Projecte llegit
Títol: Multi-gravity assist design tool for interplanetary trajectory optimisation
Estudiants que han llegit aquest projecte:
DIAZ CILLERUELO, IKER (data lectura: 15-02-2023)- Cerca aquest projecte a Bibliotècnica
DIAZ CILLERUELO, IKER (data lectura: 15-02-2023)Director/a: ALTMEYER, SEBASTIÁN ANDREAS
Departament: FIS
Títol: Multi-gravity assist design tool for interplanetary trajectory optimisation
Data inici oferta: 19-07-2022 Data finalització oferta: 19-03-2023
Estudis d'assignació del projecte:
- MU AEROSPACE S&T 21
- MU MASTEAM 2015
| Tipus: Individual | |
| Lloc de realització: EETAC | |
| Paraules clau: | |
| Multiple gravity assist, Flyby, Interplanetary trajectory, Optimization, Genetic algorithm | |
| Descripció del contingut i pla d'activitats: | |
| This project deals with the study and investigation of Multi-Planetary Gravitational Assist (MGA) trajectories for interplanetary and interstellar missions to approach other planets in our solar system and/or even other stars beyond it. As missions become increasingly complex, determining feasible trajectories quickly becomes a daunting task as these problems are extremely nonlinear, often with multiple strong basins of attraction in the neighborhood of optimal solutions, and are discontinuous when the planetary gravity-assist order is varied. The objective in this project will be to find a new (modified) approach, using a generic algorithm, for MGA problems. This new algorithm will be tested and compared with others nowadays already established algorithms.
The required software C, C++, Matlab, Python, ... is installed in the school virtual cluster (clufa), which will also be used to carry out the simulations for the project. |
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| Overview (resum en anglès): | |
| This master thesis presents the development of a genetic algorithm for optimizing multiplanetary gravity assist trajectories. The algorithm was designed to address the challenges of finding the most efficient trajectory for a spacecraft traveling into deep space
using multiple planets while performing gravity-assist maneuvers. Given a model for the interplanetary trajectory, the algorithm is able to find a feasible optimal solution. The proposed approach was tested on a set of real missions and was shown to produce solutions more optimal than the real ones given our trajectory model. The results demonstrate the effectiveness of evolutionary algorithms, in concrete genetic algorithms, in finding optimal multi-planetary gravity assist trajectories, making it a valuable tool for mission planning in space exploration. |
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