Prediction of Flutter Derivatives by Artificial Neural Networks
學年 97
學期 1
出版（發表）日期 2008-10-01
作品名稱 Prediction of Flutter Derivatives by Artificial Neural Networks
作品名稱（其他語言）
著者 Chen, Chern-hwa; 吳重成; Wu, Jong-cheng; Chen, Jow-hua
單位 淡江大學土木工程學系
出版者 Amsterdam: Elsevier BV
著錄名稱、卷期、頁數 Journal of Wind Engineering and Industrial Aerodynamics 96(10-11), pp.1925-1937
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.
關鍵字 Artificial neural network; Flutter derivative; Rectangular section model; Wind tunnel test
語言 en
ISSN 0167-6105
期刊性質
收錄於
產學合作
通訊作者
審稿制度
國別 NLD
公開徵稿
出版型式
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50456 )

機構典藏連結