Flutter speed prediction by using deep learning
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 predic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-11-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878140211062275 |
| _version_ | 1819003164184018944 |
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| author | Yi-Ren Wang Yi-Jyun Wang |
| author_facet | Yi-Ren Wang Yi-Jyun Wang |
| author_sort | Yi-Ren Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T23:16:39Z |
| format | Article |
| id | doaj.art-7a2587ef3bc34340833c3f799ba55d26 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-20T23:16:39Z |
| publishDate | 2021-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-7a2587ef3bc34340833c3f799ba55d262022-12-21T19:23:38ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402021-11-011310.1177/16878140211062275Flutter speed prediction by using deep learningYi-Ren WangYi-Jyun WangDeep 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.https://doi.org/10.1177/16878140211062275 |
| spellingShingle | Yi-Ren Wang Yi-Jyun Wang Flutter speed prediction by using deep learning Advances in Mechanical Engineering |
| title | Flutter speed prediction by using deep learning |
| title_full | Flutter speed prediction by using deep learning |
| title_fullStr | Flutter speed prediction by using deep learning |
| title_full_unstemmed | Flutter speed prediction by using deep learning |
| title_short | Flutter speed prediction by using deep learning |
| title_sort | flutter speed prediction by using deep learning |
| url | https://doi.org/10.1177/16878140211062275 |
| work_keys_str_mv | AT yirenwang flutterspeedpredictionbyusingdeeplearning AT yijyunwang flutterspeedpredictionbyusingdeeplearning |