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Area Engineering
Development of a machine learning algorithm for directional thrust control in aerospace nozzle
keywords AEROSPACE, MACHINE LEARNING, DEEP LEARNING, OPTIMIZATION, TRAJECTORY CONTROL
Reference persons CARLO GIOVANNI FERRO, PAOLO MAGGIORE, BARTOLOMEO MONTRUCCHIO
External reference persons matteo dallavedova
antonio marceddu
alessandro aimasso
matteo bertone
Research Groups 16-ASTRA: Additive manufacturing for Systems and sTRuctures in Aerospace
Thesis type SIMULATIONS AND DESIGN
Description This thesis aims to develop and validate a machine learning algorithm for thrust vectoring control in solid propellant aerospace nozzles. The goal is to speed up the prediction system of the maneuver to be performed to reach the target point while maintaining unaltered accuracy and efficiency.
Throughout the duration of the thesis, the student will be supervised by Drs. Carlo Ferro and Antonio Marceddu and Profs. Bartolomeo Montrucchio and Paolo Maggiore. Upon completion of the thesis, the student is expected to improve their programming skills, along with their ability to work on projects of medium complexity.
For more information, please contact all professors at the following e-mails:
bartolomeo.montrucchio@polito.it
carlo.ferro@polito.it
antonio.marceddu@polito.it
alessandro.aimasso@polito.it
matteo.bertone@polito.it
paolo.maggiore@polito.it
matteo.dallavedova@polito.it
Deadline 07/03/2025
PROPONI LA TUA CANDIDATURA