Sun-Ki Hong

Shape optimization of electromagnetic devices using immune algorithm

A new method employing the immune algorithm (IA) as the search method for the shape optimization of an electromagnetic device is presented. The method is applied to the shape optimization of a pole face of an electromagnet. For the magnetic field analysis the finite element method is used. It is shown that the proposed method with the IA is very useful for the shape optimization of electromagnetic devices.

Finite element analysis of hysteresis motor using the vector magnetization-dependent model

This paper presents a finite element analysis procedure combined with a vector hysteresis model for the accurate analysis of an hysteresis motor. The vector magnetization-dependent model is adopted to calculate the vector magnetization of the hysteresis ring. From the magnitude and direction of the magnetic field intensity, the magnetization of each ring element is calculated by the vector model. By comparing the simulation results with experimental data, it is found that the proposed method gives good results.

Torque calculation of hysteresis motor using vector hysteresis model

This paper presents how to determine the thickness of the hysteresis ring of hysteresis motor using the finite element method combined with a vector hysteresis model. From the magnitude and direction of the magnetic field intensity, the magnetization of each ring element is calculated by the vector hysteresis model and the torque can be obtained from the vector sum of each torque of each element or from the area of the hysteresis loop. From these calculations, it is found that the motor torque is not proportional to the thickness of the ring. As a result, an optimum thickness of the hysteresis ring exists and it can be determined by the proposed method.

Analysis of hysteresis motor using finite element method and magnetization-dependent model

In this paper, finite element analysis (FEA) of a hysteresis motor using a magnetization-dependent model is presented. The hysteresis loop in the hysteresis ring is calculated from the maximum flux density which is obtained by the FEA. The proposed method is applied to a sample motor and the simulation result shows a very good agreement with the experimental one. Various formulations for hysteresis region are compared and it is found that pseudo-permeability method is most effective.

Finite element analysis of magnetizer using Preisach model

A numerical algorithm is developed to predict the distribution of the remnant magnetic flux density in the capacitor-discharge pulse magnetizer by combining the finite element method and the magnetization-dependent Preisach model. After computing the magnetic field by using the finite element method, magnetization-dependent Preisach model is used to simulate the hysteresis curve and get the magnetization. By comparing the demagnetization curves of the anisotropic rubber ferrite magnet and the cogging torque distribution of the VCR drum motor with the experimental results, the validity of the suggested algorithm is proved.

Vector hysteresis model for unoriented magnetic materials

Presented in this paper is a vector hysteresis model for unoriented magnetic materials expanded from the magnetization-dependent Preisach model. Namely, the Preisach elements have rotational capabilities and they memorize their directions, and the Preisach plane is divided into reversible, irreversible regions and the regions maintaining their previous states. By integrating the Preisach plane vectorially for the magnitude-varying rotating field, the hysteresis phenomena such as the directions and magnitude of the magnetization and the hysteresis losses can be calculated. >

Properties of the vector hysteresis model for unoriented magnetic materials

Presented in this paper are the necessary conditions, the properties and the parameter determination method for the vector hysteresis model for unoriented magnetic materials expanded from the magnetization-dependent Preisach model. Hysteresis operators become vectorized and can have any directions according to the applied field history. Under these conditions, vector hysteresis phenomena can be calculated clearly. >

Iron Loss Analysis Considering Excitation Conditions Under Alternating Magnetic Fields

In this paper, the nature of iron loss in electrical steel during alternating field excitation is investigated more precisely. The exact definition of AC iron loss is cleared by accurately measuring the iron loss for conditions of both the sinusoidal magnetic field and sinusoidal magnetic flux density. The results of this approach to iron loss calculations in electrical steel are compared to experimentally-measured losses. In addition, an inverse hysteresis model considering eddy current loss was developed to analyze the iron loss when the input is the voltage source. With this model, the inrush current in the inductor or transformer as well as the iron loss can be calculated.

An improved finite element analysis of magnetic system considering magnetic hysteresis

Most magnetic materials have nonlinear hysteresis phenomena. In this paper, the efficient combination of the finite element method with a hysteresis model is presented. A magnetization-dependent model is employed as a hysteresis model. Finite element formulation is derived from the modified constitutive equation. This method removes the additional computational cost such as calculation of the residual magnetization and shows good convergence with short calculation time.

Formulation of the Everett function using least square method

The Preisach model needs a distribution function or Everett function to simulate the hysteresis phenomena. To obtain these functions, many experimental data obtained from the first order transition curves are required. In this paper, a simple procedure to determine the Everett function analytically using the Gaussian distribution function and least square method is proposed. The Everett function for the interaction field axis is known to have Gaussian distribution and it needs three points to determine the Gaussian parameters. Another one or more points are used to compensate the experimental errors by the least square method. We got good agreement for comparing the simulation results with the experimental ones.