教師資料查詢 | 類別: 期刊論文 | 教師: 王怡仁WANG YI-REN (瀏覽個人網頁)

標題:Flutter speed prediction by using deep learning
學年110
學期1
出版(發表)日期2021/11/18
作品名稱Flutter speed prediction by using deep learning
作品名稱(其他語言)
著者Yi-Ren Wang; Yi-Jyun Wang
單位
出版者
著錄名稱、卷期、頁數Advances in Mechanical Engineering 13(11)
摘要Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.
關鍵字Flutter analysis;deep learning;deep neural network;long short-term memory
語言英文(美國)
ISSN1687-8140
期刊性質國外
收錄於SCI;
產學合作
通訊作者Yi-Ren Wang
審稿制度
國別英國
公開徵稿
出版型式,電子版
相關連結
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