A machine learning approach to improve sailboat resistance prediction

In order to estimate the installed propulsion power aboard a boat, naval and ocean engineers make use of tools to assess the hull resistance through the water. It allows the designer to investigate the effect of changes on the hull parameters during the project's first steps when there is still freedom for modifications. The available models to predict the resistance of sailboats estimate the residual resistance, while the frictional component is calculated based on ITTC-57. This approach leads to difficulties at low speeds since the calculated frictional resistance is larger than the total resistance obtained from the experiment. Therefore, its application is restricted above a minimum speed. Moreover, the available models consist of several sub-models, one for each Froude number. The present work proposes a unique model to predict the total resistance of bare-hull sailboats based on machine learning. Three systematic series were used as input. The best machine learning model could predict the total resistance efficiently even for small Froude numbers. With the presented model, the designer will have a unique tool capable of quickly predicting the total resistance of bare-hull sailboats including at low speeds. Both the input data and the predictive model are shared in complementary digital material.

Automated summary

Naval and ocean engineers estimate the installed propulsion power aboard a boat by assessing hull resistance through the water. Existing models for predicting sailboat resistance face difficulties at low speeds. This study proposes a unique machine learning model that efficiently predicts the total resistance of bare-hull sailboats, including at low speeds.