English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 55208/89501 (62%)
造訪人次 : 10716794      線上人數 : 22
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50456


    題名: Prediction of Flutter Derivatives by Artificial Neural Networks
    作者: Chen, Chern-hwa;吳重成;Wu, Jong-cheng;Chen, Jow-hua
    貢獻者: 淡江大學土木工程學系
    關鍵詞: Artificial neural network;Flutter derivative;Rectangular section model;Wind tunnel test
    日期: 2008-10
    上傳時間: 2010-08-09 17:56:42 (UTC+8)
    出版者: Amsterdam: Elsevier BV
    摘要: This study presents an approach using artificial neural networks (ANN) algorithm for predicting the flutter derivatives of rectangular section models without wind tunnel tests. Firstly, a database of flutter derivatives is identified from a back-propagation (BP) ANN model that is built using experimental dynamic responses of rectangular section models in smooth flow as the input/output data. Then, these limited sets of database are employed as input/output data to establish a prediction ANN frame model to further predict the flutter derivatives for other rectangular section models without conducting wind tunnel tests. The results presented indicate that this ANN prediction scheme works reasonably well. Therefore, instead of going through wind tunnel tests, this ANN approach provides a convenient and feasible option for expanding the flutter derivative database that can help to determine an appropriate basic shape of the bridge section in the preliminary design.
    關聯: Journal of Wind Engineering and Industrial Aerodynamics 96(10-11), pp.1925-1937
    DOI: 10.1016/j.jweia.2008.02.044
    顯示於類別:[土木工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    0167-6105_96(10-11)p1925-1937.pdf451KbAdobe PDF267檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