Research on Iterative Calculation and Optimization Methods of Aero-engine On-board Model

doi:10.16182/j.issn1004731x.joss.22-FZ0905

Abstract

Abstract:

Aeroengine is a complex and time-varying multivariable thermophysical system. The research on the convergence accuracy and rate of the component-level model is of great significance to the model-based engine health management, performance and fault-tolerant control. The existing engine component-level models are generally based on the traditional quasi-Newton method to solve the equilibrium equations simultaneously. Compared with the traditional Newton-Raphson method (N-R method), the convergence speed is optimized, but it is difficult to meet accuracy and real-time requirements of the dynamic model airborne applications within the full envelope. An adaptive variable step factor quasi-Newton method is proposed, which can reduce the number of model iterations to the greatest extent while ensuring the calculation accuracy of the model, and greatly improve the calculation speed of the model and create conditions for the on-board application of the model. The simulation test results based on the current engine computing platform MPC5554 microcontroller show that, compared with the traditional algorithm, the proposed adaptive variable step factor quasi-Newton algorithm has better convergence and real-time performance.

Key words: aero-engine component-level model, equilibrium equation, quasi-newton method, adaptive step size factor, real-time performance

CLC Number:

O242.23

Xinghua Luo, Jia Geng, Ming Li, Bei Liu, Lei Wang, Zhiping Song. Research on Iterative Calculation and Optimization Methods of Aero-engine On-board Model[J]. Journal of System Simulation, 2022, 34(12): 2649-2658.

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Table 1

Number of calls required for different initial residuals and different solution models

$Δ 0$	N-R	M-QN	Ada-QN
0.000 018	11	6	7
0.000 038	11	7	7
0.000 056	16	7	7
0.000 256	21	12	7
0.000 986	21	13	7
0.004 421	26	18	13
0.007 371	36	19	13
0.010 646	41	24	13
0.029 751	51	19	13
0.153 476	66	24	13
0.219 936	76	31	19
0.285 491	81	31	19
0.416 598	91	30	19
0.567 461	91	31	19

