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飞机燃油系统全飞行剖面热边界模拟与温度预测

王瑞卿 李栋 李运华 王曦

王瑞卿, 李栋, 李运华, 等 . 飞机燃油系统全飞行剖面热边界模拟与温度预测[J]. 北京航空航天大学学报, 2022, 48(3): 369-375. doi: 10.13700/j.bh.1001-5965.2020.0555
引用本文: 王瑞卿, 李栋, 李运华, 等 . 飞机燃油系统全飞行剖面热边界模拟与温度预测[J]. 北京航空航天大学学报, 2022, 48(3): 369-375. doi: 10.13700/j.bh.1001-5965.2020.0555
WANG Ruiqing, LI Dong, LI Yunhua, et al. Thermal boundary simulation and temperature prediction for aircraft fuel system with full flight profile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 369-375. doi: 10.13700/j.bh.1001-5965.2020.0555(in Chinese)
Citation: WANG Ruiqing, LI Dong, LI Yunhua, et al. Thermal boundary simulation and temperature prediction for aircraft fuel system with full flight profile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 369-375. doi: 10.13700/j.bh.1001-5965.2020.0555(in Chinese)

飞机燃油系统全飞行剖面热边界模拟与温度预测

doi: 10.13700/j.bh.1001-5965.2020.0555
基金项目: 

国家科技重大专项 2017-V-0015-0067

详细信息
    通讯作者:

    李运华, E-mail: yhli@buaa.edu.cn

  • 中图分类号: V245.3;TK39

Thermal boundary simulation and temperature prediction for aircraft fuel system with full flight profile

Funds: 

National Science and Technology Major Project 2017-V-0015-0067

More Information
  • 摘要:

    通过仿真实验和机器学习,对影响飞机燃油系统温度的主要因素进行了研究,并对燃油系统温度进行了预测。对飞机燃油系统的基本结构布局进行了描述。利用Simulink仿真平台建立了燃油系统热动态仿真,该模型可以模拟出全飞行剖面下燃油回路各个节点的温度,通过改变不同的条件得到影响燃油系统各个节点温度的主要影响因素,并通过机器学习模型对燃油系统的温度进行预测。研究成果可以估计和感知燃油系统的工作温度及飞机液压、滑油等系统的工作温度,为进一步进行燃油液压系统的热边界感知和机载液压与机电系统热载荷吸收控制打下基础。

     

  • 图 1  燃油系统工作原理

    Figure 1.  Operation principle of fuel system

    图 2  全飞行剖面飞机飞行高度和马赫数随时间变化

    Figure 2.  Flight altitude and Mach number changing with time for full flight profile aircraft

    图 3  Simulink仿真模型示意图

    Figure 3.  Schematic diagram of Simulink simulation model

    图 4  燃油系统各节点温度

    Figure 4.  Temperature at each node of fuel system

    图 5  燃油系统温度随燃油系统流量变化

    Figure 5.  Fuel system temperature changing with fuel system flow

    图 6  LSTM架构

    Figure 6.  LSTM architecture

    图 7  LSTM模型特征贡献度分析

    Figure 7.  Analysis of feature contribution degree of LSTM model

    图 8  不同数据预测效果

    Figure 8.  Different data prediction effects

    图 9  LSTM模型细胞个数对预测效果影响

    Figure 9.  Effect of the number of cells in LSTM model and its prediction result

    表  1  输入变量属性及编号

    Table  1.   Input variables attributes and numbers

    特征范畴 特征属性 编号
    环境信息 飞行高度 1
    飞行速度 2
    飞行状态 燃油箱内燃油质量 3~7
    燃油系统流量 8、9
    增压泵功率 10
    换热器功率 11
    历史温度 供油箱历史温度 12~14
    增压泵历史温度 15~17
    末级换热器(9号位置)历史温度 18~20
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-27
  • 录用日期:  2020-11-20
  • 网络出版日期:  2022-03-20

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