基于航天器解体事件的空间碎片寿命算法

研究了针对航天器解体事件所生成的空间碎片的寿命计算方法.给出了基于NASA标准航天器解体模型的航天器解体算法.该算法生成的一系列碎片参数,将作为寿命计算的初始条件.总结了现有求解碎片寿命的算法,并提出了一种半分析算法.该算法运用平均根数法的思路,计算了在J2摄动项的影响下,碎片的半长轴和偏心率的变化率;并采用微分积分法预报半长轴和偏心率随时间的变化.为了适应时变大气模型,该算法限制了计算步长.通过与数值法的比较分析了算法的计算速度和精度.选用了3种大气模型:SA76、GOST和MSIS-00,分析了不同大气模型在计算碎片寿命之间的差异.通过与P-78卫星解体事件的实测数据对比验证了整个算法的正确性.      

在轨航天器撞击解体特性分析与计算

对在轨航天器撞击解体问题进行了研究, 基于NASA 标准解体模型, 给出了在轨航天器撞击解体算法, 重点分析了解体计算时应该满足的几个约束条件. 编写了该算法的通用仿真计算程序, 并通过与P-78 卫星解体事件的实测数据比较验证了算法和程序的正确性. 研究结果对预测分析在轨撞击事件的毁伤特性具有重要的参考价值.      

基于解体事件的空间碎片轨道演化算法研究

针对航天器解体事件所生成的空间碎片的演化过程，进行了数学分析，确定了新生成的空间碎片的速度增量，在该增量作用下碎片轨道会发生变更，本文根据该增量得出了空间碎片在轨道变更后的轨道根数，分析了在大气阻力摄动作用下，空间碎片的数目和轨道分布的演化情况，给出了相关结果，结果表明此算法可行。     

空间碎片云演变过程的阶段划分

根据碎片云从破碎点开始向空间扩散过程中碎片密度和形状的变化规律,以几何形状和起主要作用的因素为特征,定义了球形、椭球形、绳形、螺旋线形、全方位弥漫直至球壳形六个演变阶段.论述了在各个阶段的主要特征和对演变过程起主要作用的因素.总结了与演变过程相关的轨道运动理论和研究方法,分析了各个阶段演变的动力学原理.在球形阶段起主要作用的是分离速度;椭球形阶段可以利用线性化相对运动方程进行分析;绳形与螺旋线形在几何上有质变,但都有结点和结线,并可以利用速度增量理论分析和解释其存在的原因.轨道摄动力消除了结点和结线,导致碎片云的全方位弥漫,并最终使碎片云趋于球壳形.推导和罗列了各阶段转换标志点时刻的计算公式,利用计算机仿真的方法,给出了近地轨道各个阶段碎片云分布示意图,验证了演变过程阶段划分的合理性.     

Observation campaign dedicated to 1968-081E fragments identification

This paper proposes a comprehensive approach to associate origins of space objects newly discovered during optical surveys in the geostationary region with spacecraft breakup events. A recent study has shown that twelve breakup events would be occurred in the geostationary region. The proposed approach utilizes orbital debris modeling techniques to effectively conduct prediction, detection, and classification of breakup fragments. Two techniques are applied to get probable results for origin identifications. First, we select an observation point where a high detection rate for one breakup event among others can be expected. Second, we associate detected tracklets, which denotes the signals associated with a physical object, with the prediction results according to their angular velocities. The second technique investigates which breakup event a tracklet would belong to, and its probability by using the k-nearest neighbor (k-NN) algorithm.     

一种基于TLE数据的轨道异常分析方法

空间在轨物体的轨道异常是航天工程及预警领域普遍关注的问题,及时发现轨道异常意义重大,通过分析空间物体的轨道异常,可以及时发现和识别规避事件或碰撞事件,还可以了解监测网的能力.本文提出一种基于TLE数据的简单的轨道异常分析方法——长半轴变化法.该方法快速有效,应用到低轨在用卫星和美俄解体碎片的异常分析中,异常物体正确识别率可达到100%;对美俄解体碎片进行轨道异常分析后得出,美国空间监视网可以稳定探测90%以上的解体碎片.      

