Prediction of Flutter Derivatives Using Artificial Neural Networks

淡江大學機構典藏 > 工學院 > 土木工程學系暨研究所 > 會議論文 > Item 987654321/69952

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題名:	Prediction of Flutter Derivatives Using Artificial Neural Networks
作者:	Chen, C. H.;Lin, Y. Y.;Chen, J. H.
貢獻者:	淡江大學土木工程學系
關鍵詞:	artificial neural network;flat plate;flutter derivative;wind tunnel test
日期:	2006-07
上傳時間:	2011-10-23 20:38:04 (UTC+8)
出版者:	Yokohama: Japan Association for Wind Engineering
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.
關聯:	The Fourth International Symposium on Computational Wind Engineering, 4p.
顯示於類別:	[土木工程學系暨研究所] 會議論文

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