Model of pulsating current traction motor taking into consideration magnetic losses in steel

The aim of the work is to develop a mathematical model of the traction motor of the pulsating current of an electric locomotive taking into account the magnetic losses in the motor steel to determine the starting parameters depending on the voltage of the armature winding. Methodology. Mathematical modeling of electromagnetic processes in a traction motor of pulsating current is applied taking into account the nonlinear nature of the armature inductance, the inductance of the excitation winding and the nonlinear nature of the universal magnetic characteristic. The magnetic losses in the steel of the traction motor were taken into account by establishing the dependence of these losses on the frequency of reversal, the magnetic flux in the magnetic circuit of the motor and the geometric dimensions of the motor. Results. The mathematical model of calculation of starting parameters of the traction engine of the pulsating current of the traction drive of the electric locomotive of alternating current taking into account the equation of instantaneous value of losses in engine steel is developed. The dynamic characteristics of the traction motor with pulsating current are obtained. It allows to investigate starting parameters of the traction engine on the basis of the received mathematical model and to design elements of the traction drive of the electric locomotive according to the specification, to choose optimum design parameters. Originality. For the first time a comprehensive study of the pulsating current traction motor was carried out taking into account the nonlinear nature of the armature inductance, excitation winding inductance and nonlinear nature of the universal magnetic characteristic and taking into account the magnetic losses in the motor steel. Practical significance. The model of the traction motor of pulsating current taking into account losses in steel of the engine on the basis of the carried-out calculation is developed. Experimental studies have confirmed the adequacy of the model, which allows to apply the obtained model to develop a mathematical model of an AC electric locomotive to study the electrodynamic processes in it at different modes of operation of the electric locomotive.

Solar Flare Prediction Based on the Fusion of Multiple Deep-learning Models

Solar flare formation mechanisms and their corresponding predictions have commonly been difficult topics in solar physics for decades. The traditional forecasting method manually constructs a statistical relationship between the measured values of solar active regions and solar flares that cannot fully utilize the information related to solar flares contained in observational data. In this article, we first used neural-network methods driven by the measured magnetogram and magnetic characteristic parameters of the sunspot group to learn the prediction model and predict solar flares. The prediction fusion model is based on a deep neural network, convolutional neural network, and bidirectional long short-term memory neural network and can predict whether a sunspot group will have a flare event above class M or class C in the next 24 or 48 hr. The real skill statistics (TSS) and F1 scores were used to evaluate the performances of our fusion model. The test results clearly show that this fusion model can make full use of the information related to solar flares and combine the advantages of each independent model to capture the evolution characteristics of solar flares, which is a much better performance than traditional statistical prediction models or any single machine-learning method. We also proposed two frameworks, namely F1_FFM and TSS_FFM, which optimize the F1 score and TSS score, respectively. The cross validation results show that they have their respective advantages in the F1 score and TSS score.

The Study of the Influence of Matrix, Size, Rotation Angle, and Magnetic Field on the Isothermal Entropy, and the Néel Phase Transition Temperature of Fe2O3 Nanocomposite Thin Films by the Monte-Carlo Simulation Method

In this paper, the study of the influence of the matrix structure (mxm) of thin-film, rotation angle (α), magnetic field (B), and size (D) of Fe2O3 nanoparticle on the magnetic characteristic quantities such as the magnetization oriented z-direction (MzE), z-axis magnetization (Mz), total magnetization (Mtot), and total entropy (Stot) of Fe2O3 nanocomposites by Monte-Carlo (MC) simulation method are studied. The applied MC Metropolis code achieves stability very quickly, so that after 30 Monte Carlo steps (MCs), the change of obtained results is negligible, but for certainty, 84 MCs have been performed. The obtained results show that when the mxm and α increase, the magnetic phase transition appears with a very small increase in temperature Néel (TNtot). When B and D increase, TNtot increases very strongly. The results also show that in Fe2O3 thin films, TNtot is always smaller than with Fe2O3 nano and Fe2O3 bulk. When the nanoparticle size is increased to nearly 12 nm, then TNtot = T = 300 K, and between TNtot and D, there is a linear relationship: TNtot = −440.6 + 83D. This is a very useful result that can be applied in magnetic devices and in biomedical applications.

Identification of Material Properties and Optimal Design of Magnetically Shielded Rooms

In this paper, we propose an optimal design procedure for magnetically shielded rooms. Focusing on multi-layer ferromagnetic structures, where inner layers operate at very low magnetic field, we propose an identification method of the magnetic material characteristic in the Rayleigh region. A numerical model to simulate the shielding efficiency of a multi-layer ferromagnetic structure is presented and experimentally tested on different geometries and layer configurations. The fixed point iterative method is adopted to handle the nonlinearity of the magnetic material. In conclusion, the optimization of the design parameters of a MSR is discussed, using the Vector Immune System algorithm to minimize the magnetic field inside the room and the cost of the structure. The results highlight that a linear magnetic characteristic for the material is sufficient to identify the suitable geometry of the shield, but the nonlinear model in the Rayleigh region is of fundamental importance to determine a realistic shielding factor.

Super-Antiferromagnetics Nanoparticles for Magnetic Hyperthermia Applications: Effect of the Dc and Ac Magnetic Field

The importance of magnetic hyperthermia cancer treatments is based on the magnetic characteristic of the nanoparticles and their dependence on the DC and AC magnetic fields. In this paper we Study the dynamic magnetic hysteresis (DMH) of Super Antiferromagnetic nanoparticle, we use Brown’s continuous diffusion model to we evaluate the hysteresis loops, for extensive ranges of the anisotropy, the ac and dc magnetic fields.