Multi-objective robust design optimization and knowledge mining of a centrifugal fan that takes dimensional uncertainty into account

Kazuyuki Sugimura, Shinkyu Jeong, Shigeru Obayashi, Takeshi Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

A new design approach named MORDE (multi-objective robust design exploration), in which multi-objective robust optimization techniques and data mining techniques are combined, is proposed in this paper. We first developed a widely applicable design framework for multi-objective robust optimization. In this framework, probabilistic representation of design variables are introduced and Kriging models are used to approximate relations between design variables with uncertainty and multiple design objectives. A multi-objective genetic algorithm optimizes the mean and standard deviation of the responses. We then applied the framework to the real-world design problem of a centrifugal fan used in a washer-dryer. Taking dimensional uncertainty into account, we optimized the means and standard deviations of the resulting distributions of fan efficiency and turbulent noise level. Steady Reynolds-averaged Navier Stokes simulations were used to build Kriging models that approximate these objective functions. With the obtained non-dominated solutions, we demonstrated how to analyze features of solutions and select design candidates. We also attempted to acquire design knowledge by applying several data mining techniques. Self-organizing map was used to visualize and reuse the high dimensional design data. Decision tree analysis and rough set theory were used to extract design rules to improve the product's performance. We also discussed differences in types of rules, which were extracted by both methods.

Original languageEnglish
Title of host publication2008 Proceedings of the ASME Turbo Expo
Subtitle of host publicationPower for Land, Sea, and Air
Pages2659-2668
Number of pages10
EditionPART C
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 ASME Turbo Expo - Berlin, Germany
Duration: 2008 Jun 92008 Jun 13

Publication series

NameProceedings of the ASME Turbo Expo
NumberPART C
Volume6

Other

Other2008 ASME Turbo Expo
CountryGermany
CityBerlin
Period08/6/908/6/13

Keywords

  • Centrifugal fan
  • Data mining
  • Decision tree
  • Genetic algorithm
  • Kriging model
  • Multi-objective robust optimization
  • Rough set
  • Self-organizing map
  • Taguchi method

ASJC Scopus subject areas

  • Engineering(all)

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