Influence of permanent magnet parameters on the performances of claw pole machines used in hybrid vehicles

Introduction. Claw pole machines (CPM) are commonly used in the automotive industry. Recently, importance has focused on the use and introduction of permanent magnets (PM) in this type of machine to increase the power density. This paper studies the performance of permanent magnet claw pole machines (PM-CPM) used in hybrid electric vehicle applications. The structure considers that the PMs are placed between the claws of the rotor. Purpose. The influence of the PM magnetization effect on the performance of synchronous PM-CPM is analyzed. Radial and tangential magnetizations are applied to obtain the best possible sinusoidal shape of the electromotive force and an acceptable cogging torque. Then, the electromagnetic performance of the PM-CPM is analyzed and evaluated. Furthermore, due to the complexity of the rotor armature, it seems difficult to give a direct relationship between the PM parameters and the machine torque. This led us to study the effects of magnets geometrical dimensions variations on the torque and its ripple. Method. 3D nonlinear model of the machine is analyzed using the finite element method and comparisons between some electromagnetic performances are processed. Results. It was found that the tangential magnetization of PMs makes it possible to obtain a better distribution of the flux density and a minimum of cogging torque mainly responsible for vibrations and acoustic noise. Also, we observed a non-linear variation between the torque and its ripples depending on the dimensions of the PM. In fact, electromagnetic torque increases linearly with PM size but this is not the case for torque ripples. References 22, tables 2, figures 16

Antisymmetrization of a Mean Field Calculation of the T-Matrix

The usual definition of the prior(post) interaction $V(V^\prime )$ between projectile and target (resp. ejectile and residual target) being contradictory with full antisymmetrization between nucleons, an explicit antisymmetrization projector ${\cal A}$ must be included in the definition of the transition operator, $T\equiv V^\prime{\cal A}+V^\prime{\cal A}GV.$ We derive the suitably antisymmetrized mean field equations leading to a non perturbative estimate of $T$. The theory is illustrated by a calculation of forward $\alpha$-$\alpha$ scattering, making use of self consistent symmetries.Comment: 30 pages, no figures, plain TeX, SPHT/93/14

Thermal effects on magnetic hysteresis modeling

A temperature dependent model is necessary for the generation of hysteresis loops of ferromagnetic materials. In this study, a physical model based on the Jiles-Atherton model has been developed to study the effect of temperature on the magnetic hysteresis loop. The thermal effects were included through a model of behavior depending on the temperature parameters Ms and k of the Jiles-Atherton model. The temperaturedependent Jiles-Atherton model was validated through measurements made on ferrite material (3F3). The results have been found to be in good agreement with the model

Neurocomputing Techniques to Predict the 2D Structures by Using Lattice Dynamics of Surfaces

A theoretical study of artificial neural network modelling, based on vibrational dynamic data for 2D lattice, is proposed in this paper. The main purpose is to establish a neurocomputing model able to predict the 2D structures of crystal surfaces. In material surfaces, atoms can be arranged in different possibilities, defining several 2D configurations, such as triangular, square lattices, etc. To describe these structures, we usually employ the Wood notations, which are considered as the simplest manner and the most frequently used to spot the surfaces in physics. Our contribution consists to use the vibration lattice of perfect 2D structures along with the matrix and Wood notations to build up an input-output set to feed the neural model. The input data are given by the frequency modes over high symmetry points and the group velocity. The output data are given by the basis vectors corresponding to surface reconstruction and the rotation angle which aligns the unit cell of the reconstructed surface. Results showed that the method of collecting the dataset was very suitable for building a neurocomputing model that is able to predict and classify the 2D surface of the crystals. Moreover, the model was able to generate the lattice spacing for a given structure

Continuous Functions Modeling with Artificial Neural Network: An Improvement Technique to Feed the Input-Output Mapping

The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage