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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/69952


    Title: Prediction of Flutter Derivatives Using Artificial Neural Networks
    Authors: Chen, C. H.;Lin, Y. Y.;Chen, J. H.
    Contributors: 淡江大學土木工程學系
    Keywords: artificial neural network;flat plate;flutter derivative;wind tunnel test
    Date: 2006-07
    Issue Date: 2011-10-23 20:38:04 (UTC+8)
    Publisher: Yokohama: Japan Association for Wind Engineering
    Abstract: 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.
    Relation: The Fourth International Symposium on Computational Wind Engineering, 4p.
    Appears in Collections:[土木工程學系暨研究所] 會議論文

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