Target Selection of Multiple Gravity-Assist Trajectories for Solar Boundary Exploration
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摘要: 太阳系边际探测将增进人类对太阳系形成与演化的认知,是未来深空探测的重要方向。由于太阳系边际距离地球遥远,探测所需轨道能量大,利用多天体借力飞行是实现太阳系边际探测的必然选择。研究了面向太阳系边际探测的多天体借力目标选择问题。给出了两种多天体借力动力学模型,并评估了两种模型各自的优势。基于太阳系边际探测的约束与目标处理方法,提出了一种结合两种多天体借力模型的逐步多层嵌套优化方法,并给出设计步骤,实现不同任务约束下太阳系边际探测多天体借力转移轨道优化设计。以2030—2040年针对太阳系鼻尖与尾部探测为例,给出了最优多天体轨道飞行序列排序,验证了所提方法的有效性。仿真结果表明,针对太阳系鼻尖的最优多天体借力序列为地球–金星–地球–地球–木星–土星–太阳系边际鼻尖,而针对太阳系尾部的最优序列为地球–金星–地球–地球–木星–海王星–太阳系边际尾部,研究可以为我国未来太阳系边际探测目标选择与任务规划提供参考。Abstract: Solar system boundary exploration will enhance our understanding of the formation and evolution of the solar system, which is an important issue of future deep space exploration. As the boundary is far from Earth, the energy needed in the exploration is huge. Thus, gravity-assist technique is essential to carry out a solar system boundary exploration mission. This paper aims at multiple gravity-assist transfer design in solar system boundary exploration missions. First, processing method of goals and constraints in solar system boundary exploration are studied. And a progressive nested-loop optimization method combining two different kinds of multiple gravity-assist dynamics is provide, as well as the detailed steps. At last, taking the nose and the tail of solar system boundary for example, the optimal fly-by sequences are provided, proofing the validity of the method. The simulations demonstrates that the optimal multiple gravity –assists trajectories is Earth-Venus-Earth-Earth-Jupiter-Saturn- nose of solar system, and the optimal multiple gravity –assists trajectories is Earth-Venus-Earth-Earth-Neptune-tail of solar system. The research will provide the reference for the target selection and mission planning for future solar system exploration in China.Highlights
● The application scopes of two models of multiple gravity-assists are given. ● A progressive nested-loop optimization method combining two kinds of multiple gravity-assists model is proposed. ● The optimal multiple gravity-assist trajectories to the nose and the tail of solar system boundary are obtained. -
表 1 MGA模型与MGA-DSM模型表现对比
Table 1 Comparison between MGA and MGA-DSM
模型 MGA MGA-DSM 精确度 — √ 收敛性 √ — 共振借力表现 — √ 表 2 太阳系边际鼻尖初步设计借力序列排名
Table 2 Gravity-assist sequence ranking for preliminary design of solar system boundary nose
排名 借力序列 出发时间/
(年/月/日)速度增量/
(km·s–1)1 [3,2,3,3,5,6,nose] 2033/1/22 0.000 0 2 [3,3,5,6,nose] 2034/11/3 1.901 8 3 [3,2,2,5,6 nose] 2033/1/31 2.475 3 4 [3,2,2,4,2,5,nose] 2036/6/26 3.025 7 5 [3,3,5,nose] 2038/1/31 3.112 9 6 [3,4,5,6,nose] 2034/10/4 3.159 4 7 [3,2,3,5,6,nose] 2036/3/26 3.408 4 8 [3,3,3,5,6,nose] 2031/11/24 3.471 1 9 [3,2,2,5,nose] 2039/5/12 3.836 5 10 [3,2,2,3,nose] 2039/5/13 4.026 8 11 [3,4,3,5,nose] 2034/10/8 4.377 9 表 3 地球-金星-地球-地球-木星-土星-鼻尖转移轨道参数
Table 3 E-V-E-E-J-S-helionose transfer parameters
日期
(年/月/日)借力天体 发射C3/
(km2·s–2)借力高度/
km2033/01/22 地球发射 13.731 4 — 2033/07/13 金星借力 8 190 2034/05/24 地球借力 4331 2036/09/04 地球借力 190 2038/06/11 木星借力 172 756 2040/09/13 土星借力 260707 2068年 飞行距离
> 102 AU与尾部方向
矢量夹角 < 17°表 4 地球–-地球–木星–土星–鼻尖转移轨道参数
Table 4 E-E-J-S-nose transfer detailed parameters
日期
(年/月/日)轨道机动 发射C 3/
(km2·s-2)借力高度/km 2033/09/24 地球发射 50.000 0 — 2035/10/08 深空机动 — 2036/08/22 地球借力 64 2038/08/09 木星借力 238 633 2041/01/10 土星借力 241 072 2066年 飞行距离> 100 AU 探测器日心距
> 100 AU与尾部方向矢量
夹角 < 28°表 5 太阳系边际尾部初步设计借力序列排名
Table 5 Gravity-assist sequence ranking for preliminary design of solar system boundary tail
排名 借力序列 出发时间
(年/月/日)速度增量/
