Publisher DOI: 10.1016/j.oceaneng.2021.109542
Title: noiseNet: A neural network to predict marine propellers’ underwater radiated noise
Language: English
Authors: Wang, Youjiang  
Wang, Keqi 
Abdel-Maksoud, Moustafa  
Keywords: Cavitation;Machine learning;Marine propeller;Neural network;Noise
Issue Date: 15-Sep-2021
Source: Ocean Engineering 236: 109542 (2021-09-15)
Journal: Ocean engineering 
Abstract (english): 
A dedicated neural network architecture called noiseNet has been developed to predict URN (Underwater Radiated Noise) of cavitating marine propellers. The noiseNet predicts the sound pressure level at the first three blade passing frequencies with knowing the propeller geometry, ship hull wake field and working conditions. The physical mechanism of the URN generation is firstly analyzed. Thereafter, the physical knowledge about the hydrodynamics and hydroacoustics of marine propellers are used to develop the noiseNet architecture. A dataset obtained with the boundary element method and Ffowcs Williams–Hawkings acoustic analogy is used for the training and evaluation. The evaluation conducted on fully unseen cases shows a mean absolute error of 7.34 dB.
URI: http://hdl.handle.net/11420/10111
ISSN: 0029-8018
Institute: Fluiddynamik und Schiffstheorie M-8 
Document Type: Article
Project: Effiziente Methoden zur Bestimmung der vom Propeller induzierten hydroakustischen Abstrahlung 
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