Research on Modeling, Optimization and Application of Aeroengine Oil System

doi:10.16182/j.issn1004731x.joss.23-1599

Abstract

Abstract:

In response to the high cost and long cycle of using experimental methods for monitoring, diagnosing, and predicting lubricating oil system, a simulation model for oil system is constructed and optimized, and the application of the model in health management of oil system is proposed. Based on the physical characteristics of the components in the oil system, subsystem models for ventilation, oil supply, thermodynamics, and oil return are constructed using a certain engine oil system as an example, and the whole oil system model is constructed and solved iteratively. The model is optimized by combining particle swarm optimization and genetic algorithm. The comparative optimization results show that the particle swarm algorithm has good convergence and optimization effect. The average error of the working parameters of the oil system under different typical working conditions is reduced from more than 10% to about 2%, indicating a certain degree of accuracy. Based on the scalability of the model and combined with the requirements of health management in the oil system, the application of the model in oil monitoring, diagnosis, prediction, and other aspects is analyzed to provide support for the health management of the oil system.

Key words: aeroengine, oil system, particle swarm optimization, genetic algorithm, health management

CLC Number:

V233.4

Huang Shijie, Zhang Zhensheng, Cai Jing, Zhang Rui. Research on Modeling, Optimization and Application of Aeroengine Oil System[J]. Journal of System Simulation, 2025, 37(5): 1266-1279.

