Application of machine learning to design low noise propellers

Doctoral Thesis (2022)
Author(s)

P.S. Doijode (TU Delft - Ship Design, Production and Operations)

Contributor(s)

T. J.C. van Terwisga – Promotor (TU Delft - Ship Hydromechanics and Structures)

S Hickel – Promotor (TU Delft - Aerodynamics)

Klaas Visser – Copromotor (TU Delft - Ship Design, Production and Operations)

Research Group
Ship Design, Production and Operations
Copyright
© 2022 P.S. Doijode
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 P.S. Doijode
Research Group
Ship Design, Production and Operations
ISBN (print)
978-94-6419-576-7
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Over 90% of international trade is carried out over seas. Shipping is currently the cheapest mode of transoceanic transport. The traffic density of shipping lanes on seas, oceans, and also rivers is likely to increase. Consequently, the GHG, NOx, SOx and noise emissions from shipping will rise making it more difficult to meet stricter emission regulations which the IMO aims to achieve. One opportunity to reduce emissions is by designing more efficient and quieter propellers.

To design quieter and more efficient propellers an optimal blade loading solution is required. For a rigid propeller, the blade loading distribution is optimized by modifying the geometry. The propeller geometry must be modified to achieve optimal loading that maximizes efficiency and minimizes acoustic emissions. In addition to efficiency and noise considerations, propeller optimization must consider thrust, ship speed, fairing constraints as well as unsteady wake of the vessel....

Files

Dissertation.pdf
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