Hull and Propeller Performance Decomposition via an Adaptive Machine Learning Framework

Malte Mittendorf, Ulrik Dam Nielsen, Ditte Gundermann

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

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Abstract

This paper deals with a machine learning methodology for decomposing hull and propeller performance. This is facilitated by an ensemble approach predicting both propeller revolutions and shaft power under consistent simulated reference conditions. The derived performance estimates are compared to ISO 19030 and a semi-empirical framework aimed at isolating propeller performance. The case ship is a >300 m cruise vessel sailing in the Caribbean Sea. The auto-logged sensor data covers seven years with three dry-docking intervals and numerous in-water cleaning events. It is concluded that the separation of propeller performance is subject to multiple uncertainty sources and can thus not be reliably assessed. The practical relevance and potential shortcomings of the method are discussed.
Original languageEnglish
Title of host publicationProceedings of the 8th Hull Performance & Insight Conference (HullPIC’23)
EditorsVolker Bertram
Publication date2023
Pages70-82
Publication statusPublished - 2023
Event8th Hull Performance & Insight Conference (HullPIC) - Pontignano, Italy
Duration: 28 Aug 202330 Aug 2023

Conference

Conference8th Hull Performance & Insight Conference (HullPIC)
Country/TerritoryItaly
CityPontignano
Period28/08/202330/08/2023

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