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 language | English |
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Title of host publication | Proceedings of the 8th Hull Performance & Insight Conference (HullPIC’23) |
Editors | Volker Bertram |
Publication date | 2023 |
Pages | 70-82 |
Publication status | Published - 2023 |
Event | 8th Hull Performance & Insight Conference (HullPIC) - Pontignano, Italy Duration: 28 Aug 2023 → 30 Aug 2023 |
Conference
Conference | 8th Hull Performance & Insight Conference (HullPIC) |
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Country/Territory | Italy |
City | Pontignano |
Period | 28/08/2023 → 30/08/2023 |