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Projecte llegit

Títol: Genetic algorithm optimazation as multi-gravity assist design tool


Estudiants que han llegit aquest projecte:


Director: ALTMEYER, SEBASTIÁN ANDREAS

Departament: FIS

Títol: Genetic algorithm optimazation as multi-gravity assist design tool

Data inici oferta: 09-01-2023     Data finalització oferta: 09-09-2023



Estudis d'assignació del projecte:
    MU AEROSPACE S&T 15
    MU AEROSPACE S&T 21
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
Multiple gravity assist, Interplanetary trajectory design, Planning, Optimization, Genetic algorithm
 
Descripció del contingut i pla d'activitats:
In about the last 50 years many spacecraft have been launched in an effort to explore our solar system (and beyond), although only a fraction of our solar system has been explored. Larger
spacecraft and scientific payloads will be required to further explore the solar system.
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.

The objective in this project will be the use of a generic algorithm to optimize the trajectory using MGA, to obtain a minimization of the delta-v required for the spacecraft. Aside the improvement of various parameters like convergence, diversity, cross-over as well as the extended work on the cost-function, the main focus will lie on the determination of the best sequence of planets for a mission. The algorithm will be tested and compared with others nowadays already established algorithms and experimental missions.
 
Overview (resum en anglès):

This master thesis presents the development of a genetic algorithm for optimizing interplanetary trajectories using multi-planetary gravity assists while considering time as an additional objective in the fitness evaluation. The objective of the research is to address the challenges of designing efficient trajectories that minimize both delta-v and travel duration. The aim of this thesis is to develop an all-encompassing deep space mission trajectory design tool where a trade-off between the total delta-v used and the arrival time can be made, in the interest of the overall mission profile. The results obtained highlight the effectiveness of genetic algorithms in finding optimal multi-planetary gravity assist trajectories and contribute to the advancement of trajectory optimization techniques for future space missions.


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