This thesis aims to describe a work which consisted in going to develop a matlab code that would optimize the performance of a Helicon thruster. The work carried out was divided into two main parts. The first part involved modifying the Global Model and then validating it by comparing the results obtained from the code with data collected from previously performed experiments. During this work, in addition to modifying the code in order to obtain more consistent results with real data, an optimization of the code performance was made, in order to minimize the computational time. Once the Global model has been validated, two optimization algorithms have been implemented to maximize performance, in particular a genetic algorithm and an Exhaustive Grid Search have been chosen. These codes were then applied to real cases to be tested and validated.
This thesis aims to describe a work which consisted in going to develop a matlab code that would optimize the performance of a Helicon thruster. The work carried out was divided into two main parts. The first part involved modifying the Global Model and then validating it by comparing the results obtained from the code with data collected from previously performed experiments. During this work, in addition to modifying the code in order to obtain more consistent results with real data, an optimization of the code performance was made, in order to minimize the computational time. Once the Global model has been validated, two optimization algorithms have been implemented to maximize performance, in particular a genetic algorithm and an Exhaustive Grid Search have been chosen. These codes were then applied to real cases to be tested and validated.
Ottimizzazione di un motore Helicon
PASINATO, LORENZO
2022/2023
Abstract
This thesis aims to describe a work which consisted in going to develop a matlab code that would optimize the performance of a Helicon thruster. The work carried out was divided into two main parts. The first part involved modifying the Global Model and then validating it by comparing the results obtained from the code with data collected from previously performed experiments. During this work, in addition to modifying the code in order to obtain more consistent results with real data, an optimization of the code performance was made, in order to minimize the computational time. Once the Global model has been validated, two optimization algorithms have been implemented to maximize performance, in particular a genetic algorithm and an Exhaustive Grid Search have been chosen. These codes were then applied to real cases to be tested and validated.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/48218