Figures/Tables 11

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 1

Fig. 7

Table 2

Table 3

Fig. 8

References 25

1	李国权. 航空发动机滑油系统的现状及未来发展[J]. 航空发动机, 2011, 37(6): 49-52, 62.
	Li Guoquan. Present and Future of Aeroengin Oil System[J]. Aeroengine, 2011, 37(6): 49-52, 62.
2	吴大观. 涡轮风扇发动机及其系统的性能研究[M]. 北京: 国防工业出版社, 1986.
3	Cheng A K. SSME Alternate Turbopumps Axial Thrust Balance and Secondary Flow Models: 322-002-91-153-R01[R]. [S.l. : s.n.], 1992.
4	Majumdar A K, LeClair A C, Moore C, et al. Generalized Fluid System Simulation Program (GFSSP) - Version 6[C]//51st AIAA/SAE/ASEE Joint Propulsion Conference. Reston: AIAA, 2015: AIAA 2015-3850.
5	杨家旺, 姜会庆, 周琳. 航空发动机滑油供油系统建模及应用[J]. 工业技术创新, 2019, 6(3): 80-85.
	Yang Jiawang, Jiang Huiqing, Zhou Lin. Modeling and Application of Lubricating Oil Supply System for Aeroengines[J]. Industrial Technology Innovation, 2019, 6(3): 80-85.
6	白杰, 朱永新, 何文博, 等. 基于AMESim的某型航空发动机滑油供油系统故障模拟[J]. 科学技术与工程, 2020, 20(9): 3784-3789.
	Bai Jie, Zhu Yongxin, He Wenbo, et al. Fault Simulation of a Certain Type of Aeroengine Lubricating Oil Supply System Based on AMESim[J]. Science Technology and Engineering, 2020, 20(9): 3784-3789.
7	朱永新. 基于改进支持向量机的航空发动机滑油系统故障诊断研究[D]. 天津: 中国民航大学, 2020.
	Zhu Yongxin. Research on Fault Diagnosis of Aero-engine Oil System Based on Improved Support Vector Machine[D]. Tianjin: Civil Aviation University of China, 2020.
8	路彬, 刘振侠, 吕亚国, 等. 航空发动机滑油通风系统性能计算仿真[J]. 航空计算技术, 2011, 41(4): 32-35.
	Lu Bin, Liu Zhenxia, Yaguo Lü, et al. Performance Calculation Simulation of Aeroengine Lubrication Vent System[J]. Aeronautical Computing Technique, 2011, 41(4): 32-35.
9	方露露. 航空发动机轴承腔及回油管路两相流动换热规律研究[D]. 南京: 南京航空航天大学, 2020.
	Fang Lulu. Study on the Oil-gas Two-phase Flow and Heat Transfer in an Aero-engine Bearing Chamber and the Scavenge Pipes[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020.
10	汪元林. 航空发动机滑油系统温升研究[D]. 南京: 南京航空航天大学, 2016.
	Wang Yuanlin. Research on Temperature Addition of Aeroengine Oil System[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2016.
11	马明明. 基于试飞数据的航空发动机滑油系统模型建立及应用[J]. 润滑与密封, 2017, 42(10): 121-126.
	Ma Mingming. Establishment and Application of Aero-engine Oil System Model Based on Flight Test Data[J]. Lubrication Engineering, 2017, 42(10): 121-126.
12	刘波, 周强, 程礼, 等. 构建航空发动机滑油系统稳态模型[J]. 推进技术, 2005, 26(6): 556-559.
	Liu Bo, Zhou Qiang, Cheng Li, et al. Construction of Steady Model for an Engine Oil System[J]. Journal of Propulsion Technology, 2005, 26(6): 556-559.
13	张效伟. 涡扇发动机润滑系统性能计算与分析[D]. 西安: 西北工业大学, 2006.
14	闫星辉, 郭迎清, 殷锴, 等. 基于MATLAB/Simulink的滑油系统建模仿真与优化[J]. 航空动力学报, 2017, 32(3): 740-748.
	Yan Xinghui, Guo Yingqing, Yin Kai, et al. Modeling Simulation and Optimization of Oil System Based on MATLAB/Simulink[J]. Journal of Aerospace Power, 2017, 32(3): 740-748.
15	张书刚, 郭迎清, 陆军. 基于GasTurb/MATLAB的航空发动机部件级模型研究[J]. 航空动力学报, 2012, 27(12): 2850-2856.
	Zhang Shugang, Guo Yingqing, Lu Jun. Research on Aircraft Engine Component-level Models Based on GasTurb/MATLAB[J]. Journal of Aerospace Power, 2012, 27(12): 2850-2856.
16	«航空发动机设计手册»总编委会. 航空发动机设计手册: 第12册传动及润滑系统[M]. 北京: 航空工业出版社, 2002.
	Editorial Board of Aeroengine Design Manual. Aeroengine Design Manual: Volume 12 Transmission and Lubrication System[M]. Beijing: Aviation Industry Press, 2002.
17	赵然, 高红霞, 李明. 直升机滑油系统流动阻力特性研究[J]. 航空制造技术, 2013(12): 89-94.
	Zhao Ran, Gao Hongxia, Li Ming. Study of Flow Resistance Characteristics for Helicopter Oil System[J]. Aeronautical Manufacturing Technology, 2013(12): 89-94.
18	吕亚国, 刘振侠. 航空发动机管壳式燃-滑油散热器换热特性计算[J]. 航空动力学报, 2014, 29(12): 2830-2835.
	Yaguo Lü, Liu Zhenxia. Heat Transfer Characteristics Calculation for Aero-engine Shell-tube Fuel-oil Heat Exchanger[J]. Journal of Aerospace Power, 2014, 29(12): 2830-2835.
19	闫星辉. 民用涡扇发动机滑油系统建模仿真与故障诊断[D]. 西安: 西北工业大学, 2016.
	Yan Xinghui. Modeling, Simulation and Fault Diagnosis of Civil Turbofan Engine Oil System[D]. Xi'an: Northwestern Polytechnical University, 2016.
20	Zhang Mengyang, Zhang Xuyinglong, Gao Shan, et al. Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)[J]. Applied Sciences, 2023, 13(15): 9038.
21	Shi Yuhui, Eberhart Russell C. Parameter Selection in Particle Swarm Optimization[C]//Evolutionary Program-ming VII. Berlin: Springer Berlin Heidelberg, 1998: 591-600.
22	Qu Meijiao, Li Mengqi, Song Yuheng, et al. Multi-objective Optimization Design Method for Whole-aeroengine Coupling Vibration[J]. Aerospace, 2023, 10(2): 99.
23	Volponi A J. Gas Turbine Engine Health Management: Past, Present, and Future Trends[J]. Journal of Engineering for Gas Turbines and Power, 2014, 136(5): 051201.
24	曹明, 黄金泉, 周健, 等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅰ:气路、机械和FADEC系统故障诊断与预测[J]. 航空学报, 2022, 43(9): 封2, 1-33.
	Cao Ming, Huang Jinquan, Zhou Jian, et al. Current Status, Challenges and Opportunities of Civil Aero-engine Diagnostics & Health Management Ⅰ: Diagnosis and Prognosis of Engine Gas Path, Mechanical and FADEC[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 封2, 1-33.
25	尉询楷, 杨立, 刘芳, 等. 航空发动机预测与健康管理[M]. 北京: 国防工业出版社, 2014.
	Wei Xunkai, Yang Li, Liu Fang, et al. Aeroengine Prognostics and Health Management[M]. Beijing: National Defense Industry Press, 2014.

