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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 Cover

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

Polish Maritime Research
Volume 14 (2007): Issue 4 (October 2007)
By:   
Open Access
|Apr 2008
References

References

  1. Karppinen T.: Criteria for Seakeeping Performance Predictions, ESPOO 1987
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  2. Journée J.M.J.: Verification and Validation of Ship Motions. Program SEAWAY, Report1213a, Delft University of Technology, The Netherlands, 2001
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  3. Journée J.M.J.: Theoretical Manual of SEAWAY, Report1216a, Delft University of Technology, The Netherlands, 2001
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  4. Szelangiewicz T.: Ship's Operational Effectiveness Factor as Criterion for Cargo Ship Design Estimation, Marine Technology Transaction, Polish Academy of Sciences, Branch in Gdańsk, Vol. 11, 2000.
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DOI: https://doi.org/10.2478/v10012-007-0037-6 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
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Language: English
Page range: 31 - 39
Published on: Apr 25, 2008
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other,
Geosciences,
Atmospheric science and climatology,
Life sciences,
Life sciences, other

© 2008 Tomasz Cepowski, published by Gdansk University of Technology
This work is licensed under the Creative Commons License.

Volume 14 (2007): Issue 4 (October 2007)