The aim of this work is to present a new tool for the analysis of magnetic field problems considering 2-D magnetic hysteresis. In particular, this tool makes use of the Finite Element Method to solve the magnetic field problem in real device, and fruitfully exploits a neural network (NN) for the modeling of 2-D magnetic hysteresis of materials. The NS has as input the magnetic inductions components B at the k-th simulation step and returns as output the corresponding values of the magnetic field H corresponding to the input pattern. It is trained by vector measurements performed on the magnetic material to be modeled. This input/output scheme is directly implemented in a FEM code employing the magnetic potential vector A formulation. Validations through measurements on a real device have been performed.

Cardelli, E., Faba, A., Laudani, A., Lozito, G.M., RIGANTI FULGINEI, F., Salvini, A. (2016). A Neural-FEM tool for the 2-D magnetic hysteresis modeling. PHYSICA. B, CONDENSED MATTER, 486, 111-115 [10.1016/j.physb.2015.12.006].

A Neural-FEM tool for the 2-D magnetic hysteresis modeling

Cardelli, Ermanno;LAUDANI, ANTONINO;LOZITO, GABRIELE MARIA;RIGANTI FULGINEI, Francesco;SALVINI, Alessandro
2016-01-01

Abstract

The aim of this work is to present a new tool for the analysis of magnetic field problems considering 2-D magnetic hysteresis. In particular, this tool makes use of the Finite Element Method to solve the magnetic field problem in real device, and fruitfully exploits a neural network (NN) for the modeling of 2-D magnetic hysteresis of materials. The NS has as input the magnetic inductions components B at the k-th simulation step and returns as output the corresponding values of the magnetic field H corresponding to the input pattern. It is trained by vector measurements performed on the magnetic material to be modeled. This input/output scheme is directly implemented in a FEM code employing the magnetic potential vector A formulation. Validations through measurements on a real device have been performed.
2016
Cardelli, E., Faba, A., Laudani, A., Lozito, G.M., RIGANTI FULGINEI, F., Salvini, A. (2016). A Neural-FEM tool for the 2-D magnetic hysteresis modeling. PHYSICA. B, CONDENSED MATTER, 486, 111-115 [10.1016/j.physb.2015.12.006].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/295008
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