A Novel Constrained Latin Hypercube Sampling for Electric Machine

Jinlong Li, Nadia M.L. Tan, Zhuang Xu, Weinong Fu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

It is important to accurately construct a data-driven geometric model for the electric machine (EM) to avoid parts overlapping, which can result in the failure of performance analysis using finite element method (FEM) tools. This paper presents a novel approach that combines Latin hypercube sampling (LHS) and geometric repair operators to generate samples for the design of experiments (DoE). Firstly, the parametric geometric model, constraints, and the corresponding geometric reparations for EM are introduced. Then, the proposed approach is employed to address the geometric constraints when constructing the EM models for DoE. The proposed approach is numerically validated using a surface mounted permanent magnet synchronous machine, demonstrating the effectiveness in improving space-filling characteristics and feasibility through constrained LHS. Furthermore, the correlation analysis results based on the DoE can be used to reduce the design variables for multi-objective optimization and support surrogate modelling for EM performance prediction.

Original languageEnglish
Title of host publicationIEACon 2023 - 2023 IEEE Industrial Electronics and Applications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-74
Number of pages6
ISBN (Electronic)9798350347517
DOIs
Publication statusPublished - 2023
Event4th IEEE Industrial Electronics and Applications Conference, IEACon 2023 - Penang, Malaysia
Duration: 6 Nov 20237 Nov 2023

Publication series

NameIEACon 2023 - 2023 IEEE Industrial Electronics and Applications Conference

Conference

Conference4th IEEE Industrial Electronics and Applications Conference, IEACon 2023
Country/TerritoryMalaysia
CityPenang
Period6/11/237/11/23

Keywords

  • Constrained LHS
  • Correlation analysis
  • Geometric repair operator
  • Parametric model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Control and Optimization

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