Journal
OCEAN ENGINEERING
Volume 165, Issue -, Pages 528-537Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2018.07.035
Keywords
Artificial Neural Networks; Multi-layer perceptron; MARAD Systematic Series; Resistance prediction; Calm water resistance
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The present study investigates the use of Artificial Neural Networks (ANNs) for the resistance prediction of hullforms designed according to the MARAD Systematic Series. This series comprises 16 full hullforms, specifically designed for use as bulk carriers and tankers. Experimental data for the residual resistance coefficient of these hulls provided by MARAD in a series of diagrams have been used to train and evaluate a series of neural networks aiming to estimate the residual resistance coefficient of ships designed according to the MARAD Series. The adopted procedure along with the obtained results are presented and discussed.
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