RESISTANCE AND TRIM MODELING OF THE NAPLES HARD CHINE SYSTEMATIC SERIES

Abstract

An Artificial Neural Network (ANN) method with multiple-outputs is used to develop the mathematical models for the Naples Systematic Series (NSS) of resistance, dynamic trim, wetted surface area and length of wetted surface area, as functions of length beam ratio, slenderness ratio, longitudinal centre of gravity and volumetric Froude number. Multiple ANN output enables simultaneous use of all the available resistance and trim data, producing both an output for resistance and for trim. Similar results are obtained for the wetted surface area and length of wetted surface area datasets. Note that the multiple-output models share a common ANN structure, with only slight differences in equations for resistance and trim, and for wetted surface area and length of wetted surface area. *This paper is upgraded and corrected version of a paper published under the same title at the 11th High Speed Marine Vehicles Conference (HSMV 2017) in Naples, 25th -26th October 2017.