K.R. Shao

Sequential Optimization Method for the Design of Electromagnetic Device

Three sequential optimization methods, sequential least square method, sequential Kriging method, and sequential linear Bayesian method, are presented for the optimization design of electromagnetic device. Sequential optimization method (SOM) is composed of coarse optimization process and fine optimization process. The main purpose of the former is to reduce the design space; while the target of the latter is to update the optimal design parameters. To illustrate the performance of the proposed methods, an analytic test function and the TEAM Workshop Problem 22 are investigated. Experimental results of test function demonstrate that SOM can obtain satisfactory solutions; and practical application illustrates that the number of finite element sample points is less than 1/10 compared with that by direct optimization method, while the optimal results are even better than that by direct optimization method.

Robust Design Optimization of PM-SMC Motors for Six Sigma Quality Manufacturing

In our previous work, soft magnetic composite (SMC) material was employed to design cores for two kinds of permanent magnet (PM) motors, namely transverse flux machine (TFM) and claw pole motor. Compared with motors designed by traditional silicon steel sheets, these motors require 3D flux design with new material and new manufacturing method. Meanwhile, the performances of these motors highly depend on the material and manufacturing parameters besides structure parameters. Therefore, we present a robust design optimization method for high quality manufacturing of these PM-SMC motors to improve their industrial applications. Thereafter, from the design analysis of a PM-SMC TFM, it can be found that the proposed method can significantly improve the manufacturing quality and reliability of the motor, and reduce the manufacturing cost.

System Level Six Sigma Robust Optimization of a Drive System With PM Transverse Flux Machine

From our previous study, permanent magnet (PM) transverse flux machine with soft magnetic composite material core is very promising. However, from the point of view of engineering application, at least two more aspects have to be considered. First, not only the machine but also its control system has to be investigated and optimized simultaneously. Second, robust design requirements must be included in the system level optimization, which is a very important issue in modern quality design. Therefore, to improve the applications of this type of machine, we present a system level six sigma robust optimization method to design a drive system with this motor and field-oriented control scheme. The optimal robust solutions obtained are compared with those from deterministic optimization method and initial design scheme. From the comparison, we can see that the systems reliabilities and robust levels are improved significantly by the proposed method, and the probability of system failure can be reduced a lot.

Sequential Subspace Optimization Method for Electromagnetic Devices Design With Orthogonal Design Technique

We present two new sequential optimization strategies, a sequential subspace optimization method (SSOM) and an improved sequential optimization method (SOM) with orthogonal experimental design technique, to deal with optimization design problems of electromagnetic devices in this work. To implement the proposed methods, we first divide the whole design factors into three sets, namely highly-significant, significant, and nonsignificant factors. Then the whole design space can be correspondingly divided into three subspaces with these three sets of factors. Thereafter, SSOM is presented to sequentially optimize those subspaces. In the subspace, we present an improved SOM based on orthogonal experimental design technique to get optimal solutions. Finally, by investigating TEAM benchmark problem 22, we can see that the sampling efficiency can be improved significantly and the computational cost of finite element analysis can be saved remarkably by the proposed methods.

Multilevel Design Optimization of a FSPMM Drive System by Using Sequential Subspace Optimization Method

In our previous research, flux-switching permanent magnet machine (FSPMM) was investigated for the application in hybrid electric vehicles. To obtain the best performance of the whole drive system, a new multilevel design optimization method is presented for this kind of machine and a field oriented control system. The proposed multilevel optimization method is based on sequential subspace optimization method. In the implementation, three levels are employed to obtain the optimal design scheme at the system level. Meanwhile, sequential optimization method is employed to reduce the computation cost of finite element analysis on the motor level. Finally, from the design analysis, it can be found that the proposed method can provide design scheme with better performance, while the needed computation cost is reduced greatly for this FSPMM drive system.

An Improved Multiquadric Collocation Method for 3-D Electromagnetic Problems

The multiquadric radial basis function method (MQ RBF or, simply, MQ) developed recently is a truly meshless collocation method with global basis functions. It was introduced for solving many 1- and 2-D partial differential equations (PDEs), including linear and nonlinear problems. However, few works are found for electromagnetic PDEs, especially for 3-D problems. This paper presents an improved MQ collocation method for 3-D electromagnetic problems. Numerical results show a considerable improvement in accuracy over the traditional MQ collocation method, although both methods are direct collocation method with exponential convergence

Multiobjective Sequential Design Optimization of PM-SMC Motors for Six Sigma Quality Manufacturing

In our previous work, two kinds of permanent magnet (PM) synchronous motors, transverse flux motor (TFM) and claw pole motor, were designed and fabricated using the soft magnetic composite (SMC) cores. This paper presents multiobjective and robust design optimization for high-quality manufacturing of these PM-SMC motors to improve their industrial applications. Meanwhile, an improved multiobjective sequential optimization method is presented to reduce the computation cost. Thereafter, a PM TFM with SMC core is investigated to illustrate the performance of the proposed method. From the discussion, it can be found that six sigma quality manufacturing was achieved for all Pareto design schemes given by the proposed method. Furthermore, manufacturing cost and computation cost have been reduced a lot.

Meshless method based on orthogonal basis for computational electromagnetics

This paper discovers and researches problems on numerical oscillations of the solution in element-free Galerkin method (EFGM) when it uses high order polynomial basis, and puts forward the meshless method based on orthogonal basis (MLMBOB), which is composed of essential boundary conditions with Penalty method, then gets the numerical solutions of the partial differential equations. This method holds nearly all qualities of EFGM and removes many drawbacks of it, and it has high accuracy when high order orthogonal basis is used. Therefore, it is fit for many problems in engineering computational electromagnetics. Examples are given to prove the proposed method.

Electromagnetic Device Design Based on RBF Models and Two New Sequential Optimization Strategies

We present two new strategies for sequential optimization method (SOM) to deal with the optimization design problems of electromagnetic devices. One is a new space reduction strategy; the other is model selection strategy. Meanwhile, radial basis function (RBF) and compactly supported RBF models are investigated to extend the applied model types for SOM. Thereafter, Monte Carlo method is employed to demonstrate the efficiency and superiority of the new space reduction strategy. Five commonly used approximate models are considered for the discussion of model selection strategy. Furthermore, by two TEAM benchmark examples, we can see that SOM with the proposed new strategies and models can significantly speed the optimization design process, and the efficiency of SOM depends a little on the types of approximate models.

Improved Sequential Optimization Method for High Dimensional Electromagnetic Device Optimization

Improved sequential optimization method (SOM) and dimension reduction optimization method (DROM) are presented for high dimensional optimization design problems of electromagnetic devices. Improved SOM can simultaneously optimize the statistical approximate models and optimization algorithms more efficiently compared with SOM. Using DROM, a high dimensional problem can be converted into a low dimensional problem with expert experience or some design of experiment techniques. Then two TEAM benchmark problems (Problem 22 and Problem 25) are investigated to illustrate the efficiency of the proposed methods. From the experimental results, we can see that the presented methods can obviously reduce the computational cost of finite element analysis, while the optimal results also satisfy design specification.