Prediction of total resistance coefficients using neural networks

Abstract

The Holtrop & Mennen method is widely used at the initial design stage of ships for estimating the resistance of the ship (Holtrop and Mennen, 1982). The Holtrop & Mennen method provide a prediction of the total resistance’s components. In this work we present a neural network model which performs the same task as the Holtrop & Mennem’s method, for two of the total resistance’s components. A multilayer perceptron has been therefore trained to learn the relationship between the input (length-displacement ratio, prismatic coefficient, longitudinal position of the centre of buoyancy, after body form and Froude number) and the target variables (form factor and wave-making and wave-breaking resistance per unit weight of displacement). The network architecture with best generalization properties was obtained through an exhaustive validation analysis (Bishop, 1995). The results of this model have been compared against those provided by the Holtrop & Mennen method, and it was found that the quality of the prediction is improved over the entire range of data. The neural network provides an accurate estimation of two total resistance’s components with Froude number and hull geometry coefficients as variables.