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基于神经网络的复杂前缘飞行器FADS系统冗余设计

周印佳 万千 徐艺哲 齐玢 石泳

周印佳,万千,徐艺哲,等. 基于神经网络的复杂前缘飞行器FADS系统冗余设计[J]. 北京航空航天大学学报,2024,50(3):757-764 doi: 10.13700/j.bh.1001-5965.2022.0341
引用本文: 周印佳,万千,徐艺哲,等. 基于神经网络的复杂前缘飞行器FADS系统冗余设计[J]. 北京航空航天大学学报,2024,50(3):757-764 doi: 10.13700/j.bh.1001-5965.2022.0341
ZHOU Y J,WAN Q,XU Y Z,et al. Redundancy design of a FADS system on a complex leading-edge vehicle using neural network approach[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):757-764 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0341
Citation: ZHOU Y J,WAN Q,XU Y Z,et al. Redundancy design of a FADS system on a complex leading-edge vehicle using neural network approach[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):757-764 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0341

基于神经网络的复杂前缘飞行器FADS系统冗余设计

doi: 10.13700/j.bh.1001-5965.2022.0341
基金项目: 国家自然科学基金(11902026); 航天进入减速与着陆技术实验室开放基金(EDL19092115)
详细信息
    通讯作者:

    E-mail:zhouyinjia@126.com

  • 中图分类号: V221.7

Redundancy design of a FADS system on a complex leading-edge vehicle using neural network approach

Funds: National Natural Science Foundation of China (11902026); Open Fund for Aerospace Entry Deceleration and Landing Technology Laboratory ( EDL19092115)
More Information
  • 摘要:

    嵌入式大气数据传感(FADS)系统基于飞行器表面压力测量解算迎角、侧滑角、马赫数、来流动压与静压等飞行参数,能够有效解决探出机体的空速管前缘无法适应高超声速飞行器在巡航阶段所面临的严酷气动加热问题,同时满足飞行器对隐身性能的需求。目前,关于神经网络方法及FADS系统用于复杂型面前缘飞行器的分析和研究工作较少。针对自主返回的高超声速飞行器在着陆阶段的亚/跨声速条件,考虑薄前缘和进气道部件等影响开展复杂前缘飞行器的头部FADS系统冗余设计和验证。在复杂前缘飞行器头部开设15个测压孔,通过大量精细化数值仿真建立飞行器在不同来流条件下的压力数据库,并利用风洞试验对典型工况进行验证。针对复杂型面前缘飞行器,基于压力数据建立4套神经网络算法并开展冗余设计研究,包括1套9孔算法与3套冗余算法。其中,9孔算法的精度较高,对迎角的解算误差在0.07°以内,对侧滑角的解算误差在0.3°以内,对马赫数的解算误差在0.0012以内,对来流动压与静压的解算相对误差均在1.5%以内。此外,建立具有一定容错性的系统解算流程,在任意单个测压孔失效的情况下能够继续保持来流参数的有效输出。

     

  • 图 1  类HTV-3X复杂前缘

    Figure 1.  Complex leading edge of HTV-3X

    图 2  仿真模型

    Figure 2.  Simulation model

    图 3  FADS系统测压孔布局

    Figure 3.  FADS system pressure orifice configuration

    图 4  仿真与试验数据对比

    Figure 4.  Comparison between simulated and experimental data

    图 5  不同算法的测压孔配置

    Figure 5.  Configuration of pressure orifices for different algorithms

    图 6  飞行参数解算误差对比

    Figure 6.  Comparison of the airdata estimation error

    图 7  FADS系统解算流程

    Figure 7.  Solving procedure of FADS

    表  1  神经网络模型训练数据库

    Table  1.   Database for neural network training

    迎角/(°) 侧滑角/(°) 高度/km 马赫数
    −10, −8, −6, −2, 0, 1, 2, 3, 4, 5, 6, 7,
    8, 9, 10, 11, 13, 15, 17, 20
    −15, −13, −11, −9, −7, −5, −3, −2, −1,
    0, 1, 2, 3, 5, 7, 9, 11, 13, 15
    0, 2, 4, 6, 8, 10 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6,
    0.65, 0.7, 0.75, 0.8, 0.85, 0.9
    下载: 导出CSV

    表  2  各算法最大误差

    Table  2.   Maximum estimation error of each algorithm

    算法迎角/(°)侧滑角/(°)马赫数动压/%静压/%
    冗余算法10.310.940.00724.95.5
    冗余算法20.220.860.0143.35.4
    冗余算法30.172.200.0134.57.4
    9孔算法0.0680.290.00131.31.4
    下载: 导出CSV

    表  3  各算法99%数据误差包络

    Table  3.   The 99% data error envelope for each algorithm

    算法迎角/(°)侧滑角/(°)马赫数动压/%静压/%
    冗余算法10.170.730.00653.94.6
    冗余算法20.130.650.00813.03.3
    冗余算法30.140.720.00843.34.2
    9孔算法0.0600.110.00120.910.77
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-10
  • 录用日期:  2022-08-15
  • 网络出版日期:  2022-08-23
  • 整期出版日期:  2024-03-27

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