University of Illinois Urbana-Champaign

Finite element analysis of 3D electric machine problems

Yao, Wang

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Yao_Wang.pdf

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https://hdl.handle.net/2142/16475

Description

Title
Finite element analysis of 3D electric machine problems
Author(s)
Yao, Wang
Issue Date
2010-06-22T19:37:16Z
Director of Research (if dissertation) or Advisor (if thesis)
Jin, Jianming
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2010-06-22T19:37:16Z
Keyword(s)
  • Finite element
  • Electric Machine
  • Simulation
Abstract
The objective of this research is to develop an efficient numerical simulation tool to analyze electric machine problems. As one of the largest fossil energy consumers, electric machines have to be designed properly with robust simulation tools that are capable of handling the inherent complexity, such as multiple materials, complicated geometry, eddy currents, and saturation. In this thesis, we adopt the finite element method, one of the most powerful numerical schemes, to analyze electric machine problems. Starting from Maxwell’s equations, we derive the governing equations using both magnetic vector and electric scalar potentials. We then discuss the finite element implementation, which includes the use of higher-order elements and isoparametric elements. Since electric machines usually operate in a low frequency band, it is necessary to handle the low-frequency breakdown problem properly. In this thesis, we propose two possible solutions. One is to solve the singular system directly by using an iterative solver. Another is to regularize the singular matrix with tree-cotree splitting. An algorithm of finding the minimum spanning tree is given. The two solutions have different effects on the convergence of iterative solvers, which is important to the efficiency of the simulation tool. Besides, the convergence of iterative solvers can be affected by other factors, such as different formulations and different preconditioners. In order to further improve the efficiency of the algorithm, we conduct a detailed convergence discussion. Finally, we model the nonlinear problem using the Newton-Raphson method. We utilize cubic splines and relaxation factors to improve the convergence of the Newton-Raphson iteration.
Graduation Semester
2010-5
Permalink
http://hdl.handle.net/2142/16475
Copyright and License Information
Copyright 2010 Wang Yao

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