Flow field data mining of Pareto-Optimal Airfoils using proper orthogonal decomposition

Akira Oyama, Paul C. Verburg, Taku Nonomura, Harry W.M. Hoeijmakers, Kozo Fujii

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

11 Citations (Scopus)

Abstract

The capability of a proper-orthogonal-decomposition-based data mining approach for the analysis of flow field data of Pareto-optimal solutions is demonstrated. This method enables a designer to extract design knowledge by examining baseline data and a limited number of eigenvectors and orthogonal base vectors. The flow data analyzed herein are the pressure field data of the Pareto-optimal solutions of an aerodynamic transonic airfoil shape optimization problem. The results of the present study indicate that the proper-orthogonal- decomposition-based data mining approach is useful for extracting design knowledge from the flow field data of the Pareto-optimal solutions.

Original languageEnglish
Title of host publication48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781600867392
DOIs
Publication statusPublished - 2010
Externally publishedYes

Publication series

Name48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

ASJC Scopus subject areas

  • Aerospace Engineering

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