留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于粒子群算法的飞机燃油箱热参数反演

杜明杰 吕旭飞 魏锦洲 姚尚宏

杜明杰, 吕旭飞, 魏锦洲, 等. 基于粒子群算法的飞机燃油箱热参数反演[J]. 航空动力学报, 2023, 38(1):250-256 doi: 10.13224/j.cnki.jasp.20210360
引用本文: 杜明杰, 吕旭飞, 魏锦洲, 等. 基于粒子群算法的飞机燃油箱热参数反演[J]. 航空动力学报, 2023, 38(1):250-256 doi: 10.13224/j.cnki.jasp.20210360
DU Mingjie, LÜ Xufei, WEI Jinzhou, et al. Inversion of thermal parameters of aircraft fuel tank based on particle swarm optimization[J]. Journal of Aerospace Power, 2023, 38(1):250-256 doi: 10.13224/j.cnki.jasp.20210360
Citation: DU Mingjie, LÜ Xufei, WEI Jinzhou, et al. Inversion of thermal parameters of aircraft fuel tank based on particle swarm optimization[J]. Journal of Aerospace Power, 2023, 38(1):250-256 doi: 10.13224/j.cnki.jasp.20210360

基于粒子群算法的飞机燃油箱热参数反演

doi: 10.13224/j.cnki.jasp.20210360
详细信息
    作者简介:

    杜明杰(1992-),男,工程师,硕士生,主要从事燃油系统试飞方面的研究

  • 中图分类号: V217

Inversion of thermal parameters of aircraft fuel tank based on particle swarm optimization

  • 摘要:

    为获取用于定量评估可燃性的飞机燃油箱热参数,从集中参数法建立燃油箱热模型的假设出发,基于粒子群算法和飞行试验数据,对某型飞机中央油箱热参数的反演进行了探索。选取了4种不同的参数作为目标函数,对比研究了目标函数的选取对热参数反演结果的影响。研究结果显示:反演得出的燃油箱热参数模型,其输出值与试验值变化规律一致,证明了该方法的有效性;其次以整体均方差为目标函数的反演结果与试验值最为吻合,模型输出值与试验值最大偏差为2.62 K;最后对整体均方差增加惩罚项的措施能够使反演后热参数模型满足适航规章的要求。

     

  • 图 1  某型机油箱系统布置示意图

    Figure 1.  Layout schematic diagram of an aircraft fuel tank system

    图 2  热天短航程数据预处理

    Figure 2.  Data preprocessing for short flight in hot weather

    图 3  目标函数随迭代次数变化

    Figure 3.  Objective function changes with the iterations

    图 4  模型输出与试验燃油温度对比图

    Figure 4.  Comparison diagram of fuel temperature between model output and flight test

    表  1  飞行试验基本信息

    Table  1.   Essential information of flight tests

    序号地面气温/K巡航时间/min备注
    Flt-1306.928.5
    Flt-2299.91
    Flt-3305.527短航程
    Flt-4307.2180长航程
    Flt-5310.128
    Flt-6294.5180长航程
    Flt-7305.130
    Flt-8290.228短航程
    Flt-9287.41
    下载: 导出CSV

    表  2  平衡温差变量定义

    Table  2.   Definition of equilibrium temperature difference

    Hp/mΔT/K
    场高X(1)
    609.6X(2)
    3048X(3)
    7620X(4)
    9144X(5)
    HcrX(6)
    下载: 导出CSV

    表  3  热时间常数τ变量定义

    Table  3.   Definition of thermal time constant τ s

    Hp/mQfr/kg
    050010002500满油
    场高X(7)X(8)X(9)X(10)X(11)
    3048X(12)X(13)X(14)X(15)X(16)
    HcrX(17)X(18)X(19)X(20)X(21)
    下载: 导出CSV

