Flutter speed prediction by using deep learning

doi:10.1177/16878140211062275

淡江大學機構典藏 > 工學院 > 航空太空工程學系暨研究所 > 期刊論文 > Item 987654321/121624

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121624

题名:	Flutter speed prediction by using deep learning
作者:	Wang, Yi-Ren;Wang, Yi-Jyun
关键词:	Flutter analysis;deep learning;deep neural network;long short-term memory
日期:	2021-11-18
上传时间:	2021-11-19 12:10:20 (UTC+8)
出版者:	SAGE Journals
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.
關聯:	Advances in Mechanical Engineering 13(11)
DOI:	10.1177/16878140211062275
显示于类别:	[航空太空工程學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	100	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....