The article presents an exploratory study on the application to ship hydrodynamics of unsupervised nonlinear design-space dimensionality reductionmethods, 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 comparedwith PCA-based approaches. The data set under investigation is formed by the results of 9000 potential flow simulations coming froman 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 x 5101 elements. The reduced-dimensionality representation of shape and physical parameters is set to provide a normalizedmean 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

Assessing the interplay of shape and physical parameters by unsupervised nonlinear dimensionality reduction methods / Serani, Andrea; D'Agostino, Danny; Fortunato Campana, Emilio; Diez, Matteo. - In: JOURNAL OF SHIP RESEARCH. - ISSN 0022-4502. - 64:4(2020), pp. 313-327. [10.5957/JOSR.09180056]

Assessing the interplay of shape and physical parameters by unsupervised nonlinear dimensionality reduction methods

Danny D'Agostino;
2020

Abstract

The article presents an exploratory study on the application to ship hydrodynamics of unsupervised nonlinear design-space dimensionality reductionmethods, 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 comparedwith PCA-based approaches. The data set under investigation is formed by the results of 9000 potential flow simulations coming froman 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 x 5101 elements. The reduced-dimensionality representation of shape and physical parameters is set to provide a normalizedmean 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
2020
Dimensionality Reduction; Unsupervised Learning; Shape Optimization;
01 Pubblicazione su rivista::01a Articolo in rivista
Assessing the interplay of shape and physical parameters by unsupervised nonlinear dimensionality reduction methods / Serani, Andrea; D'Agostino, Danny; Fortunato Campana, Emilio; Diez, Matteo. - In: JOURNAL OF SHIP RESEARCH. - ISSN 0022-4502. - 64:4(2020), pp. 313-327. [10.5957/JOSR.09180056]
File allegati a questo prodotto
File Dimensione Formato  
Serani_preprint_Assessing-the-Interplay_2020.pdf

accesso aperto

Note: https://doi.org/10.5957/JOSR.09180056
Tipologia: Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.38 MB
Formato Adobe PDF
4.38 MB Adobe PDF
Serani_Assessing-the-Interplay_2020.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.1 MB
Formato Adobe PDF
3.1 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1493170
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact