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ニューラルネットワークを用いた船体撓み振動の予測
https://kindai.repo.nii.ac.jp/records/7290
https://kindai.repo.nii.ac.jp/records/7290320d6ac3-2c43-407e-a20a-0b5d16adad63
名前 / ファイル | ライセンス | アクション |
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AN00063799-20081220-0069.pdf (864.1 kB)
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Item type | ☆紀要論文 / Departmental Bulletin Paper(1) | |||||
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公開日 | 2010-07-02 | |||||
タイトル | ||||||
タイトル | ニューラルネットワークを用いた船体撓み振動の予測 | |||||
その他(別言語等)のタイトル | ||||||
その他のタイトル | Estimation of Hull Vibration Using Neural Network | |||||
著者 |
奥本, 泰久
× 奥本, 泰久× 河村, 健太郎 |
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言語 | ||||||
言語 | jpn | |||||
キーワード | ||||||
主題 | Hull vibration, Natural frequency, Neural network, Prediction Resonance | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | departmental bulletin paper | |||||
著者(英) | ||||||
en | ||||||
Okumoto, Yasuhisa | ||||||
著者(英) | ||||||
en | ||||||
Kawamura, Kentaro | ||||||
著者 所属 | ||||||
近畿大学工学部知能機械工学科 | ||||||
著者 所属 | ||||||
近畿大学工学部知能機械工学科 | ||||||
著者所属(翻訳) | ||||||
Department of Intelligent Mechanical Engineering, School of Engineering, Kinki University | ||||||
著者所属(翻訳) | ||||||
Department of Intelligent Mechanical Engineering, School of Engineering, Kinki University | ||||||
版 | ||||||
出版タイプ | NA | |||||
出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||
出版者 名前 | ||||||
出版者 | 近畿大学工学部 | |||||
書誌情報 |
近畿大学工学部研究報告 en : Research reports of the School of Engineering, Kinki University 号 42, p. 69-72, 発行日 2008-12-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0386491X | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Ships often vibrate due to the excitation of propeller, main engine, etc. The severe case is the flexural vibration of the whole body as a hull girder system by resonance with such excitation forces. When the natural frequency of the hull girder vibration harmonizes with the excitation force, the heavy vibration occurs by the resonance, resulting the defects of structural members and also unpleasantness for the crew on board. Though there is the difficulty of the prediction of the natural frequencies, the accurate estimation is essential to avoid the resonance in early design stage. There have been two methods to predict the natural frequencies until now: using a simple formula combined with the past measurements, and using FEM (Finite Element Method) analysis of the whole ship body model. The former has less accuracy, and the latter needs high engineering skill and much computer time, then this research applied a new method in mid-point between two, using the neural network, The calculation focused on a conventional crude oil tanker as an example. As a result, the estimated frequencies agreed well with the measurement data, either the sample (teaching) data or arbitrary ship data. By this, anyone can easily estimate the frequertcies. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |