Design-space dimensionality reduction in shape optimization by Karhunen-Loeve expansion (Articolo in rivista)

Type
Label
  • Design-space dimensionality reduction in shape optimization by Karhunen-Loeve expansion (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.cma.2014.10.042 (literal)
Alternative label
  • Diez, Matteo; Campana, Emilio F.; Stern, Frederick (2015)
    Design-space dimensionality reduction in shape optimization by Karhunen-Loeve expansion
    in Computer methods in applied mechanics and engineering
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Diez, Matteo; Campana, Emilio F.; Stern, Frederick (literal)
Pagina inizio
  • 1525 (literal)
Pagina fine
  • 1544 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 283 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 20 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Consiglio Nazionale delle Ricerche (CNR); University of Iowa (literal)
Titolo
  • Design-space dimensionality reduction in shape optimization by Karhunen-Loeve expansion (literal)
Abstract
  • The paper presents a methodology to reduce the dimension of design spaces in shape optimization problems, while retaining a desired level of geometric variance. The method is based on a generalized Karhunen-Loeve expansion (KLE). Arbitrary shape modification spaces are assessed in terms of Karhunen-Loeve modes (eigenvectors) and associated geometric variance (eigenvalues). The former are used as a basis in order to build a reduced-dimensionality representation of the shape modification. The method is demonstrated for the shape optimization of a high-speed catamaran, based on CFD simulations and aimed at the reduction of the wave component of calm-water resistance. KLE is applied to three design spaces with large dimensionality (>= 20), based on a free form deformation technique. The space with the largest geometric variance is selected for dimensionality reduction and design optimization. N-dimensional design spaces are used, with N = 1, 2, 3, and 4, retaining up to the 95% of the geometric variance associated to the original space. The correlation between the objective reduction achieved, the dimension N and the geometric variance of the reduced-dimensionality space is shown and found significant. (C) 2014 Elsevier B.V. All rights reserved. (literal)
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