滑油系统工作参数	标准起飞工况			最大巡航工况
滑油系统工作参数	Simulink 计算结果	Flowmaster 基准结果	相对误差/%	Simulink 计算结果	Flowmaster 基准结果	相对误差/%
平均绝对误差/%	10.94			17.40
前轴承腔压力/kPa	147.85	147.3	0.37	50.10	49.0	2.24
中轴承腔压力/kPa	180.70	180.3	0.22	65.95	68.4	-3.58
后轴承腔压力/kPa	177.85	177.3	0.31	64.15	66.8	-3.97
供油泵后压力/MPa	0.56	0.55	1.82	0.43	0.42	2.38
前轴承腔供油量/(L/min)	24.75	24.3	1.85	23.75	23.5	1.06
中轴承腔供油量/(L/min)	3.26	3.1	5.16	3.22	2.9	11.03
后轴承腔供油量/(L/min)	2.62	2.8	-6.43	2.58	2.7	-4.44
齿轮箱供油量/(L/min)	17.86	17.2	3.84	17.14	16.6	3.25
滑油总供油量/(L/min)	48.49	48.5	-0.02	46.69	46.4	0.63
主供油路温度/℃	75.13	106.3	-29.32	74.62	133.7	-44.19
主回油路温度/℃	103.01	134.4	-23.36	100.55	157.1	-36.00
前轴承腔回油温度/℃	107.12	135.0	-20.65	103.95	161.2	-35.51
中轴承腔回油温度/℃	106.67	138.5	-22.98	101.74	159.4	-36.17
后轴承腔回油温度/℃	88.75	118.9	-25.36	88.63	145.2	-38.96
齿轮箱回油温度/℃	98.73	127.2	-22.38	97.39	155.8	-37.49

滑油系统工作参数	标准起飞工况					最大巡航工况
滑油系统工作参数	Flowmaster基准结果	PSO 优化	相对误差/%	GA 优化	相对误差/%	Flowmaster基准结果	PSO 优化	相对误差/%	GA 优化	相对误差/%
平均绝对误差/%			2.11		2.02			1.98		2.79
前轴承腔压力/kPa	147.3	147.85	0.37	147.85	0.37	49.0	50.10	2.24	50.10	2.24
中轴承腔压力/kPa	180.3	180.70	0.22	180.70	0.22	68.4	65.95	-3.58	65.95	-3.58
后轴承腔压力/kPa	177.3	177.85	0.31	177.85	0.31	66.8	64.15	-3.97	64.15	-3.97
供油泵后压力/kPa	0.55	0.56	1.82	0.56	1.82	0.42	0.43	2.38	0.43	2.38
前轴承腔供油量/(L/min)	24.3	24.75	1.85	24.75	1.85	23.5	23.75	1.06	23.75	1.06
中轴承腔供油量/(L/min)	3.1	3.26	5.16	3.26	5.16	2.9	3.22	11.03	3.22	11.03
后轴承腔供油量/(L/min)	2.8	2.62	-6.43	2.62	-6.43	2.7	2.58	-4.44	2.58	-4.44
齿轮箱供油量/(L/min)	17.2	17.86	3.84	17.86	3.84	16.6	17.14	3.25	17.14	3.25
滑油总供油量/(L/min)	48.5	48.49	-0.02	48.49	-0.02	46.4	46.69	0.63	46.69	0.63
主供油路温度/℃	106.3	105.94	-0.34	106.43	0.12	133.7	130.64	-2.29	133.43	-0.20
主回油路温度/℃	134.4	130.39	-2.98	132.46	-1.44	157.1	157.11	0.01	156.37	-0.46
前轴承腔回油温度/℃	135.0	131.31	-2.73	135.97	0.72	161.2	161.01	-0.12	158.95	-1.40
中轴承腔回油温度/℃	138.5	137.56	-0.68	146.29	5.62	159.4	161.99	1.62	156.8	-1.63
后轴承腔回油温度/℃	118.9	123.17	3.59	121.31	2.03	145.2	144.65	-0.38	151.23	4.15
齿轮箱回油温度/℃	127.2	128.87	1.31	126.71	-0.39	155.8	152.66	-2.02	153.49	-1.48