    表  4  目标函数定义

    Table  4.   Definition of objective function

    序号参数名计算公式备注
    条件1最大偏差$J(\mathrm{\Delta }T,\tau )=\mathrm{m}\mathrm{a}\mathrm{x}\left(\left|{T}_{pq}-{T}_{ \mathrm{m}, pq}\right|\right)$
    条件2均方误差$J(\mathrm{\Delta }T,\tau )=\dfrac{1}{nN}\displaystyle\sum _{p=1}^{N}{\displaystyle\sum _{q=1}^{n}({T}_{pq}-{T}_\mathrm{m},{qp}) ^{2} }$
    条件3架次最大均方误差$J(\mathrm{\Delta }T,\tau )=\mathrm{m}\mathrm{a}\mathrm{x}\left[\dfrac{1}{n}{\displaystyle\sum _{p=1}^{n}({T}_{pq}-{T}_\mathrm{m},{pq}) ^{2} }\right]$
    条件4带惩罚项的
    均方误差
    $J(\mathrm{\Delta }T,\tau )=\dfrac{1}{nN}\displaystyle\sum _{p=1}^{N}{\displaystyle\sum _{q=1}^{n}\left[a\left({T}_{pq}-{T}_\mathrm{m},{pq}\right)\right]^{2} }$Tm, pqTpq <−1.1 K时,a取3;其余情况a取1。
    下载: 导出CSV

    表  5  平衡温差ΔT反演结果

    Table  5.   Inversion results of equilibrium temperature difference ΔT K

    序号Hp/m
    场高609.6304876209144Hcr
    条件19.672.276.9722.9122.137.95
    条件207.0210.9817.6021.1410.58
    条件35.302.4425.0620.6615.6215.29
    条件49.672.276.9722.9122.137.95
    下载: 导出CSV

    表  6  热时间常数τ反演结果

    Table  6.   Inversion results of thermal time constant τ s

    序号Hp/mQfr/kg
    050010002500满油
    条件1场高1442120000186008510
    3048129905310189501779013000
    Hcr14810860020000105602590
    条件2场高135531890520000556520000
    30481167011781120000144385891
    Hcr178191302420000271315178
    条件3场高166781432913519749912388
    304877651498917212144324058
    Hcr4773165320000695.615225
    条件4场高24131339820000385112687
    3048141241458020000328119373
    Hcr6676964320000435012419
    下载: 导出CSV

    表  7  模型输出值与试验结果对比分析

    Table  7.   Comparative analysis of model output values and test results

    序号统计参数Flt-1Flt-2Flt-3Flt-4Flt-5Flt-6Flt-7Flt-8Flt-9
    条件1最大偏差/K2.682.812.812.812.662.281.170.610.68
    架次最大均方差/K21.931.782.143.272.531.720.400.090.09
    均方差/K21.82
    tns/min32039623840000
    条件2最大偏差/K2.322.622.621.722.371.532.030.880.83
    架次最大均方差/K21.281.401.630.501.740.330.940.110.14
    均方差/K20.77
    tns/min170310260000
    条件3最大偏差/K2.212.902.523.112.222.212.070.970.72
    架次最大均方差/K21.161.441.441.441.391.101.440.330.17
    均方差/K21.17
    tns/min170280230000
    条件4最大偏差/K0.881.870.920.610.830.471.740.490.29
    架次最大均方差/K21.025.221.060.830.960.333.650.310.18
    均方差/K21.30
    tns/min005000000
    下载: 导出CSV
  • [1] 魏书有.飞机燃油箱可燃性暴露评估研究[D].天津: 中国民航大学,2013.

    WEI shuyou. Aircraft fuel tank flammability exposure evaluation research[D].Tianjin: Civil Aviation University of China,2013.(in Chinese)
    [2] Federal Aviation Administration. Reduction of fuel tank flammability in transport category airplane:Amendment Nos. 25-125[S].Washington DC: Federal Register,2008:14-15.
    [3] Aviation Rulemaking Advisory Committee.Fuel tank harmonization working group final report[R].Washington DC: Aviation Rulemaking Advisory Committee,1998.
    [4] Federal Aviation Administration. A review of the flammability hazard of jet a fuel vapor in civil transport aircraft fuel tanks[R].DOT/FAA/AR-98/26,1998.
    [5] SUMMER S M .Mass loading effects on fuel vapor concentration in an aircraft fuel tank ullage[R].DOT/FAA/AR-99/65,1999.
    [6] SUMMER S M.Ground and flight testing of Boeing 737 center wing fuel tank inerted with nitro-gen-enriched air[R].DOT/FAA/AR-01/63,2001.
    [7] CAVAGE W M,SUMMER S.A study of flammability of commercial transport airplane wing fuel tanks[R].DOT/FAA/AR-08/8,2008
    [8] 中国民用航空局.运输类飞机适航标准:CCAR-25-R4[S].北京:中国民用航空局,2011:256.
    [9] 张瑞华,刘卫华,刘春阳,等. 运输类飞机燃油箱可燃性适航符合性方法[J]. 航空动力学报,2020,35(5): 1099-1108.

