Flow Field Data Mining of Pareto-Optimal Airfoils Using Proper Orthogonal Decomposition

Autor:	Oyama, Akira, Verburg, Paul C., Nonomura, Taku, Hoeijmakers, Harry W.M., Fuji, Kozo, Muellner, George K.
Rok vydání:	2010
Předmět:	Airfoil Mathematical optimization Computer science Aerodynamics Base (topology) computer.software_genre Design knowledge Flow field Physics::Fluid Dynamics Flow (mathematics) Data mining Transonic computer Eigenvalues and eigenvectors
Zdroj:	Proceedings 48th AIAA Aerospace Sciences Meeting 2010: Including the New Horizons Forum and Aerospace Exposition, 1-11 STARTPAGE=1;ENDPAGE=11;TITLE=Proceedings 48th AIAA Aerospace Sciences Meeting 2010 Scopus-Elsevier
DOI:	10.2514/6.2010-1140
Popis:	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-orthogonaldecomposition-based data mining approach is useful for extracting design knowledge from the flow field data of the Pareto-optimal solutions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed0a37bf3d1825a88c7d2e2e15481057 https://doi.org/10.2514/6.2010-1140 Zobrazit plný text záznamu