Analytical design methods for transfer trajectories between the Earth and the Lunar Orbital Station

Gao, Yongfei; Wang, Zhao-Kui; Zhang, Yulin

doi:10.1007/s10509-018-3426-7

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…However, the selections of the parameters are different. Peng [14] and Gao [15] selected the entry point parameters of the lunar influence sphere to play the orbital patched conic parameters. Li [16] selected the exit point parameters to do this.…”

Section: Although the Constellation Program Was Replaced Later By The...mentioning

confidence: 99%

“…The latitude and longitude on the Moon's influence sphere, and flight duration from TLI to the moment when the lunar probe passes into the Moon's influence sphere are selected as the trans-lunar orbital design variables [14,15]. The Newton iteration is applied to calculate the Earth-centered true of anomaly of the entrance point on the Moon's influence sphere, and then the trans-lunar orbit before the moment when the lunar probe passes into the Moon's influence sphere can be designed.…”

Section: A Retrograde Semi-analytical Modelmentioning

confidence: 99%

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Reachable Set Analysis of Practical Trans-Lunar Orbit via a Retrograde Semi-Analytic Model

Sheng-gang

2022

Preprint

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Different from the Apollo flight mode, a safer trans-lunar flight mode for the crews is preferred. The previous-arrived lunar module rendezvous with the crew exploration vehicle on the low lunar destination orbit, and then the crews ride the lunar module for descending the lunar surface destination. The lunar module, which includes the descent and ascent stages, flies from low Earth orbit to the low lunar destination orbit with two tangential impulses. The low lunar destination orbital reachable-set of its practical trans-lunar orbit limits the crew’s lunar surface reachable area. Therefore, the low lunar destination orbital reachable-set of the practical trans-lunar orbit is analyzed in this paper. A retrograde semi-analytic model is proposed for rapidly computing the practical two-impulse trans-lunar orbit firstly, which refers the ephemeris table twice for more precision perilune orbital elements. Then, the envelope of the reachable-set, which is generated using the multiple-level traversal searching approach with the retrograde semi-analytic model, is re-checked by the continuation theory with the high-precision orbital model. Besides, some factors that affect the reachable-set are also measured. The results show that neither the Earth-Moon distance nor the trans-lunar duration affects the reachable-set. However, if the trans-lunar injection inclination is smaller than the inclination of the moon’s path, the reachable-set becomes smaller or even reduces into an empty set. In brief, the proposed retrograde semi-analytic model for computing the reachable-set provides a helpful and fast tool for selecting an applicable lunar surface landing site for the manned lunar mission overall design.

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Section: Although the Constellation Program Was Replaced Later By The...mentioning

confidence: 99%

Section: A Retrograde Semi-analytical Modelmentioning

confidence: 99%

Reachable Set Analysis of Practical Trans-Lunar Orbit via a Retrograde Semi-Analytic Model

Sheng-gang

2022

Preprint

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“…Topputo [7] proposed a direct transcription and multiple shooting strategy to solve the coplanar two-impulse Earth-Moon transfers in the restricted four-body model, and systematically concluded the variation of the velocity increment with the flight time. Lyu et al [8] researched the two-impulse Earth-Moon transfers in the circular restricted three-body problem (CRTBP) based on a differential correction approach. Gao et al [9] designed the twoimpulse Earth-Moon trajectory by employing the lunar orbit station and analyzed orbital transfer windows in typical years.…”

Section: Introductionmentioning

confidence: 99%

Fast Solution to the Free Return Orbit's Reachable Domain of the Manned Lunar Mission by Deep Neural Network

Yang,

Li,

Zhang

et al. 2024

J. of Syst. Eng. Electron.

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It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit's inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

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“…Li et al [9] proposed a modified patched conic model to generate trans-Earth trajectories from high-latitude lunar landing sites. Gao et al [10] studied two-impulse transfer trajectories from a lunar orbital station to the Earth. An iterative algorithm was combined with the patched conic approximation, and the orbital windows were also analyzed.…”

Section: Introductionmentioning

confidence: 99%

Multiphase Trajectory Optimization of a Lunar Return Mission to an LEO Space Station

Yang

Zhang

et al. 2021

International Journal of Aerospace Engineering

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Lunar exploration architecture can be made more flexible and reliable with the support of a low-Earth orbit (LEO) space station. This study therefore evaluated a proposed hybrid optimization scheme to design the entire trajectory of a reusable spacecraft starting from trans-Earth injection (EI) at the perilune and ending at an LEO space station. As such a trajectory has multiple constraints and multiple dynamical models, it is divided into the trans-Earth phase, aerocapture phase, and postatmospheric phase. The optimization scheme is performed at two levels: sublevel and top level. At the sublevel, two novel pseudo rules are proposed to optimize the trans-Earth trajectory so that it satisfies the coplanar constraints of the space station. Then, in the aerocapture phase, the bank angle is optimized to satisfy the mission constraints, and in the atmospheric phase, the one-impulsive maneuver is performed and optimized to insert the spacecraft into the target space station orbit. The multiple phases are connected to each other by boundary conditions where the terminal state of the previous phase is transformed into the initial state of the following phase. At the top level, the vacuum perigee height is selected as the mission design variable based on problem characteristics analysis and a hybrid optimization scheme is conducted to minimize the total velocity increment. The simulation results demonstrate that the proposed hybrid optimization method is effective for the design of an entire trajectory with acceptable velocity cost which is less than that in the previous study. The coplanar constraints of the space station and other mission constraints in each phase are also satisfied. Furthermore, the proposed trajectory design method is shown to be applicable to a reusable spacecraft returning to an LEO space station parked in any arbitrary orbital plane.

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Analytical design methods for transfer trajectories between the Earth and the Lunar Orbital Station

Cited by 8 publications

References 27 publications

Reachable Set Analysis of Practical Trans-Lunar Orbit via a Retrograde Semi-Analytic Model

Reachable Set Analysis of Practical Trans-Lunar Orbit via a Retrograde Semi-Analytic Model

Fast Solution to the Free Return Orbit's Reachable Domain of the Manned Lunar Mission by Deep Neural Network

Multiphase Trajectory Optimization of a Lunar Return Mission to an LEO Space Station

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