On the effect of considering more realistic particle shape and mass parameters in MMOD risk assessments

One of the primary mission risks tracked in the development of all spacecraft is that due to micro-meteoroids and orbital debris (MMOD). Both types of particles, especially those larger than 0.1 mm in diameter, contain sufficient kinetic energy due to their combined mass and velocities to cause serious damage to crew members and spacecraft. The process used to assess MMOD risk consists of three elements: environment, damage prediction, and damage tolerance. Orbital debris risk assessments for the Orion vehicle, as well as the Shuttle, Space Station and other satellites use ballistic limit equations (BLEs) that have been developed using high speed impact test data and results from numerical simulations that have used spherical projectiles. However, spheres are not expected to be a common shape for orbital debris; rather, orbital debris fragments might be better represented by other regular or irregular solids. In this paper we examine the general construction of NASA’s current orbital debris (OD) model, explore the potential variations in orbital debris mass and shape that are possible when using particle characteristic length to define particle size (instead of assuming spherical particles), and, considering specifically the Orion vehicle, perform an orbital debris risk sensitivity study taking into account variations in particle mass and shape as noted above. While the results of the work performed for this study are preliminary, they do show that continuing to use aluminum spheres in spacecraft risk assessments could result in an over-design of its MMOD protection systems. In such a case, the spacecraft could be heavier than needed, could cost more than needed, and could cost more to put into orbit than needed. The results obtained in this study also show the need to incorporate effects of mass and shape in mission risk assessment prior to first flight of any spacecraft as well as the need to continue to develop/refine BLEs so that they more accurately reflect the shape and material density variations inherent to the actual debris environment.     

Characterization of the cataloged Fengyun-1C fragments and their long-term effect on the LEO environment

The intentional breakup of Fengyun-1C on 11 January 2007 created the most severe orbital debris cloud in history. The altitude where the event occurred was probably the worst location for a major breakup in the low Earth orbit (LEO) region, since it was already highly populated with operational satellites and debris generated from previous breakups. The addition of so many fragments not only poses a realistic threat to operational satellites in the region, but also increases the instability (i.e., collision cascade effect) of the debris population there.     

Instability of the present LEO satellite populations

Several studies conducted during 1991–2001 demonstrated, with some assumed launch rates, the future unintended growth potential of the Earth satellite population, resulting from random, accidental collisions among resident space objects. In some low Earth orbit (LEO) altitude regimes where the number density of satellites is above a critical spatial density, the production rate of new breakup debris due to collisions would exceed the loss of objects due to orbital decay.     

Practical method to identify orbital anomaly as spacecraft breakup in the geostationary region

Identifying spacecraft breakup events is an essential issue for better understanding of the current orbital debris environment. This paper proposes an observation planning approach to identify an orbital anomaly, which appears as a significant discontinuity in archived orbital history, as a spacecraft breakup. The proposed approach is applicable to orbital anomalies in the geostationary region. The proposed approach selects a spacecraft that experienced an orbital anomaly, and then predicts trajectories of possible fragments of the spacecraft at an observation epoch. This paper theoretically demonstrates that observation planning for the possible fragments can be conducted. To do this, long-term behaviors of the possible fragments are evaluated. It is concluded that intersections of their trajectories will converge into several corresponding regions in the celestial sphere even if the breakup epoch is not specified and it has uncertainty of the order of several weeks.     

空间碎片的空间密度算法研究

由于空间碎片的影响, 空间环境日益恶劣,有必要建立空间碎片环境工程模型对空间碎片撞击航天器进行风险评估.本文研究了空间碎片环境模型中的重要环节, 即碎片的空间密度问题,在统计理论与椭圆轨道理论基础上,分析及推导了空间碎片在空间中的分布状况及空间密度,并得出了碎片空间密度的空间坐标函数解析表达式.同时对引入假设条件的合理性进行了讨论,并利用双行元数据对结论进行了验证, 利用本文方法得出的结果与双行元数据吻合.      

Bayesian linking of geosynchronous orbital debris tracks as seen by the Large Synoptic Survey Telescope

We describe a Bayesian sampling model for linking and constraining orbit models from angular observations of “streaks” in optical telescope images. Our algorithm is particularly suited to situations where the observation times are small fractions of the orbital periods of the observed objects or when there is significant confusion of objects in the observation field. We use Markov Chain Monte Carlo to sample from the joint posterior distribution of the parameters of multiple orbit models (up to the number of observed tracks) and parameters describing which tracks are linked with which orbit models. Using this algorithm, we forecast the constraints on geosynchronous (GEO) debris orbits achievable with the planned Large Synoptic Survey Telescope (LSST). Because of the short 15 s exposure times, preliminary orbit determinations of GEO objects from LSST will have large and degenerate errors on the orbital elements. Combined with the expected crowded fields of GEO debris it will be challenging to reliably link orbital tracks in LSST observations given the currently planned observing cadence.     

