Data mining for aerodynamic design space

Shinkyu Jeong, Kazuhisa Chiba, Shigeru Obayashi

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

Analysis of variance (ANOVA) and self-organizing map (SOM) were applied to data mining for aerodynamic design space. These methods make it possible to identify the effect of each design variable on objective functions. ANOVA shows the information quantitatively, while SOM shows it qualitatively. Furthermore, ANOVA can show the effects of interaction between design variables on objective functions and SOM can visualize the trade-offs among objective functions. This information will be helpful for designers to determine the final design from non-dominated solutions of multi-objective problems. These methods were applied to two design results: a fly-back booster in reusable launch vehicle design, which has 4 objective functions and 71 design variables, and a transonic airfoil design performed with the adaptive search region method.

Original languageEnglish
Pages (from-to)452-469
Number of pages18
JournalJournal of Aerospace Computing, Information and Communication
Volume2
Issue number11
DOIs
Publication statusPublished - 2005

ASJC Scopus subject areas

  • Aerospace Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Data mining for aerodynamic design space'. Together they form a unique fingerprint.

Cite this