Abstract
The noise reduction technology of ship propellers is currently one of the challenging directions, despite some progress having been made, continuous research and development are still underway. This paper elucidates commonly used noise reduction methods, namely geometric structure optimization and material optimization. Geometric structure optimization involves aspects such as the number of propeller blades, disk loading ratio, skew angle, and blade shape. Material optimization encompasses material selection, surface coating optimization, and propeller duct design. Corresponding optimization methods are provided for both approaches. A novel and innovative research direction is proposed, leveraging machine learning and neural networks to optimize propeller parameters. Additionally, employing a circumferential friction nanogenerator to sense propeller bearings and identify the coupling relationship between electrical signals and factors such as roughness and rotational speed. This optimization aims to reduce noise by optimizing propeller parameters. The paper concludes by offering insights for current research on ship propeller noise reduction technology in China.
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