Tesi etd-06222014-131003 |
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Tipo di tesi
Tesi di dottorato di ricerca
Autore
TRIPODI, ERNESTO
URN
etd-06222014-131003
Titolo
Numerical and analytical models for electromechanical motion systems.
Settore scientifico disciplinare
ING-IND/31
Corso di studi
INGEGNERIA
Relatori
tutor Musolino, Antonino
tutor Rizzo, Rocco
commissario Díez Jiménez, Efrén
commissario Tartagni, Marco
tutor Rizzo, Rocco
commissario Díez Jiménez, Efrén
commissario Tartagni, Marco
Parole chiave
- applied electromagnetism
- Computational electromagnetics
- Electromagnetic Launchers
- MagLev
- Magnetic Bearings
Data inizio appello
01/07/2014
Consultabilità
Completa
Riassunto
This dissertation presents our contribution on the
field of applied electromagnetism.
Our work has mainly been focused on the design and modeling of devices exploiting magneto-mechanical properties (e.g. magnetic levitation (MagLev) systems, Electromagnetic Launchers (EML), electro-dynamic bearings). The numerical modeling of the problem has been designed through a low frequency integral formulation of the Maxwell equations coupled with the Newton Euler dynamics equations. The non-linear system has been solved wit different schemes, mainly based on predictor corrector approaches.
A C framework exploiting GPGPU capability (Nvidia CUDA) has been developed and validated by comparison with experimental results or FEM simulations. A lot of interest has also been posed to the design of innovative electro-mechanical devices, that have been deeply analysed and simulated by the developed numerical formulation.
field of applied electromagnetism.
Our work has mainly been focused on the design and modeling of devices exploiting magneto-mechanical properties (e.g. magnetic levitation (MagLev) systems, Electromagnetic Launchers (EML), electro-dynamic bearings). The numerical modeling of the problem has been designed through a low frequency integral formulation of the Maxwell equations coupled with the Newton Euler dynamics equations. The non-linear system has been solved wit different schemes, mainly based on predictor corrector approaches.
A C framework exploiting GPGPU capability (Nvidia CUDA) has been developed and validated by comparison with experimental results or FEM simulations. A lot of interest has also been posed to the design of innovative electro-mechanical devices, that have been deeply analysed and simulated by the developed numerical formulation.
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