Space debris cumulative flux considering the Interval Distance-based method

The availability of engineering models to estimate the risk from space debris is essential for space missions. According to current research, cumulative flux calculation is mostly carried out based on the equal-width interval discretization. The method discretizes the volume around the Earth into cells defined in earth centered inertial coordinates. The resulting debris flux onto a target object is shown to depend on the chosen size of the cells. To avoid a discretization error, this must be accounted for. In order to present reliable flux predictions for space mission, the algorithm improvement is an ongoing topic for the related research field. The aim of this study was to examine the discretization error during the cumulative flux determination process. Both the effect of interval step length and the orbital boundary are under investigation. Several typical orbits are selected as examples here and the 2018/01/03 TLE (Two Line Element) data published by the US Space Surveillance Network is used as the debris background in this paper. Furthermore, the Interval Distance-Based method for Discretization (IDD) is adopted in this paper. A position-centered flux determination method is introduced based on the IDD method. According to the example analysis, the IDD used in the flux calculation process provides results which are less affected by the interval step-size setup; and the orbital boundary has no effect on the calculation process. In other words, the discretization error is significantly reduced. The position-centered method provided a possible suggestion for the improvement of space debris environment models.     

空间物体解体碎片云的长期演化建模与分析

空间碎片云由空间物体解体产生的大量空间碎片组成,由于其相对集中地分布在有限的空间内,将会对临近航天器产生较大的碰撞威胁。为了分析解体碎片云长期分布特点,文章首先利用数值积分方法对空间碎片云短期分布规律进行了研究;在此基础上,针对处于环状分布的碎片云,根据碎片所在的轨道高度和具有的面质比值,将碎片划分到不同分组,以每个组作为研究对象,建立了描述碎片云在大气阻力作用下的解析演化模型。模型避免了对单个解体碎片的运动状态进行积分,可大大降低对计算资源和计算时间的需求。考虑在高度为1422km 圆轨道上运行的物体,解体产生了1780个碎片,利用解析演化模型得到碎片云未来50年内的演化分布状态。数值结果表明,碎片云的峰值密度在解体物体轨道高度附近,并在大气阻力作用下向更大高度区间内扩散;较低高度区间内碎片密度具有先增加,然后在大气阻力作用下不断减少的特点。     

基于多项式近似的空间碎片群体轨道预报算法

快速准确地分析空间碎片群轨道演化行为对于其他在轨航天器碰撞规避至关重要。在各摄动力的作用下,空间碎片群演化运动呈现出复杂的非线性特征。空间碎片群体个体数量巨大,如果通过对空间碎片群中每个空间碎片进行轨道积分来分析群体预报的方法会导致计算量过大。针对该问题,提出一种基于多项式近似的轨道快速预报分析方法。该方法将空间碎片群分为少量的标称碎片和其他大量关联碎片。针对标称碎片的轨道预报采用数值积分求解保证预报精度;而针对其他大量的关联碎片轨道预报问题,采用多项式泰勒展开半解析方法求解,从而在保证预报精度的前提下有效减少空间碎片群轨道预报的计算量。为了验证方法的有效性,对不同空间碎片群进行了轨道预报仿真。仿真结果表明,当轨道预报精度设定在1m范围内时,多项式近似算法的计算量较蒙特卡洛方法计算效率提高了2.2~17.2倍,验证了所提出方法的有效性。     

Debris flux comparisons from the Goldstone Radar,Haystack Radar,and Hax Radar prior,during, and after the last solar maximum

The continual monitoring of the low Earth orbit (LEO) debris environment using highly sensitive radars is essential for an accurate characterization of these dynamic populations. Debris populations are continually evolving since there are new debris sources, previously unrecognized debris sources, and debris loss mechanisms that are dependent on the dynamic space environment. Such radar data are used to supplement, update, and validate existing orbital debris models. NASA has been utilizing radar observations of the debris environment for over a decade from three complementary radars: the NASA JPL Goldstone radar, the MIT Lincoln Laboratory (MIT/LL) Long Range Imaging Radar (known as the Haystack radar), and the MIT/LL Haystack Auxiliary radar (HAX). All of these systems are highly sensitive radars that operate in a fixed staring mode to statistically sample orbital debris in the LEO environment. Each of these radars is ideally suited to measure debris within a specific size region. The Goldstone radar generally observes objects with sizes from 2 mm to 1 cm. The Haystack radar generally measures from 5 mm to several meters. The HAX radar generally measures from 2 cm to several meters. These overlapping size regions allow a continuous measurement of cumulative debris flux versus diameter from 2 mm to several meters for a given altitude window. This is demonstrated for all three radars by comparing the debris flux versus diameter over 200 km altitude windows for 3 nonconsecutive years from 1998 to 2003. These years correspond to periods before, during, and after the peak of the last solar cycle. Comparing the year to year flux from Haystack for each of these altitude regions indicate statistically significant changes in subsets of the debris populations. Potential causes of these changes are discussed. These analysis results include error bars that represent statistical sampling errors.     

