教師資料查詢 | 類別: 會議論文 | 教師: 林堉溢 Lin Yuh-yi (瀏覽個人網頁)

標題:Prediction of Flutter Derivatives Using Artificial Neural Networks
學年
學期
發表日期2006/07/16
作品名稱Prediction of Flutter Derivatives Using Artificial Neural Networks
作品名稱(其他語言)
著者Chen, C. H.; Lin, Y. Y.; Chen, J. H.
作品所屬單位淡江大學土木工程學系
出版者Yokohama: Japan Association for Wind Engineering
會議名稱The Fourth International Symposium on Computational Wind Engineering
會議地點Yokohama, Japan
摘要This paper develops an artificial neural network (ANN) algorithm to predict the
flutter derivatives of rectangular section models. Firstly, the ANN model uses the
experimental dynamic responses of the section model in smooth flow to train a
back-propagation (BP) neural network frame. The flutter derivatives can be determined using
weight matrices in the neural network. The second part of this study is to predict the flutter
derivatives of the rectangular section models without wind tunnel tests. Based on the given
flutter derivatives of the rectangular section models tested in wind tunnel, the prediction
frames of neural network are then established. The flutter derivatives of the rectangular
section models, with the B/D ratios other than those obtained from the wind tunnel tests can
be predicted by using this approach. The results show that this prediction scheme is
reasonably well. By using this ANN approach, the database of the aerodynamic coefficients of
bridge sections could be expanded.
關鍵字artificial neural network;flat plate;flutter derivative;wind tunnel test
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20060716~20060719
通訊作者
國別日本
公開徵稿Y
出版型式紙本
出處The Fourth International Symposium on Computational Wind Engineering, 4p.
相關連結
SDGs
Google+ 推薦功能,讓全世界都能看到您的推薦!