On the use of Artificial Neural Networks for the calm water resistance prediction of MARAD Systematic Series’ hullforms

Abstract 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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