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Application of artificial neural networks to approximation and identification of sea-keeping performance of a bulk carrier in ballast loading condition

Abstract

This paper presents an application of artificial neural networks to approximation and identification of additional wave-generated resistance, slamming and internal forces depending on ship motion and wave parameters. The analysis was performed for a typical bulk carrier in ballast loading conditions. The investigations were carried out on the basis of ship response data calculated by means of exact numerical methods. Analytical functions presented in the form of artificial neural networks were analyzed with a view of their accuracy against standard values. Possible ways of application of the artificial neural networks were examined from the point of view of accuracy of approximation and identification of the assumed ship response parameters.

Keywords:

ship, ship sea keeping qualities, artificial neural networks, slamming, additional wave generated resistance, internal forces, shear forces, bending moments, wave parameters, approximation, identification, assessment

Details

Issue
Vol. 14 No. 4(54) (2007)
Section
Latest Articles
Published
25-04-2008
DOI:
https://doi.org/10.2478/v10012-007-0037-6
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Author Biography

Tomasz Cepowski,
Maritime University of Szczecin, Institute of Marine Navigation



Authors

Tomasz Cepowski

Maritime University of Szczecin, Institute of Marine Navigation

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