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基于雷诺数的大气阻力模型在飞行器再入预报中的应用

刘劲宏 徐劲 杜建丽 潘建平

刘劲宏, 徐劲, 杜建丽, 潘建平. 基于雷诺数的大气阻力模型在飞行器再入预报中的应用[J]. 空间科学学报, 2022, 42(2): 277-283. doi: 10.11728/cjss2022.02.210222020
引用本文: 刘劲宏, 徐劲, 杜建丽, 潘建平. 基于雷诺数的大气阻力模型在飞行器再入预报中的应用[J]. 空间科学学报, 2022, 42(2): 277-283. doi: 10.11728/cjss2022.02.210222020
LIU Jinghong, XU Jin, DU Jianli, PAN Jianping. Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 277-283. DOI: 10.11728/cjss2022.02.210222020
Citation: LIU Jinghong, XU Jin, DU Jianli, PAN Jianping. Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 277-283. DOI: 10.11728/cjss2022.02.210222020

基于雷诺数的大气阻力模型在飞行器再入预报中的应用

doi: 10.11728/cjss2022.02.210222020
基金项目: 国家自然科学基金项目(12003075),中国博士后科学基金项目(2021 M703487)和重庆交通大学人才引进项目(20 JDKJC-B018)共同资助
详细信息
    作者简介:

    刘劲宏:E-mail:liu-jh@whu.edu.cn

  • 中图分类号: P402

Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies

  • 摘要: 随着对空间技术服务需求的增加和空间碎片主动移除技术的实现,未来空间碎片将以数量多、质量大、难分解等特点频繁再入大气层,给地面人员和财产安全造成更多威胁。因此,亟需对火箭体等大型航天器的大气再入进行预警,然而因缺乏合适的大气阻力系数模型难以实现高精度的大气再入预报。为此,在简化航天器模型的基础上引入基于雷诺数的大气动力模型,通过RK6(7)对运动微分方程数值积分得到预报结果,并与高精度数值轨道传播器HPOP以及半解析轨道传播器WHU-SST的预报结果进行对比。实验表明:在运动微分方程中引入基于雷诺数的大气动力模型提前30天对火箭体进行大气再入预报,精度显著提升,某些目标的预报误差从96%下降至7.8%;仅使用TLE数据,将新模型用于地面风险评估能够使真实陨落位置位于预报的统计陨落位置中。

     

  • 图  1  火箭体40145和41027再入过程中的高度变化

    Figure  1.  Altitude changes with time during the objects of 40145 and 41027 reentry atmosphere

    图  2  不同纵横比的弹道系数随入射角度的变化

    Figure  2.  Variation of ballistic coefficient with incident angle for different aspect ratio

    图  3  入射角对火箭体再入预报的影响和入射角为12°时火箭体的预报误差百分比(红色实横线是预测误差为0的基线,即第30天结果,红色点线为预报误差±10%的范围线,品红点线为预报误差±20%的范围线)

    Figure  3.  Influence of the incident angle on the reentry prediction of the rocket body and the prediction error percentage of the rocket body when the incident angle is 12° (Red solid line is baseline with 0 prediction error, the red dotted line deviates ±10% from the baseline, and the magenta dotted line deviates ±20%)

    图  4  基于雷诺数的大气阻力系数模型的陨落预报位置分布(黑色三角形为残骸找到的真实位置,这里将其作为陨落位置;红色点为陨落预报位置)

    Figure  4.  Distributions of decayed locations predicted by new atmospheric drag coefficient model based on Reynolds number (Black triangle is for the real falling location and the red dot is for the falling location of prediction)

    表  1  5种长征系列火箭体的二级火箭尺寸和质量

    Table  1.   Size and mass of two-stage rockets of five kinds of Long March series rocket carriers

    目标类型起飞
    质量/t
    结构
    质量/t
    推进剂质量/t长度/m直径/m等效球体直径/m
    CZ-2C R/B 38.2 3.2 35 8.387 3.35 5.2071
    CZ-2D R/B 40.644 3.122 34.736 9.007 3.35 5.3324
    CZ-3B R/B 49.4 9.943 3.35 5.5110
    CZ-3C R/B 48.644 3.222 43.736 9.943 3.35 5.5110
    CZ-4B R/B 52.7 10.9 3.35 5.6825
    下载: 导出CSV

    表  2  8个火箭体轨道信息和预报误差

    Table  2.   Orbit information and prediction errors of 8 rocket bodies

    NORAD_ID目标类型倾角/
    (°)
    远地点/
    km
    近地点/
    km
    偏心率再入时间HPOP预报
    误差
    WHU-SST
    预报误差
    新模型预报
    误差
    34840 CZ-2C R/B 97.49 148 133 0.002326 2014-12-14 0.1838 0.1103 0.0464
    37942 CZ-2C R/B 97.06 128 115 0.001491 2014-03-19 0.4401 0.3047 0.1075
    38253 CZ-3B R/B 54.71 257 119 0.112676 2017-08-18 Fail 0.8361 –0.1591
    40120 CZ-4B R/B 98.12 153 137 0.002277 2017-05-27 –0.7333 Fail 0.0397
    40138 CZ-2D R/B 97.95 155 133 0.014247 2015-06-14 0.3437 0.1375 0.1610
    40145 CZ-4B R/B 97.47 164 132 0.016632 2014-10-23 0.552 0.4127 0.0068
    40550 CZ-3C R/B 54.54 390 86 0.389825 2016-03-23 0.3484 0.2091
    41027 CZ-4B R/B 97.35 155 127 0.014069 2015-12-26 0.9655 0.7586 0.0781
     Fail表示预报误差超过100%。
    下载: 导出CSV
  • [1] PARDINI C, ANSELMO L. Uncontrolled re-entries of spacecraft and rocket bodies: a statistical overview over the last decade[J]. Journal of Space Safety Engineering, 2019, 6(1): 30-47 doi: 10.1016/j.jsse.2019.02.001
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    [5] LI Bin. Researches on Key Technologies of Fast and Accurate Orbit Determination and Prediction of Space Debris[D]. Wuhan: Wuhan University, 2017
    [6] MOE M M, WALLACE S D, MOE K. Refinements in determining satellite drag coefficients: method for resolving density discrepancies[J]. Journal of Guidance, Control, and Dynamics, 1993, 16(5): 991 doi: 10.2514/3.21118
    [7] PARDINI C, ANSELMO L, MOE K, et al. Drag and energy accommodation coefficients during sunspot maximum[J]. Advances in Space Research, 2010, 45(5): 638-650 doi: 10.1016/j.asr.2009.08.034
    [8] MEHTA P M, LINARES R, WALKER A C. Photometric data from nonresolved objects for improved drag and reentry prediction[J]. Journal of Spacecraft and Rockets, 2018, 55(4): 959-970 doi: 10.2514/1.A33825
    [9] SCHAAF S A, CHAMBRE P L. Flow of Rarefied Gases[M]. Princeton: Princeton University Press, 2017
    [10] CAO Z, TAFTI D K. Investigation of drag, lift and torque for fluid flow past a low aspect ratio (1∶4) cylinder[J]. Computers & Fluids, 2018, 177: 123-135
    [11] PARDINI C, ANSELMO L. Assessing the risk and the uncertainty affecting the uncontrolled re-entry of manmade space objects[J]. Journal of Space Safety Engineering, 2018, 5(1): 46-62 doi: 10.1016/j.jsse.2018.01.003
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出版历程
  • 收稿日期:  2021-02-22
  • 录用日期:  2021-10-08
  • 修回日期:  2021-10-08
  • 网络出版日期:  2022-05-25

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