Predicting Propeller Behaviour Using Machine Learning

Abstract

This thesis focuses on developing a model that effectively captures and generalizes the four quadrant behaviour of propellers, which is crucial for understanding and optimizing propulsion systems in marine vessels. Accurate prediction of four quadrant behaviour offers significant benefits to the industry, including reducing fuel consumption, mapping hull growth accurately, and identifying efficiency losses due to propeller cavitation. Unlike existing approaches that mainly concentrate on modelling the physical interactions between the water and the propeller blade, this thesis investigates different machine learning methods and finds that the ensemble method yields the best model for predicting four-quadrant behaviour. The method is tested on different the different data sets available. The data set whose propeller type is the same as the propellers used in the fleet is used to generate predictions for propellers currently in use in the fleet.