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The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions
Abstract
Decision support systems (DSS) recently have been increasingly in use during ships operation. They require realistic input data regarding different aspects of navigation. To address the optimal weather routing of a ship, which is one of the most promising field of DSS application, it is necessary to accurately predict an actually attainable speed of a ship and corresponding fuel consumption at given loading conditions and predicted weather conditions. In this paper, authors present a combined calculation method to predict those values. First, a deterministic modeling is applied and then an artificial neural network (ANN) is structured and trained to quickly mimic the calculations. The sensitivity of the ANN to adopted settings is analyzed as well. The research results confirm a more than satisfactory quality of reproduction of speed and fuel consumption data as the ANN response meet the calculation results with high accuracy. The ANN-based approach, however, requires a significantly shorter time of execution. The directions of future research are outlined.
Citations
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Authors (5)
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Roberto Vettor
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Hubert Krata
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Carlos Guedes Soares
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.12716/1001.15.02.23
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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TransNav - The International Journal on Marine Navigation and Safety of Sea Transportation
no. 15,
pages 437 - 444,
ISSN: 2083-6473 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Krata P., Kniat A., Vettor R., Krata H., Soares C. G.: The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions// TransNav - The International Journal on Marine Navigation and Safety of Sea Transportation -,iss. 15(2) (2021), s.437-444
- DOI:
- Digital Object Identifier (open in new tab) 10.12716/1001.15.02.23
- Verified by:
- Gdańsk University of Technology
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