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

• Autorzy: Cepowski T.
• Języki publikacji: EN

Abstrakty

EN

This paper presents an application of artificial neural networks to approximation andidentification of additional wave-generated resistance, slamming and internal forcesdepending 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 viewof their accuracy against standard values. Possible ways of application of the artificialneural networks were examined from the point of view of accuracy of approximation and identification ofthe assumed ship response parameters

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2007
• Tom: 14
• Numer: 4
• Opis fizyczny: p.31-39,fig.,ref.

Twórcy

autor

Cepowski T.

• Institute of Marine Navigation, Maritime University of Szczecin, Waly Chrobrego 1/2, 70-500 Szczecin, Poland

Bibliografia

• 1. Karppinen T.: Criteria for Seakeeping Performance Predictions, ESPOO 1987
• 2. Journée J.M.J.: Verification and Validation of Ship Motions.Program SEAWAY, Report1213a, Delft University of Technology, The Netherlands, 2001
• 3. Journée J.M.J.: Theoretical Manual of SEAWAY, Report1216a, Delft University of Technology, The Netherlands, 2001
• 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.