Table 1

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 19

1	李家瑞. 航空发动机建模技术研究[D]. 南京: 南京航空航天大学, 2005.
	Li Jiarui. Research on Simulation of Aero-Engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2005.
2	曹源, 金先龙, 孟光. 航空发动机的非线性模块化建模与仿真[J]. 计算机辅助设计与图形学学报, 2005, 17(3): 505-510.
	Cao Yuan, Jin Xianlong, Meng Guang. Non-Linear Modular Modeling and Simulation of Aeroengine[J]. Journal of Computer-Aided Design & Computer Grap, 2005, 17(3): 505-510.
3	Zhao H, Liao Z, Liu J, et al. A Highly Robust Thrust Estimation Method with Dissimilar Redundancy Framework for Gas Turbine engine[J]. Energy(S0360-5442), 2022, 245: 123255.
4	Xu M, Liu J, Li M, et al. Improved Hybrid Modeling Method with Input and Output Self-Tuning for Gas Turbine Engine[J]. Energy(S0360-5442), 2022, 238(A): 121672.
5	Liao Z, Wang J, Liu J, et al. Uncertainties in Gas-Path Diagnosis of Gas Turbines: Representation and Impact Analysis[J]. Aerospace Science and Technology (S1270-9638), 2021, 113: 106724.
6	温思歆. 基于模型参考自适应的航空发动机控制及验证[D]. 大连: 大连理工大学, 2020.
	Wen Sixin. Aero-engine Control and Verification Based on Model Reference Adaptive Controller[D]. Dalian: Dalian University Of Technology, 2020.
7	周文祥, 黄金泉, 黄开明. 航空发动机简化实时模型仿真研究[J]. 南京航空航天大学学报, 2005, 37(2): 251-255.
	Zhou Wenxiang, Huang Jinquan, Huang Kaiming. Real-Time Simulation System for Aeroengine Based on Simplified Model[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2005, 37(2): 251-255.
8	肖伟. 某型涡扇发动机实时建模与仿真研究[D]. 南京: 南京航空航天大学, 2008.
	Xiao Wei. The Research on Real-time Modeling and Simulation for a Turbine-fan Aeroengine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008.
9	尹大伟. 航空发动机模型求解算法及性能寻优控制中的参数估计研究[D]. 长沙: 国防科学技术大学, 2011.
	Yin Dawei. Algorithms for Solving Aeroengine Nonlinear Mathematical Model and Parameter Estimation in Performance-Seeking Control[D]. Changsha: National University of Defense Technology, 2011.
10	任子晖, 王坚. 加速收敛的粒子群优化算法[J]. 控制与决策, 2011, 26(2): 201-206.
	Ren Zihui, Wang Jian. Accelerate Convergence Particle Swarm Optimization Algorithm[J]. Control and Decision, 2011, 26(2): 201-206.
11	张强, 李本威, 马力. 粒子群算法在航空发动机部件模型求解中的应用[J]. 系统仿真学报, 2009, 21(12): 3584-3587.
	Zhang Qiang, Li Benwei, Ma Li. Application of Particle Swarm Optimization in Solution to Aeroengine Component-level Model[J]. Journal of System Simulation, 2009, 21(12): 3584-3587.
12	王军, 隋岩峰. 求解航空发动机数学模型的迭代算法及其改进算法的收敛性研究[J]. 系统仿真学报, 2014, 26(2): 310-314.
	Wang Jun, Sui Yanfeng. Study on Convergence and Improvement of Iteration Methods in Aero-engine Mathematical Models[J]. Journal of System Simulation, 2014, 26(2): 310-314.
13	陆军, 郭迎清, 张书刚. 航空发动机非线性模型实时计算的迭代方法研究[J]. 航空动力学报, 2010, 25(3): 681-686.
	Lu Jun, Guo Yingqing, Zhang Shugang. Research on the Iteration Methods in Aero-Engine Non-Linear Model Real-Time Computation[J]. Journal of Aerospace Power, 2010, 25(3): 681-686.
14	殷锴, 周文祥, 乔坤, 等. 航空发动机部件级模型实时性提高方法研究[J]. 推进技术, 2017, 38(1): 199-206.
	Yin Kai, Zhou Wenxiang, Qiao Kun, et al, Research on Methods of Improving Real-Time Performance for Aero-Engine Component Level Model[J]. Journal of Propulsion Technology, 2017, 38(1): 199-206.
15	伍谦, 李秋红, 单睿斌. 航空发动机气动热力系统模型求解方法研究[J]. 计算机仿真, 2019, 36(1): 76-81.
	Wu Qian, Li Qiuhong, Shan Ruibin. Research on Method for Solving Aero-Engine Thermodynamic System Model[J]. Computer Integrated Manufacturing system, 2019, 36(1): 76-81.
16	陈义成. 航空发动机模型修正技术研究[D]. 天津: 中国民航大学, 2020.
	Chen Yicheng. Research on Modification Technology of Aeroengine Model[D]. Tianjin: Civil Aviation University of China, 2020.
17	朱正琛. 涡轴发动机模型修正及控制方法研究[D]. 南京: 南京航空航天大学, 2016.
	Zhu Zhengchen. Research on Turbo-shaft Engine Model Correction and Control Method[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2016.
18	Wang H, Tewarson R P. A Quasi-Gauss-Newton Method for Solving Non-Linear Algebraic Equations[J]. Computers&Mathematics with Applications(S0898-1221), 1993, 25(1): 53-63.
19	Kristensen Philip K, Emilio Martínez Pañeda. Phase Field Fracture Modelling Using Quasi-Newton Methods and a New Adaptive Step Scheme[J]. Theoretical and Applied Fracture Mechanics(S0167-8442), 2020, 107: 102446.

[1]	Zhang Yunyin, Liu Chunguang, Ma Xiaojun, Liao Zili. Real-time Simulation Research on Electric Drive System of Armored Vehicle [J]. Journal of System Simulation, 2017, 29(1): 107-114.
[2]	Jia Lishan, Wang Liwen. Construction of Real-time Simulation Model of Fire Fighting Pump System of Airport Fire Engine [J]. Journal of System Simulation, 2015, 27(6): 1209-1213.