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Despite the fact that renewable energies are progressing, the present reality shows us that conventional methods of energy generation are more efficient just as contaminating. It leads to find not only new greener methods, but systems capable of improving the efficiency of the renewable energies. This is just what Sun-tracking solar panels try to do. In this project, an azimuth/ elevation mount for Sun-tracking in Burkina Faso with specific space, transport and power requirements is designed. The document includes an initial study of solar geometry, the state-of-the-art of the solar technology, the design decision from several options and the structural analysis. The final result is a preliminary design of a Sun-tracking solar panel

The Tisserand graph allows to find feasible interplanetary transfers in the frame of the multi-gravity assist trajectory design. An optimisation technique relying on this tool would allow for an automatic planetary sequence selection, which would then be used in a more general inteplanetary optimisation method. This study aims at the:- Implementation of an automatic path-finding method using the Tisserand graph.- Assembly of the previous method into an established multi-gravity assist optimiser. Nowadays, technology advances have given us the chance to achieve things that a couple generations ago could not even dream of. Space exploration is one of those achievements and space mission designing is a vital part of it. Since the firsts space missions, the methods used to design them have been increasingly evolving and sophisticating with time. Scheduling a space mission is a complex task that takes time and effort to achieve. A crucial factor to determine a space mission trajectory is cost, which is basically reflected in the fuel used to accomplish the mission. In order to reduce the quantity of propellant used in a mission, multi-gravity assists provide the capability to the spacecraft of altering its speed and path. Throughout this process, analysis to optimize the path of the mission are done. To do so, the optimizers need a list of planet sequences to start analyzing. A first approach to obtain viable lists of planets is using Tisserand graphs. The Tisserand graph is a graphical tool developed to aid in searching for paths for a space mission. The aim of this bachelor’s thesis is to implement a code that uses a tree search algorithm to find viable multi-gravity-assist trajectories from a Tisserand graph in an automatic manner. Several versions of the code have been implemented until reaching a definitive version that fulfills the requirements of the thesis. The final version uses a depth-limited search algorithm to explore a search tree generated automatically in an iterative process from a Tisserand graph, storing in the process all the found flybys sequences.

The Tisserand graph is a graphical method used for interplanetary trajectory design. Nowadays the graph is used by expert hand to manually select feasible solutions, so the automatisation of the selection process is desired. This study aims to:- Identify potential automatisation procedures to find solutions on the Tisserand graph.- Analyse the suitability of the proposed procedures and selection of the optimal one. Interplanetary missions have awoken the interest of space agencies with the objective of discovering and analyzing distant planets. Distant targets become impractical to explore due to the large amount of propellant that is needed, and therefore saving fuel becomes of great use in such missions. The use of gravity-assist saves fuel by using the gravitational field of planets in order to modify the heliocentric velocity of a spacecraft or probe. The Tisserand graph is a useful tool that can be used to find sequences of gravity-assists in order to reach a target planet, and for that reason it becomes a powerful tool in preliminary mission design. In this thesis, a method to find paths in a complete automatic manner on the Tisserand graph is described and developed. The outcome of this study is an algorithm that can find all possible sequences of planets in order to reach a goal planet. The algorithm aims to be as general as possible, so it can be applied to multiple orbital systems.

Sending spacecrafts to any other planet or satellite within the Solar System is a difficult task. To be able to reach it at a precise time, it is crucial to know at which position the target planet is going to be, as well as a good study of the trajectory that the spacecraft is going to follow, which shall not be too close to any other object in the Solar System. It must be also be taken into account that the spacecrafts have a limited fuel capacity. In order to avoid using a high amount of fuel, multi-gravity assist trajectories are useful to provide additional manoeuvering to the spacecrafts without consuming fuel. Given an arbitrary sequence of planets for the swing-by manoeuvres, the next step is to determine when the spacecraft must be launched and how long will it take to it to perform the transfer trajectory. In this preliminary stage of the mission analy-sis, optimization algorithms can be used to obtain the most efficient trajectory, for instance, the trajectory at which the spacecraft requires the least amount of fuel to reach its destination. From the wide variety of heuristic optimization algorithms, in this Master’s thesis four of them have been chosen (Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Algorithm and the simulated annealing algorithm). The aim of this thesis is to perform a hyperparameter optimization, that is, finding whether a certain combination of parameters for each optimizer works properly for different trajecto-ries’ scenarios: a direct launch to an outer planet and a multi-gravity assist scenario to outer planets as well as to inner planets. The results show that it exists a region of parameters for the Genetic Algorithm and the Differential Evolution Algorithm where the best solutions can be found. Simulated Annealing Algorithm shows a dependance on the scenario, as well as some parameters of the Particle Swarm Algorithm.