Using machine learning to understand the behavior of added resistance of a ship in waves - Ong, Rayner Junjie (Nanyang Technological University, 2020)
Ships experience resistances of various origins. The influence of the added resistance of a ship in waves is significant enough not to be ignored. The analysis drawn to attention identifies the different relations between the added resistance and the various factors of contribution factors, direct and indirect. These factors range from the ship profile to wave character. This report analyses the waterline of the vessel at a given angle of wave. At given angles, part of the waterline will only be exposed. Therefore, this report seeks to identify and enhance on the current diffraction calculation method. This report will also attempt to work on relationship between the diffraction added resistance and wave spectra theories employed by different theories. Experimental data collected are processed using machine learning tools to aid the in-depth analysis of the added resistance.