A neural approach for the numerical modeling of two-dimensional magnetic hysteresis

Abstract

This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, making possible a full computer simulation in a reasonable time. The neural system proposed consists of four inputs representing the magnetic field and the magnetic inductions components at each time step and it is trained by 2-d measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the neural system returns the predicted value of the field H at the same time step. A suitable partitioning of the neural system, described in the paper, makes the computing process rather fast. Validations with experimental tests and simulations for non-symmetric and minor loops are presented

A Semi-Anaytical Model for the Analysis of a Permanent Magnet Tubular Linear Generator

“…As far the evaluation of the internal impedance per phase of the machine we considered the windings constituted by a copper wire with a cross section of 2.0 mm 2 ; the total DC resistance of one winding is about 3.1 Ω while according to By using (19) the average flux density just inside the teeth of the stator (r=56.5 mm) has been evaluated. Fig.…”

“…is the radial component of the flux density on the inner radius of the stator due to the equivalent magnetization currents and to the current sheet which is equivalent to the stator windings with the given current. The average flux density entering the tooth is obtained by dividing the flux as obtained by (19) by the surface of the tooth. The second contribution, due to the leakage fluxes in the slots, is given by: 0 1 2 2 2 1 1 1 3 3 1 2 1 2 2 2 1 2 1 2 2 3 2 2 2 2 3 4 3 3 4 3 3 3 3 4 3 2 2 4 3 2…”

The AMLEV technology applied to low speed urban transportation systems

“…It is based on a FEM code and several simulations have been performed in order to identify a suitable shape and dimensions of the steel cores, capable to increase the ratio between the levitating force and the lateral destabilizing force . Nowadays, FEM packages are able to take into account nonlinear characteristics as well as hysteresis behavior of materials [13]- [15]. A high ratio = / allows designing an effective guidance system, able to counterbalance the destabilizing force.…”

Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm

“…An Ising hysteresis model is put forward on the basis of NN aimed at predicting the effect of the temperature, the field amplitude and the field frequency on the hysteresis properties [20]. Recently, some scholars have developed the vector magnetic hysteresis model in accordance with the conventional NN [21], [22], nonetheless, in the papers, the conventional feed-forward NN (FFNN) is applied, and the inherent shortcomings of the FFNN are not taken into account, for instance, easier to fall into the local minima.…”