Artificial neural network model for the evaluation of added resistance of container ships in head waves

Abstract

The decrease in ship added resistance in waves fits into both the technical and operational measures proposed by the IMO to reduce the emissions of harmful gases from ships. Namely, the added resistance in waves causes an increase in fuel consumption and the emission of harmful gases in order for the ship to maintain the design speed, especially in more severe sea states. For this reason, it is very important to estimate the added resistance in waves with sufficient accuracy in the preliminary design phase. In this paper, the possibility of applying an ANN to evaluate added resistance in waves at the different sea states that the ship will encounter during navigation is investigated. A numerical model, based on the results of hydrodynamic calculations in head waves, and ANN is developed. The model can estimate the added resistance of container ships with sufficient accuracy, based on the ship characteristics, sailing speed, and the sea state using two wave energy spectra.