    ZHANG Ruihua,LIU Weihua,LIU Chunyang,et al. Flammability and airworthiness compliance method of fuel tank for transport aircraft[J]. Journal of Aerospace Power,2020,35(5): 1099-1108. (in Chinese)
    [10] 郭军亮,周宇穗,王澍,等. 飞机燃油箱可燃性定量分析的燃油箱热参数计算方法研究[J]. 民用飞机设计与研究,2011(3): 20-22. doi: 10.3969/j.issn.1674-9804.2011.03.008

    GUO Junliang,ZHOU Yusui,WANG Shu,et al. Study of fuel tank thermal data calculating method for aircraft fuel tank flammability quantitative analysis[J]. Civil Aircraft Design and Research,2011(3): 20-22. (in Chinese) doi: 10.3969/j.issn.1674-9804.2011.03.008
    [11] 郭军亮.民用飞机燃油箱热特性数值仿真[J].航空计算技术2013,43(1):65-68.

    GUO Junliang.Numerical simulation on fuel tank thermal characters for civil aircraft[J].Aeronautical Computing Technique,2013,43(1):65-68. (in Chinese)
    [12] 吕亚国,任国哲,刘振侠,等. 飞机燃油箱热分析研究[J]. 推进技术,2015,36(1): 61-67.

    LÜ Yaguo,REN Guozhe,LIU Zhenxia,et al. Thermal analysis of fuel tank for aircraft[J]. Journal of Propulsion Technology,2015,36(1): 61-67. (in Chinese)
    [13] 祖伟. 基于粒子群优化算法的水下潜器实时路径规划技术研究[D]. 哈尔滨: 哈尔滨工程大学,2008.

    ZU Wei. Real-time path planning system based on PSO for underwater vehicles[D]. Harbin: Harbin Engineering University,2008. (in Chinese)
    [14] 韩颜,许燕,周建平. 粒子群-蚁群融合算法的机器人路径规划[J]. 组合机床与自动化加工技术,2020, 62(2): 47-50.

    HAN Yan,XU Yan,ZHOU Jianping. A fusion algorism of particle swam and ant colony optimization for robot path planning[J]. Modular Machine Tool and Automatic Manufacturing Technique,2020, 62(2): 47-50. (in Chinese)
    [15] 刘坤,谭营,何新贵. 基于粒子群优化的过程神经网络学习算法[J]. 北京大学学报(自然科学版),2011,47(2): 238-244.

    LIU Kun,TAN Ying,HE Xingui. Particle swarm optimization based learning algorithm for process neural networks[J]. Acta Scientiarum Naturalium Universitatis Pekinensis,2011,47(2): 238-244. (in Chinese)
    [16] CHRISTOPHER K M,KEVIN D,SEPPIJ.Bayesian optimization models for particle swarms[R].New York, US: the Conference on Genetic and Evolutionary Computation,2005.
    [17] 王峰,周宜红,赵春菊,等. 基于混合粒子群算法的特高拱坝不同材料热学参数反演分析[J]. 清华大学学报(自然科学版),2021,61(7): 747-755.

    WANG Feng,ZHOU Yihong,ZHAO Chunju,et al. Thermal parameter inversion for various materials of super high arch dams based on the hybrid particle swarm optimization method[J]. Journal of Tsinghua University(Science and Technology),2021,61(7): 747-755. (in Chinese)
    [18] 王乐洋,靳锡波,许光煜. 断层参数反演的动态惯性因子的粒子群算法[J]. 武汉大学学报(信息科学版),2021,46(4): 510-519.

    WANG Leyang,JIN Xibo,XU Guangyu. Particle swarm optimization algorithm with dynamic inertia factors for inversion of fault parameters[J]. Geomatics and Information Science of Wuhan Universty,2021,46(4): 510-519. (in Chinese)
  • 加载中
图(4) / 表(7)
计量
  • 文章访问数:  142
  • HTML浏览量:  28
  • PDF量:  55
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-07-10
  • 网络出版日期:  2022-09-07

目录

    /

    返回文章
    返回