Predicting the vulnerability of spacecraft components: Modelling debris impact effects through vulnerable-zones

The space environment around the Earth is populated by more than 130 million objects of 1 mm in size and larger, and future predictions shows that this amount is destined to increase, even if mitigation measures are implemented at a far better rate than today. These objects can hit and damage a spacecraft or its components. It is thus necessary to assess the risk level for a satellite during its mission lifetime. Few software packages perform this analysis, and most of them employ time-consuming ray-tracing methodology, where particles are randomly sampled from relevant distributions. In addition, they tend not to consider the risk associated with the secondary debris clouds. The paper presents the development of a vulnerability assessment model, which relies on a fully statistical procedure: the debris fluxes are directly used combining them with the concept of vulnerable zone, avoiding the random sampling the debris fluxes. A novel methodology is presented to predict damage on internal components. It models the interaction between the components and the secondary debris cloud through basic geometrical operations, considering mutual shielding and shadowing between internal components. The methodologies are tested against state-of-the-art software for relevant test cases, comparing results on external structures and internal components.     

Finite element reconstruction approach for on-orbit spacecraft breakup dynamics simulation and fragment analysis

Breakup model is the key area of space debris environment modeling. NASA standard breakup model is currently the most widely used for general-purpose. It is a statistical model found based on space surveillance data and a few ground-based test data. NASA model takes the mass, impact velocity magnitude for input and provides the fragment size, area-to-mass ratio, velocity magnitude distributions for output. A more precise approach for spacecraft disintegration fragment analysis is presented in this paper. This approach is based on hypervelocity impact dynamics and takes the shape, material, internal structure and impact location etc. of spacecraft and impactor, which might greatly affect the fragment distribution, into consideration. The approach is a combination of finite element and particle methods, entitled finite element reconstruction (FER). By reconstructing elements from the particle debris cloud, reliable individual fragments are identified. Fragment distribution is generated with undirected graph conversion and connected component analysis. Ground-based test from literature is introduced for verification. In the simulation satellite targets and impactors are modeled in detail including the shape, material, internal structure and so on. FER output includes the total number of fragments and the mass, size and velocity vector of each fragment. The reported fragment distribution of FER shows good agreement with the test, and has good accuracy for small fragments.     

Modeling of LEO orbital debris populations for ORDEM2008

The NASA Orbital Debris Engineering Model, ORDEM2000, is in the process of being updated to a new version: ORDEM2008. The data-driven ORDEM covers a spectrum of object size from 10 μm to greater than 1 m, and ranging from LEO (low Earth orbit) to GEO (geosynchronous orbit) altitude regimes. ORDEM2008 centimeter-sized populations are statistically derived from Haystack and HAX (the Haystack Auxiliary) radar data, while micron-sized populations are estimated from shuttle impact records. Each of the model populations consists of a large number of orbits with specified orbital elements, the number of objects on each orbit (with corresponding uncertainty), and the size, type, and material assignment for each object. This paper describes the general methodology and procedure commonly used in the statistical inference of the ORDEM2008 LEO debris populations. Major steps in the population derivations include data analysis, reference-population construction, definition of model parameters in terms of reference populations, linking model parameters with data, seeking best estimates for the model parameters, uncertainty analysis, and assessment of the outcomes. To demonstrate the population-derivation process and to validate the Bayesian statistical model applied in the population derivations throughout, this paper uses illustrative examples for the special cases of large-size (>1 m, >32 cm, and >10 cm) populations that are tracked by SSN (the Space Surveillance Network) and also monitored by Haystack and HAX radars operating in a staring mode.     

A study of the material density distribution of space debris

Material density is an important, yet often overlooked, property of orbital debris particles. Many models simply use a typical density to represent all breakup fragments. While adequate for modeling average characteristics in some applications, a single value material density may not be sufficient for reliable impact damage assessments. In an attempt to improve the next-generation NASA Orbital Debris Engineering Model, a study on the material density distribution of the breakup fragments has been conducted and summarized in this paper.