High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm (Articolo in rivista)

Type
Label
  • High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1080/0305215X.2014.895340 (literal)
Alternative label
  • Chen, Xi; Diez, Matteo; Kandasamy, Manivannan; Zhang, Zhiguo; Campana, Emilio Fortunato; Stern, Frederick (2015)
    High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm
    in Engineering optimization (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Chen, Xi; Diez, Matteo; Kandasamy, Manivannan; Zhang, Zhiguo; Campana, Emilio Fortunato; Stern, Frederick (literal)
Pagina inizio
  • 473 (literal)
Pagina fine
  • 494 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/record/display.url?eid=2-s2.0-84896418939&origin=inward (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 47 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 4 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Huazhong University of Science and Technology; University of Iowa; Consiglio Nazionale delle Ricerche (literal)
Titolo
  • High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm (literal)
Abstract
  • Advances in high-fidelity shape optimization for industrial problems are presented, based on geometric variability assessment and design-space dimensionality reduction by Karhunen-Loève expansion, metamodels and deterministic particle swarm optimization (PSO). Hull-form optimization is performed for resistance reduction of the high-speed Delft catamaran, advancing in calm water at a given speed, and free to sink and trim. Two feasible sets (A and B) are assessed, using different geometric constraints. Dimensionality reduction for 95% confidence is applied to high-dimensional free-form deformation. Metamodels are trained by design of experiments with URANS; multiple deterministic PSOs achieve a resistance reduction of 9.63% for A and 6.89% for B. Deterministic PSO is found to be effective and efficient, as shown by comparison with stochastic PSO. The optimum for A has the best overall performance over a wide range of speed. Compared with earlier optimization, the present studies provide an additional resistance reduction of 6.6% at 1/10 of the computational cost. © 2014 © 2014 Taylor & Francis. (literal)
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