A machine learning approach to improve sailboat resistance pbkp_rediction

Abstract:

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 pbkp_redict 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 pbkp_redict the total resistance of bare-hull sailboats based on machine learning. Three systematic series were used as input. The best machine learning model could pbkp_redict the total resistance efficiently even for small Froude numbers. With the presented model, the designer will have a unique tool capable of quickly pbkp_redicting the total resistance of bare-hull sailboats including at low speeds. Both the input data and the pbkp_redictive model are shared in complementary digital material.

Año de publicación:

2022

Keywords:

• Hull resistance
• Sailboat
• Ship design
• Machine learning

Fuente:

scopus