(km2·s–1)1 [3,2,3,3,5,8,tail] 2039/07/29 0.000 0 2 [3,3,5,tail] 2031/03/10 2.923 3 3 [3,2,2,5,tail] 2032/12/18 3.108 9 4 [3,3,5,8,tail] 2036/03/17 3.711 7 5 [3,3,4,5,8,tail] 2039/06/08 4.460 1 6 [3,2,3,3,8,tail] 2037/10/24 4.570 5 7 [3,4,3,5,tail] 2031/06/28 4.620 3 表 6 地球-金星-地球-地球-木星-海王星-尾部转移轨道参数
Table 6 E-V-E-E-J-S-N-tail transfer detailed parameters
日期
(年/月/日)借力天体 发射C3 /
(km2·s–2)借力
高度/km2039/7/29 地球发射 18.226 70 — 2040/1/12 金星借力 4476 2040/11/24 地球借力 3624 2043/3/22 地球借力 937 2044/9/13 木星借力 3575 2052/8/20 海王星借力 4 315 066 2074年 飞行距离< 103 AU 与尾部方向矢量夹角 > 7° -
[1] 陈莉丹,谢剑锋,刘勇, 等. 中国深空探测任务轨道控制技术综述[J]. 深空探测学报(中文版),2019, 6(3): 210-218.CHEN L D, XIE J F, LIU Y, et al. Review of the orbit maneuver technology for Chinese deep space exploration missions[J].Journal of Deep Space Exploration, 2019, 6(3): 210-218. [2] 郑永春,欧阳自远.太阳系探测的发展趋势与科学问题分析[J].深空探测学报(中文版),2014,1(2):83-92.ZHENG Y C, OUYANG Z Y.Development trend analysis of solar system explorationand the scientific vision for future missions[J]. Journal of Deep Space Exploration, 2014, 1(2): 83-92. [3] MACEK W M,WAWRZASZEK A,BURLAGA L F. Multifractal structures detected by Voyager 1 at the heliospheric boundaries[J]. The Astrophysical Journal Letters,2014,793(2):L30. doi: 10.1088/2041-8205/793/2/L30 [4] STRAUSS R D T. Voyager 2 enters interstellar space[J]. Nature Astronomy,2019,3(11):963-964. doi: 10.1038/s41550-019-0942-5 [5] LISSE C M,MCNUTT JR R L,BRANDT P C,et al. Interstellar Probe (ISP) observations of the solar system's debris disks[J]. AGUFM,2018,2018:SH32C-09. [6] VASILE M,DE PASCALE P. Preliminary design of multiple gravity-assist trajectories[J]. Journal of Spacecraft and Rockets,2006,43(4):794-805. doi: 10.2514/1.17413 [7] VINKΌT, IZZO D. Global Optimization heuristics and test problems for preliminary spacecraft trajectory design: ACT-TNT-MAD-GOHTPPSTD[R]. [S. l.]: European Space Agency, the Advanced Concepts Team: 2008. [8] VASILE M,MINISCI E,LOCATELLI M. Analysis of some global optimization algorithms for space trajectory design[J]. Journal of Spacecraft and Rockets,2010,47(2):334-344. [9] CERIOTT M,VASILE M. Automated multi-gravityassist trajectory planning with a modified ant colony algorithm[J]. Journal of Aerospace Computing,Information and Communication,2010,7(9):261-293. doi: 10.2514/1.48448 [10] LONGUSKI J,WILLIAMS S. Automated design of gravity-assist trajectories to mars and the outer planets[J]. Celestial Mechanics and Dynamical Astronomy,1991,52(3):207-220. doi: 10.1007/BF00048484 [11] 陈诗雨,杨洪伟,宝音贺西.木星系探测及行星穿越任务轨迹初步设计[J].深空探测学报,2019,6(2):189-194.CHEN S Y, YANG H W, BAOYIN H X. Preliminary design for the trajectories of jovian and planetary mission[J]. Journal of Deep Space Exploration, 2019, 6(2): 189-194. [12] OLDS A D,KLUEVER C A,CUPPLES M L. Interplanetary mission design using differential evolution[J]. Journal of Spacecraft and Rockets,2007,44(5):1060-1070. doi: 10.2514/1.27242 [13] GAD A,ABDELKHALIK O. Hidden genes genetic algorithm for multi-gravity-assist trajectories optimization[J]. Journal of Spacecraft and Rockets,2011,48(4):629-641. doi: 10.2514/1.52642 [14] ABDELKHALIK O,GAD A. Dynamic-size multiple populations genetic algorithm for multigravity-assist trajectories optimization[J]. Journal of Guidance,Control,and Dynamics,2012,35(2):520-529. [15] ENGLANDER J A,CONWAY B A,WILLIAMS T. Automated mission planning via evolutionary algorithms[J]. Journal of Guidance,Control,and Dynamics,2012,35(6):1878-1887. [16] MEWALDT R A, KANGAS J, KERRIDGE S J, et al. A small interstellar probe to the heliospheric boundary and interstellar space[J]. Acta Astronautica, 1995, 35: 267–276. -
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