| KEYWORD |
Physically-Informed Neural Networks for digital twins of Flight Control Actuators
keywords DIGITAL TWIN, FLIGHT CONTROL SYSTEMS, MECHATRONICS, NEURAL NETWORKS
Reference persons ANTONIO CARLO BERTOLINO, ANDREA DE MARTIN, MASSIMO SORLI
Research Groups 14-Meccatronica e servosistemi
Thesis type SIMULATION
Description "Physically-Informed Neural Networks" are currently perceived as a possible breakthrough technology for the definition of digital-twins of complex components with reduced computational requirements.
The student will perform a literature review on the subject and investigate the considered case study, an electro-hydraulic servo actuator for primary flight controls.
A physics-based, high-fidelity model of such case study will be provided.
The student will define the network topology and its training strategy, proceeding hence to replace parts of the physics-based model with the neural networks prepared during the first part of the work.
A combarison between the simulation behavior with such modification and the original physics-based model will be performed.
Expected results include the definition of a dynamic model for the flight control actuator able to maintaing a high complexity level with reduced computational load, and a first indication towards the possibility to deploy the produced model within real-time simulation environments.
Required skills Mechatronics, knowledge of Matlab/Simulink environment
Deadline 29/11/2024
PROPONI LA TUA CANDIDATURA