船舶螺旋桨降噪研究:先进材料和创新几何设计

Zhaowen Zhang
{"title":"船舶螺旋桨降噪研究:先进材料和创新几何设计","authors":"Zhaowen Zhang","doi":"10.54254/2755-2721/61/20240964","DOIUrl":null,"url":null,"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.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on ship propeller noise reduction: Advanced materials and innovative geometric design\",\"authors\":\"Zhaowen Zhang\",\"doi\":\"10.54254/2755-2721/61/20240964\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":350976,\"journal\":{\"name\":\"Applied and Computational Engineering\",\"volume\":\" 10\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied and Computational Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54254/2755-2721/61/20240964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54254/2755-2721/61/20240964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

船舶螺旋桨的降噪技术是目前具有挑战性的方向之一,尽管已经取得了一些进展,但仍在继续研究和开发。本文阐述了常用的降噪方法,即几何结构优化和材料优化。几何结构优化涉及螺旋桨叶片数量、盘载荷比、倾斜角和叶片形状等方面。材料优化包括材料选择、表面涂层优化和螺旋桨管道设计。两种方法都提供了相应的优化方法。提出了一个新颖的创新研究方向,即利用机器学习和神经网络来优化螺旋桨参数。此外,采用圆周摩擦纳米发电机来感知螺旋桨轴承,并确定电信号与粗糙度和转速等因素之间的耦合关系。这种优化旨在通过优化螺旋桨参数来降低噪音。本文最后对中国目前的船舶螺旋桨降噪技术研究提出了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Research on ship propeller noise reduction: Advanced materials and innovative geometric design
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Implementation of seamless assistance with Google Assistant leveraging cloud computing Deep learning vulnerability analysis against adversarial attacks Comparison of deep learning models based on Chest X-ray image classification DOA estimation technology based on array signal processing nested array Precise positioning and prediction system for autonomous driving based on generative artificial intelligence
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1