序号	需求内容	重要度
1	具备滑油系统监视功能	必须
2	EHM应具有滑油系统工作参数、滑油金属碎屑数据的测量和记录功能	必须
3	通过监视滑油温度、压力、消耗水平、滑油杂质等，监测滑油系统被润滑部件的健康状况，包括磨损引起的发动机中央传动齿轮、附件机匣传动齿轮、主轴承故障等	必须
4	不能因单一传感器故障或漂移导致虚监测	必须
5	应在相关技术文件中明确基于滑油系统信号分析的故障预测判断标准和逻辑	需要
6	能够对滑油系统进行监测和记录，当参数处理后达到判断准则设定值，将该事件对应的时刻记录到EHM报表中；参数处理分为全状态和平稳飞行状态两种	必须
7	对后轴承腔故障、滑油泄露、轴承故障、调压活门等滑油系统故障进行记录和反馈	必须
8	对滑油系统后腔回油温度、后腔封严压差、供油压差、滑油消耗率、在线屑末等参数进行趋势分析	必须

[1]	Wu Zisong, Chang Daofang, Gai Yuchun. Optimization of Cargo Location Allocation in Four-way Shuttle Warehousing System Based on Two-stage Hybrid Algorithm [J]. Journal of System Simulation, 2025, 37(5): 1234-1245.
[2]	Shi Xiaodong, Guo Yongcheng, Ma Mingqi, Pan Jiarui. Optimization of Vehicle Routing for Cross-infection Risk in the Epidemic [J]. Journal of System Simulation, 2025, 37(4): 910-921.
[3]	Xu Qiang, Xu Jianlei, Hu Yanhai, Chen Haihui, Zhang Xing, Xing Zhaohui. Trajectory Optimization of Robotic Arm Based on Improved Simulated Annealing Genetic Algorithm [J]. Journal of System Simulation, 2025, 37(2): 404-412.
[4]	Ma Huawei, Yan Boying. Vehicle Routing Problem with Drones Considering Zoned Distribution of Epidemic Prevention Materials [J]. Journal of System Simulation, 2025, 37(1): 234-244.
[5]	Huang Qiushi, Wang Yanyang, Wu Changliang, Huang Junfu, Zhang Shenggen, Luo Haoxuan. Cooperative Control Method of Mixed Traffic at Signalized Intersection [J]. Journal of System Simulation, 2025, 37(1): 271-283.
[6]	Zhang Wei, Jiang Yuefeng. Adaptive Particle Swarm Optimization Algorithm Based on Trap Label and Lazy Ant [J]. Journal of System Simulation, 2024, 36(7): 1631-1642.
[7]	Xiao Peng, Xie Feng, Ni Haihong, Zhang Min, Tang Zhili, Li Ni. Research on Collaborative Optimization Method of Multi-UAV Task Allocation and Path Planning [J]. Journal of System Simulation, 2024, 36(5): 1141-1151.
[8]	Tang Jinjun, Hu Lipeng, Li Mingyang, Zhang Xuan. Optimization of Highway Emergency Lane Control Based on Kriging Genetic Algorithm [J]. Journal of System Simulation, 2024, 36(5): 1165-1178.
[9]	Wan Yuanpeng, Liang Chengji, Wang Sihong, Wang Yu. Joint Distribution-Inventory Optimization and Simulation for Cold Chain Logistics Considering Order Substitution [J]. Journal of System Simulation, 2024, 36(3): 578-594.
[10]	Yan Shiliang, Wang Yinling, Lu Dandan, Pan Xiaoqin. Simulation and Optimization of Permanent Magnet Linear Machine Based on Deep Neural Network [J]. Journal of System Simulation, 2024, 36(3): 713-725.
[11]	Wei Xiang, Liu Xingxuan, Fu Dianzheng, Yang Tianji, Yang Jiaxuan. Platform Path Optimization Method Based on Cumulative Detection Probability of Sonar Search [J]. Journal of System Simulation, 2024, 36(11): 2674-2683.
[12]	Chen Jiajun, Tan Dailun. Multi-strategy Partheno-genetic Algorithm Based on Dynamic Reduction Mechanism for Solving CVRP Problem [J]. Journal of System Simulation, 2024, 36(10): 2396-2412.
[13]	Zeng Qiuwei, Hu Zhaoyong, Wang Zhile, Zhang Ruilin, Zou Gang. Driving Method of Virtual Multi-person Disassembly and Assembly Task for Aeroengine [J]. Journal of System Simulation, 2024, 36(1): 220-231.
[14]	Zhang Hongli, Deng Jingshuang. Research on Artificial Population Generation and Application Based on Genetic Algorithm [J]. Journal of System Simulation, 2023, 35(9): 1965-1974.
[15]	Yuwen Wu, Zhiyue Niu, Zhenping Li. Picking Path Planning of Container Robots Based on Improved Genetic Algorithm [J]. Journal of System Simulation, 2023, 35(5): 1086-1097.