系统仿真学报 ›› 2025, Vol. 37 ›› Issue (5): 1266-1279.doi: 10.16182/j.issn1004731x.joss.23-1599

• 研究论文 • 上一篇    下一篇

航空发动机滑油系统建模、优化及应用研究

黄世杰1, 张振生1, 蔡景1, 张瑞2   

  1. 1.南京航空航天大学 民航学院,江苏 南京 211106
    2.中国航发沈阳发动机研究所,辽宁 沈阳 110015
  • 收稿日期:2023-12-29 修回日期:2024-02-04 出版日期:2025-05-20 发布日期:2025-05-23
  • 通讯作者: 蔡景
  • 第一作者简介:黄世杰(1998-),男,硕士生,研究方向为测试性与健康管理。

Research on Modeling, Optimization and Application of Aeroengine Oil System

Huang Shijie1, Zhang Zhensheng1, Cai Jing1, Zhang Rui2   

  1. 1.Nanjing University of Aeronautics and Astronautics Civil aviation college, Nanjing 211106, China
    2.AECC Shenyang Engine Research Institute, Shenyang 110015, China
  • Received:2023-12-29 Revised:2024-02-04 Online:2025-05-20 Published:2025-05-23
  • Contact: Cai Jing

摘要:

针对基于试验方法开展滑油系统监测、诊断、预测等工作具备成本高、周期长等缺点,开展滑油系统仿真模型构建与优化并提出了模型在滑油系统健康管理中的应用。基于滑油系统元件物理特性,以某发动机滑油系统为例构建通风、供油、热力、回油等子系统模型,并开展了滑油全系统模型构建与迭代求解;结合粒子群、遗传算法对模型进行优化,对比优化结果表明粒子群算法具有较好的收敛性与优化效果,不同典型工况下滑油系统工作参数平均误差从10%以上降至2%左右,具有一定的准确性;基于模型的可扩展性,结合滑油系统健康管理要求分析模型在滑油监测、诊断、预测等方面的应用,为滑油系统的健康管理提供支撑。

关键词: 航空发动机, 滑油系统, 粒子群优化, 遗传算法, 健康管理

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

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