Machine Learning for Computation of Wave Added Resistance

Mostafa Amini-Afshar*, Malte Mittendorf, Harry B. Bingham

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

We present a machine learning model for calculation of wave added resistance. The model training is performed using a large set of pre-calculated added resistance curves covering a broad range of ship hulls and operational conditions, i.e. forward speed, draft and relative wave heading. The underlying hydrodynamic model is the classical strip-theory where the wave added resistance is computed according to a modified version of Salvesen’s formulation. It is concluded that the developed data-driven model is able to produce a non-linear mapping between a set of operational conditions as well as the ship’s main particulars to the wave added resistance coefficient.
Original languageEnglish
Title of host publicationProceedings of 40th International Workshop on Water Waves and Floating Bodies
Number of pages4
PublisherIWWWFB
Publication date2025
Publication statusPublished - 2025
Event40th International Workshop on Water Waves and Floating Bodies - Shanghai, China
Duration: 11 May 202514 May 2025

Conference

Conference40th International Workshop on Water Waves and Floating Bodies
Country/TerritoryChina
CityShanghai
Period11/05/202514/05/2025

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