Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloAssessing the Interplay of Shape and Physical Parameters by Unsupervised Nonlinear Dimensionality Reduction Methods
Anno di pubblicazione2019
FormatoElettronico
Autore/iAndrea Serani(1); Danny D'Agostino(1,2); Emilio F. Campana(1); Matteo Diez(1)
Affiliazioni autori(1)CNR-INM, Natl. Research Council-Institute of Marine Engineering, Rome, Italy (2)Department of Computer, Control, and Management Engineering "A. Ruberti", Sapienza University of Rome, Rome, Italy
Autori CNR e affiliazioni
  • MATTEO DIEZ
  • ANDREA SERANI
  • EMILIO FORTUNATO CAMPANA
Lingua/e
  • inglese
AbstractThe article presents an exploratory study on the application to ship hydrodynamics of unsupervised nonlinear design-space dimensionality reduction methods, assessing the interaction of shape and physical parameters. Nonlinear extensions of the principal component analysis (PCA) are applied, namely local PCA (LPCA) and kernel PCA (KPCA). An artificial neural network approach, specifically a deep autoencoder (DAE) method, is also applied and compared with PCA-based approaches. The data set under investigation is formed by the results of 9000 potential flow simulations coming from an extensive exploration of a 27-dimensional design space, associated with a shape optimization problem of the DTMB 5415 model in calm water at 18 kn (Froude number, Fr = .25 ). Data include three heterogeneous distributed and suitably discretized parameters (shape modification vector, pressure distribution on the hull, and wave elevation pattern) and one lumped parameter (wave resistance coefficient), for a total of 9000 × 5101 elements. The reduced-dimensionality representation of shape and physical parameters is set to provide a normalized mean squared error smaller than 5%. The standard PCA meets the requirement using 19 principal components/parameters. LPCA and KPCA provide the most promising compression capability with 14 parameters required by the reduced-dimensionality parametrizations, indicating significant nonlinear interactions in the data structure of shape and physical parameters. The DAE achieves the same error with 17 components. Although the focus of the current work is on design-space dimensionality reduction, the formulation goes beyond shape optimization and can be applied to large sets of heterogeneous physical data from simulations, experiments, and real operation measurements.
Lingua abstractinglese
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RivistaJournal of ship research (Online)
Attiva dal 1957
Editore: Society of Naval Architects and Marine Engineers - Jersey City, NJ
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1542-0604
Titolo chiave: Journal of ship research (Online)
Titolo proprio: Journal of ship research (Online)
Titolo abbreviato: J. ship res. (Online)
Numero volume della rivista-
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DOI10.5957/JOSR.09180056
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiavenonlinear dimensionality reduction methods, ship hydrodynamics, unsupervised machine learning
Link (URL, URI)https://www.ingentaconnect.com/content/sname/jsr/pre-prints/content-josr_09180056a
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione02/10/2019
Note/Altre informazioni-
Strutture CNR
  • INM — Istituto di iNgegneria del Mare
Moduli/Attività/Sottoprogetti CNR
  • ET.P02.017.002 : Ottimizzazione dei veicoli marini
Progetti Europei-
Allegati
Assessing the Interplay of Shape and Physical Parameters by Unsupervised Nonlinear Dimensionality Reduction Methods (documento privato )
Descrizione: correct proof
Tipo documento: application/pdf