面向亚轨道解体事故的碎片扩散分布建模研究

doi:10.12305/j.issn.1001-506X.2022.09.27

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (9): 2922-2928.doi: 10.12305/j.issn.1001-506X.2022.09.27

面向亚轨道解体事故的碎片扩散分布建模研究

陈万通^1,^2,*, 田书雨², 张巨联³, 刘庆⁴, 任诗雨¹

1. 中国民航大学民航航班广域监视与安全管控技术重点实验室, 天津 300300
2. 中国民航大学电子信息与自动化学院, 天津 300300
3. 上海飞机设计研究院, 上海 201109
4. 上海航天电子技术研究所, 上海 201109

收稿日期:2021-09-07 出版日期:2022-09-01 发布日期:2022-09-09
通讯作者: 陈万通
作者简介:陈万通(1986—), 男, 副教授, 博士, 硕士研究生导师, 主要研究方向为航空安全、航班广域监视和卫星导航|田书雨(1994—), 男, 硕士研究生, 主要研究方向为航空安全、通信导航监视|张巨联(1984—), 女, 工程师, 硕士, 主要研究方向为航电系统适航研究|刘庆(1985—), 男, 高级工程师, 硕士, 主要研究方向为航天测控|任诗雨(1987—), 女, 讲师, 博士, 主要研究方向为航空安全
基金资助:
国家自然科学基金(61901477);天津市教委科研计划(2020KJ011);中国民航大学民航航班广域监视与安全管控技术重点实验室开放基金(202005)

Research on modeling of debris diffusion distribution for suborbital disintegration accident

Wantong CHEN^1,^2,*, Shuyu TIAN², Julian ZHANG³, Qing LIU⁴, Shiyu REN¹

1. Key Laboratory of Civil Aviation Flight Wide Area Surveillance and Safety Control Technology, Civil Aviation University of China, Tianjin 300300, China
2. School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
3. Shanghai Aircraft Design and Research Institute, Shanghai 201109, China
4. Shanghai Aerospace Electronics Technology Institute, Shanghai 201109, China

Received:2021-09-07 Online:2022-09-01 Published:2022-09-09
Contact: Wantong CHEN

摘要/Abstract

摘要：

针对未来愈发频繁的商业亚轨道发射活动, 为降低亚轨道突发解体事故给民航客机带来的运行风险, 提高空管监视系统对危险情况的应急响应能力, 利用协方差传播算法对亚轨道解体事故碎片落点进行快速预测。该算法围绕线性化的标称轨迹, 将碎片落点预测的随机过程建模为高斯-马尔可夫过程。同时, 通过概率密度函数构造高斯-马尔可夫过程在一定置信度下的概率椭球, 量化碎片落点在标称轨迹附近的位置。在此基础上, 参照航天器标准解体模型, 对影响碎片传播轨迹的弹道系数进行推导计算。仿真结果表明, 该方法可有效提高碎片危险区的预测准确度。

关键词: 亚轨道解体事故, 协方差传播算法, 标准解体模型, 弹道系数, 危险区预测

Abstract:

In response to the increasingly frequent commercial suborbital launch activities in the future, a covariance propagation algorithm is used to rapidly predict the debris drop points of suborbital disintegration accidents to reduce the operational risk of suborbital sudden disintegration accidents to civil airliners and to improve the emergency response capability of the air traffic control surveillance system to hazardous situations. The algorithm models the stochastic process of debris drop points prediction as a Gauss-Markov process around a linearized nominal trajectory. At the same time, the probability ellipsoid of the Gauss-Markov process at a certain confidence level is constructed by the probability density function to quantify the positions of the debris drop points in the vicinity of the nominal trajectory. On this basis, the ballistic coefficients affecting the debris propagation trajectory are derived and calculated concerning the standard disintegration model of spacecraft. Simulation results show that the method can effectively improve the prediction accuracy of the debris hazard area.

Key words: suborbital disintegration accident, covariance propagation algorithm, standard disintegration model, ballistic coefficient, hazardous area prediction

中图分类号:

V411.8

陈万通, 田书雨, 张巨联, 刘庆, 任诗雨. 面向亚轨道解体事故的碎片扩散分布建模研究[J]. 系统工程与电子技术, 2022, 44(9): 2922-2928.

Wantong CHEN, Shuyu TIAN, Julian ZHANG, Qing LIU, Shiyu REN. Research on modeling of debris diffusion distribution for suborbital disintegration accident[J]. Systems Engineering and Electronics, 2022, 44(9): 2922-2928.

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参数	特征尺寸区间/m
参数	0.03≤d＜0.05	0.05≤d＜0.08	0.08≤d＜0.11	0.11≤d＜0.20	d≥0.20
碎片数量	916	383	136	126	79
面质比/(m²/kg)	0.17	0.10	0.30	0.25	0.15
速度增量Δv/(m/s)	49.20	44.46	55.64	53.65	47.08

海拔高度/km	特征尺寸区间/m
海拔高度/km	0.03≤d＜0.05	0.05≤d＜0.08	0.08≤d＜0.11	0.11≤d＜0.20	d≥0.20
0~20	1.71×10^-5	1.05×10^-5	6.85×10^-6	4.57×10^-6	2.28×10^-6
20~40	2.82×10^-4	1.73×10^-4	1.13×10^-4	7.51×10^-5	3.76×10^-5
40~60	4.60×10^-3	2.90×10^-3	1.90×10^-3	1.20×10^-3	6.18×10^-4
60~80	7.62×10^-2	4.69×10^-2	3.05×10^-2	2.03×10^-2	1.02×10^-2

亚轨道碎片	序列号
亚轨道碎片	1	2	3	4	5
特征尺寸区间/m	0.03≤d＜0.05	0.05≤d＜0.08	0.08≤d＜0.11	0.11≤d＜0.20	d≥0.20
弹道系数/(kg/m²)	6.5	11.0	3.7	4.4	7.3
落地时间/min	25.97	20.15	34.98	31.55	24.55
标记颜色	红色	绿色	黑色	灰蓝	黄色

面向亚轨道解体事故的碎片扩散分布建模研究

Research on modeling of debris diffusion distribution for suborbital disintegration accident

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