Data-driven Underwater Radiated Noise Modelling of Cavitating Marine Propellers

The potential impact of ships underwater radiated noise (URN) on marine fauna has become an important issue. The most dominant noise source on a propeller-driven vessel is propeller cavitation, and the accurate prediction of its noise signature is fundamental for the design process. In this work, we investigate the potential of using Machine Learning methods for the prediction of URN from cavitating marine propellers that can be conveniently implemented within the design process. We compare computational and experimental results on a subset of the Meridian standard propeller series, behind different severities of axial wake, for a total of 432 experiments.