Fast automatic correction method for component characteristics of the identification dynamic model of VCE
-
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。
-
关键词:
- 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.
-
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 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 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 nl nh 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 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 nl nh 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 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 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 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 双涵 0.001 0.07 -
[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 ChineseTANG 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 ChineseZHOU 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 ChineseZHENG 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 ChineseJIA 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 ChineseZHOU 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 ChineseYU 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 ChineseZHENG 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 ChinesePAN 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 ChineseWANG 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 ChinesePAN 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 ChineseCHEN 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 ChineseDUAN 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 ChineseLU 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 ChineseCHENG 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 ChineseLU 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)