Chinese)
[19]	陈玉春,徐思远,屠秋野,等. 求解航空发动机非线性方程组的变步长牛顿法[J]. 航空计算技术,2009,39(1): 39-41,44. CHEN Yuchun,XU Siyuan,TU Qiuye,et al. Variable step Newton method for solving the nonlinear equations of aero turbine engines[J]. Aeronautical Computing Technique,2009,39(1): 39-41,44. (in Chinese CHEN Yuchun, XU Siyuan, TU Qiuye, et al. Variable step Newton method for solving the nonlinear equations of aero turbine engines[J]. Aeronautical Computing Technique, 2009, 39(1): 39-41, 44. (in Chinese)
[20]	段守付,樊思齐,卢燕. 航空发动机自适应建模技术研究[J]. 航空动力学报,1999,14(4): 440-442,457. DUAN Shoufu,FAN Siqi,LU Yan. Adaptive modelling technique for aeroengine[J]. Journal of Aerospace Power,1999,14(4): 440-442,457. (in Chinese DUAN Shoufu, FAN Siqi, LU Yan. Adaptive modelling technique for aeroengine[J]. Journal of Aerospace Power, 1999, 14(4): 440-442, 457. (in Chinese)
[21]	LU Feng,HUANG Jinquan,JI Chunsheng,et al. Gas path on-line fault diagnostics using a nonlinear integrated model for gas turbine engines[J]. International Journal of Turbo & Jet-Engines,2014,31(3): 261-275.
[22]	陆军,郭迎清,张书刚. 基于改进混合卡尔曼滤波器的航空发动机机载自适应模型[J]. 航空动力学报,2011,26(11): 2593-2600. LU Jun,GUO Yingqing,ZHANG Shugang. Aeroengine on-board adaptive model based on improved hybrid Kalman filter[J]. Journal of Aerospace Power,2011,26(11): 2593-2600. (in Chinese LU Jun, GUO Yingqing, ZHANG Shugang. Aeroengine on-board adaptive model based on improved hybrid Kalman filter[J]. Journal of Aerospace Power, 2011, 26(11): 2593-2600. (in Chinese)
[23]	LI Y G,PILIDIS P,NEWBY M A. An adaptation approach for gas turbine design-point performance simulation[J]. Journal of Engineering for Gas Turbines and Power,2006,128(4): 789-795.
[24]	程都. 基于神经网络的航空发动机模型自适应修正[D]. 辽宁大连: 大连理工大学,2019. CHENG Du. Adaptive correction of aeroengine model based on neural network[D]. Dalian,Liaoning: Dalian University of Technology,2019. (in Chinese CHENG Du. Adaptive correction of aeroengine model based on neural network[D]. Dalian, Liaoning: Dalian University of Technology, 2019. (in Chinese)
[25]	鲁峰,黄金泉,仇小杰,等. 基于信息熵融合提取特征的发动机气路分析[J]. 仪器仪表学报,2012,33(1): 13-19. LU Feng,HUANG Jinquan,QIU Xiaojie,et al. Feature extraction based on information entropy fusion for turbo-shaft engine gas-path analysis[J]. Chinese Journal of Scientific Instrument,2012,33(1): 13-19. (in Chinese LU Feng, HUANG Jinquan, QIU Xiaojie, et al. Feature extraction based on information entropy fusion for turbo-shaft engine gas-path analysis[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 13-19. (in Chinese)