.png){kind=icon}
.png){kind=icon}
Return
An automatic tree search algorithm for the Tisserand graph
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2020.10.028.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Tisserand graph (TG) is a graphical tool commonly employed in the preliminary design of gravity-assisted trajectories. The TG is a two-dimensional map showing essential orbital information regarding the Keplerian orbits resulting from the close passage by one or more massive bodies, given the magnitude of the hyperbolic excess speed (nu(infinity)) and the minimum allowed pericenter height for each passage. Contours of constant nu(infinity) populate the TG. Intersections between contours allow to link consecutive flybys and build sequences of encounters en route to a selected destination. When the number of perturbing bodies is large and many nu(infinity) levels are considered, the identification of all the possible sequences of encounters through visual inspection of the TG becomes a laborious task. Besides, if the sequences are used as input for a numerical code for trajectory design and optimization, an automated examination of the TG is desirable. This contribution describes an automatic technique to explore the TG and find all the encounter paths. The technique is based on a tree search method, and the intersections between contours are computed using the regula-falsi scheme. The method is validated through comparisons with solutions available in the open literature. Examples are given of application to interplanetary mission scenarios, including the coupling with a trajectory optimizer. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Fat Lilly of Engineering, Alexandria University.
Keywords:
Spaceflight mechanics
Interplanetary trajectory design
Gravity assist
Tisserand graph
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.8
Papers:
6.3K
Citations:
2.6W