基于自适应遗传算法的爆炸冲击响应谱时域重构优化方法

孙文娟 陈海波 黄颖青

孙文娟, 陈海波, 黄颖青. 基于自适应遗传算法的爆炸冲击响应谱时域重构优化方法[J]. 高压物理学报, 2019, 33(5): 052301. doi: 10.11858/gywlxb.20180681
引用本文: 孙文娟, 陈海波, 黄颖青. 基于自适应遗传算法的爆炸冲击响应谱时域重构优化方法[J]. 高压物理学报, 2019, 33(5): 052301. doi: 10.11858/gywlxb.20180681
SUN Wenjuan, CHEN Haibo, HUANG Yingqing. Time Domain Reconstruction Optimization of Pyrotechnic Shock ResponseSpectrum via Adaptive Genetic Algorithm[J]. Chinese Journal of High Pressure Physics, 2019, 33(5): 052301. doi: 10.11858/gywlxb.20180681
Citation: SUN Wenjuan, CHEN Haibo, HUANG Yingqing. Time Domain Reconstruction Optimization of Pyrotechnic Shock ResponseSpectrum via Adaptive Genetic Algorithm[J]. Chinese Journal of High Pressure Physics, 2019, 33(5): 052301. doi: 10.11858/gywlxb.20180681

基于自适应遗传算法的爆炸冲击响应谱时域重构优化方法

doi: 10.11858/gywlxb.20180681
基金项目: 国家自然科学基金(11772322);军委装备发展部预研领域基金(6140246030216ZK01001);中国科学院战略性先导科技专项(B类)子课题(XDB22040502)
详细信息
    作者简介:

    孙文娟(1986-),女,博士研究生,讲师,主要从事爆炸冲击效应研究. E-mail: sunwenj@mail.ustc.edu.cn

    通讯作者:

    陈海波(1968-),男,博士,教授,主要从事计算力学及工程应用、振动工程研究. E-mail: hbchen@ustc.edu.cn

  • 中图分类号: V415.4

Time Domain Reconstruction Optimization of Pyrotechnic Shock ResponseSpectrum via Adaptive Genetic Algorithm

  • 摘要: 为解决现有爆炸冲击响应谱(Shock Response Spectrum,SRS)加速度重构方法依赖于大量试验数据的问题,对比了阻尼正弦与小波两种不同加速度重构方法在合成爆炸冲击响应谱时的性能。将对重构SRS质量的评估转化为与目标谱匹配度的最小值优化问题,并首次将自适应遗传算法(Adaptive Genetic Algorithm, AGA)应用于SRS重构的优化问题中。对比了交叉先行、变异先行和不定向3种不同的AGA在爆炸冲击响应谱时域重构优化中的性能,并与基本遗传算法(Genetic Algorithm, GA)进行对比。结果表明,AGA的优化结果比GA有较大幅度的改善,且不定向AGA所得结果是3种AGA方法中最好的,其SRS各频点数值均在(–3/+6)dB容差范围之内,与目标谱的匹配度更好。仿真对比算例验证了该方法在冲击响应谱的时域重构应用中具有较高的准确性和实用性,为进一步提高航天器结构在爆炸冲击载荷下响应的计算精度提供了支撑。

     

  • 图  冲击响应谱概念示意图

    Figure  1.  Graphical representation of the shock response spectrum

    图  阻尼正弦与小波合成SRS计算结果对比

    Figure  2.  Comparison results of damped sine and wavelet

    图  交叉概率和变异概率取值分析结果

    Figure  3.  Average value of Pc and Pm

    图  参数灵敏度分析前后远场SRS结果对比

    Figure  4.  Comparison results of empirical parameters and optimized parameters for far-field SRS

    图  参数灵敏度分析前后中场和近场SRS结果对比

    Figure  5.  Comparison results of empirical parameters and optimized parameters for mid-field and near-field SRS

    图  GA与AGA优化结果对比

    Figure  6.  Comparison results of GA and AGA

    图  加速度时间历程曲线

    Figure  7.  Acceleration time-history curves

    图  不定向AGA优化结果与文献[19]结果对比

    Figure  8.  Comparison results of uncertain-order AGA and Ref. [19]

    图  不同种群数目下中场目标SRS结果对比

    Figure  9.  Comparison results of mid-field SRS under different population numbers

    表  1  决策变量的取值范围

    Table  1.   Variation ranges of the decision variables

    Optimization variableVariation range
    Am(1/4 to 1/3)A0 (g)
    tdm[0.0001, 0.015] (s)
    ${\xi _{{m} } }$[0.001, 0.1]
    Nm[5, 27] (odd number)
    下载: 导出CSV

    表  2  典型爆炸冲击响应谱规范

    Table  2.   Specification of SRS

    Far fieldMedium fieldNear field
    Frequency/HzAmplitude/gFrequency/HzAmplitude/gFrequency/HzAmplitude/g
    100 80 100 150 200 250
    450 600 300 200 1 0004 000
    9001 000 1 5003 000 1 2005 000
    10 0001 00010 0003 000 10 0005 000
    下载: 导出CSV

    表  3  阻尼正弦与小波合成SRS计算结果对比

    Table  3.   Comparison results of damped sine and wavelet

    ParameterFar fieldMedium fieldNear field
    Damped sineWaveletDamped sineWaveletDamped sineWavelet
    Objective function value/g 44.2294.9103.7890.1155.51491
    Time/s142.2142.1139.7139.2 81.9 80.2
    下载: 导出CSV

    表  4  AGA选用参数

    Table  4.   Parameters of AGA

    ParameterValueParameterValueParameterValue
    Population40Pm10.1 ${{P_{{\rm{c}}0}}}$0.75
    Maximum evolutionary generation200Pm20.05 ${P_{\rm{m}}^{{\rm{LB}}}}$0.01
    Pc10.9Pm30.005${P_{\rm{m}}^{{\rm{UB}}}}$0.1
    Pc20.5${P_{\rm{c}}^{{\rm{LB}}}}$0.5 ${{P_{{\rm{m}}0}}}$0.05
    Pc30.1${P_{\rm{c}}^{{\rm{UB}}}}$0.9
    下载: 导出CSV

    表  5  GA与AGA优化结果对比

    Table  5.   Comparison results of GA and AGA (OFV: objective function value)

    AlgorithmFar fieldMedium fieldNear field
    OFV/gCurrent generationTotal time/sOFV/gCurrent generationTotal time/sOFV/gCurrent generationTotal Time/s
    GA44.20200142.2103.7200139.7155.5200 81.9
    Crossover first AGA44.05115147.1102.9102135.1156.8127 82.9
    Mutation first AGA44.29128139.8103.9137136.1155.4158 83.6
    Uncertain-order AGA44.2589172.1103.187143.0155.398102.2
    下载: 导出CSV

    表  6  不同种群数目下中场目标SRS优化值与计算时间对比

    Table  6.   Comparison of optimization values and calculation time for mid-field SRS under different population numbers

    PopulationFinal optimization value/gCalculation time of
    200 generations/s
    2077.94 53.34
    4048.48139.70
    6044.53148.80
    8037.93159.70
    10027.88221.80
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-09
  • 修回日期:  2018-11-27

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