Surrogate-based multi-objective optimization and data mining of vortex generators on a transonic infinite-wing

Nobuo Namura, Shigeru Obayashi, Shinkyu Jeong

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

1 Citation (Scopus)

Abstract

Multi-objective optimization and data mining of vortex generators (VGs) on a transonic infinite-wing was performed using computational fluid dynamics (CFD), surrogate models, and a multi-objective genetic algorithm (MOGA). VGs arrangements were defined by five design variables: height, length, incidence angle, spacing, and chord location. The objective functions which should be maximized were three: lift-drag ratio at low angle of attack, lift coefficient at high angle of attack, and chordwise separation location at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the response surface methodology with Kriging model and the modified version of it was employed because CFD analysis of the wing with VG requires a large computational time. Two types of data mining method: analysis of variance (ANOVA) and self-organizing map (SOM), were applied to the result of the optimization. It was revealed by ANOVA that the ratio of spacing to height and the incidence angle had significant influences to maximizing each objective function. By using SOM, VG designs were split into four types which have different aerodynamic characteristics respectively. The appropriate values of parameters were identified by SOM.

Original languageEnglish
Title of host publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages2910-2917
Number of pages8
DOIs
Publication statusPublished - 2013 Aug 21
Event2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, Mexico
Duration: 2013 Jun 202013 Jun 23

Publication series

Name2013 IEEE Congress on Evolutionary Computation, CEC 2013

Other

Other2013 IEEE Congress on Evolutionary Computation, CEC 2013
CountryMexico
CityCancun
Period13/6/2013/6/23

Keywords

  • Kriging model
  • analysis of variance
  • computational fluid dynamics
  • multi-objective genetic algorithm
  • radial basis function networks
  • self-organizing map

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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