The prediction of ship added resistance at the preliminary design stage by the use of an artificial neural network

Abstract

This article focuses on the use of an artificial neural network to estimate added resistance in regular head waves while using ship design parameters, such as length, breadth, draught or Froude number. In order to create a reliable model, only experimental data determined through model test measurements was used to train the neural network. This study showed that added wave resistance values predicted by the neural network soundly correlated with measured data and had good generalization ability. The developed neural network was presented in the form of mathematical function. This article presents examples of the use of this function to calculate added wave resistance. Functions presented here could have practical application in ship resistance analysis at the preliminary design stage.