The objective of this thesis is to explore the possibilities of utilising machine learning to predict the axial velocities in a nominal wake field of a single-propeller vessel, given a database with only basic parameters describing hull geometries, such as length, beam, and block coefficient. The nominal wake field is of primary importance for the propeller design process, as it represents the influence of the hull on the flow field that a propeller encounters. Furthermore, propeller-hull interactions can be derived from it.

Five different machine learning models have been developed, trained and evaluated. Visual inspection of predictions made by all five models revealed that none of the models has successfully captured the underlying physical phenomena that drive the wake field: all predictions show highly generalised wake fields. This finding was supported by a feature
importance study, which showed that the features that supposedly contribute the most to understanding the hydrodynamic phenomena that drive the wake field do not have the highest relative importance.

The limited performance of the developed models is primarily attributed to two factors: the limited dataset size and a lack of feature informativeness, meaning that the used features contain insufficient information about the problem to let a model effectively capture the underlying physical phenomena. To a lesser extent, computational limitations also play a role.