Development of accurate motor simulator using multi-scale/ multi-physics model order reduction

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K04443
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 21010:Power engineering-related
• Research Institution: Kyoto University
• Principal Investigator: Matsuo Tetsuji 京都大学, 工学研究科, 教授 (20238976)
• Co-Investigator(Kenkyū-buntansha): 菅原 賢悟 近畿大学, 理工学部, 准教授 (50718963) ; 高橋 康人 同志社大学, 理工学部, 教授 (90434290)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 電気機器工学 / シミュレーション工学 / モデル縮約 / モータ解析

Outline of Final Research Achievements

1. Multiscale MOR (model order reduction) A material scale MOR method was incorporated into the machine scale MOR method. First, we formulated a method that incorporates the homogenization method for laminated iron cores into finite element magnetic field analysis, and developed a method to reduce it using the CLN method.
2. Multiphysics MOR The CLN method was extended to the induction heating problem. We also developed a MOR method for stepping motors and succeeded in coupled analysis with the equation of motion.
3. Induction motor MOR A behavior model of the induction motor was derived using the CLN method. Through coupled analysis with the control system, we achieved high-speed, high-precision analysis of transient phenomena during startup. We also developed a nonlinear MOR method for induction motors that takes magnetic saturation into account using the parameterized CLN method.

Free Research Field

計算電磁気学

Academic Significance and Societal Importance of the Research Achievements

これまでの電気機器のモデル縮約は静止器が中心であり，可動部を持ち，特に運動誘導起電力の評価が必要なモデル縮約法は存在しなかった。本研究は，精度を損なうことなく電磁界と機械的運動を連成することが可能なモデル縮約法を実現しており，また， 材料特性の考慮や様々な物理現象を包括的に扱うことができる，マルチスケール・マルチフィジクスモデル縮約法として他に類を見ない。