This paper deals with the direct inclusion of vector hysteresis and eddy current losses in a 2D finite element (FE) time stepping analysis. It is shown that a vector Preisach model can be inverted in an efficient way by means of the Newton-Raphson method and that it thus can easily be included in the Newton-Raphson iterations of the FE equations. The eddy current losses are accounted for by considering an additional conductivity matrix in the FE equations. The method is applied to the no load simulation of an induction motor. The calculation results are discussed and compared to measurements.

Articles you may be interested inInvestigation of magnetic and magnetomechanical hysteresis properties of Fe-Si alloys with classical and mechanical Barkhausen effects and magnetoacoustic emission J. Appl. Phys. 93, 7465 (2003); 10.1063/1.1558661 Residual stresses in punched laminations: Phenomenological analysis and influence on the magnetic behavior of electrical steels J. Appl. Phys. 93, 7106 (2003); 10.1063/1.1557279 Magnetic properties of Fe-Si steel depending on compressive and tensile stresses under sinusoidal and distorted excitations

A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling.

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In this article, magnetization loops under mechanical stress and magnetostriction loops under quasistatic magnetic excitation conditions are discussed. In both cases, the hysteresis loops are modeled using the Preisach theory. The identification procedure of the material parameters is described. The article discusses first the shape of the Preisach distribution function for the study of magnetostriction loops. Next, a Preisach model is proposed for the description of magnetization loops under mechanical stress starting from the magnetization loop obtained without applying mechanical stress. A setup has been constructed for the measurement of magnetization loops under compressive or tensile stress. Also, a measuring system based on a single sheet tester and on optical displacement measurement techniques is used to establish the magnetostrictive behavior of laminated SiFe alloys. It is shown that a good correspondence between the calculated and measured magnetization and magnetostriction loops is obtained.