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Pregled bibliografske jedinice broj: 828093

Application of pattern recognition method for estimating wind loads on ships and marine objects


Valčić, Marko; Prpić-Oršić, Jasna; Vučinić, Dean
Application of pattern recognition method for estimating wind loads on ships and marine objects // 10th International Conference on Advanced Computational Engineering and Experimenting ACE-X 2016
Split, Hrvatska, 2016. (predavanje, međunarodna recenzija, pp prezentacija, znanstveni)


Naslov
Application of pattern recognition method for estimating wind loads on ships and marine objects

Autori
Valčić, Marko ; Prpić-Oršić, Jasna ; Vučinić, Dean

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, pp prezentacija, znanstveni

Skup
10th International Conference on Advanced Computational Engineering and Experimenting ACE-X 2016

Mjesto i datum
Split, Hrvatska, 3.-6.7.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Pattern recognition; wind loads; ships

Sažetak
This paper presents an extension of application capabilities of elliptic Fourier descriptors (EFDs) from the usual pattern recognition and classification problems to problems of very complex nonlinear multivariable approximations of multi-input and multi-output (MIMO) functions. Wind loads on ships and marine objects are a complicated phenomenon because of the complex configuration of the above-water part of the structure. The proposed approach of wind load estimation method presented in this paper consists of four basic parts: acquisition and pre-processing of vessel images ; image editing ; data preparation for neural network training ; validating and testing of created neural network. The method is based on elliptic Fourier features of a closed contour which are used for ship frontal and lateral closed contour representation. Therefore, this approach takes into account all aspects of the variability of the above-water frontal and lateral ship profile. For the purpose of multivariate nonlinear regression, the generalized regression radial basis neural network is trained by elliptic Fourier features of closed contours and wind load data derived from wind tunnel tests. The trained neural network is used for the estimation of non-dimensional wind load coefficients. The results for a group of car carriers are presented and compared with the experimental data.

Izvorni jezik
Engleski

Znanstvena područja
Brodogradnja



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2013-11-8722 - Ekološki pristup projektiranju broda i optimalnom planiranju rute (Jasna Prpić-Oršić, )

Ustanove
Tehnički fakultet, Rijeka,
Pomorski fakultet, Rijeka