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Please use this identifier to cite or link to this item:
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126902
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| Title: | Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures |
| Authors: | Wang, Yi-ren;Ma, Yu-han |
| Keywords: | machine learning;panel flutter;long short-term memory (LSTM) |
| Date: | 2024-08-16 |
| Issue Date: | 2025-03-20 09:29:55 (UTC+8) |
| Publisher: | MDPI |
| Abstract: | This study investigates the application of deep learning models—specifically Deep Neural
Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural
Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the
accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing
a supersonic flat plate as the main structure, the research integrates various flight conditions into the
aeroelastic equation. The resulting structural vibration data create a large-scale database for training
the models. The dataset, divided into training, validation, and test sets, includes input features such
as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance
of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM,
and LSTM-NN all showed improved training with more neurons and layers, excessive numbers
beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN
model achieved the highest accuracy in classification tasks, effectively capturing sequential features
and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling
long-term dependencies and complex non-linear relationships, making it ideal for predicting
flutter Mach numbers. Despite LSTM’s higher accuracy, it required longer training times due to
increased computational complexity, necessitating a balance between accuracy and training duration.
The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting
panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing
model architecture and training processes, deep learning models can achieve high accuracy in
predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs. |
| Relation: | Aerospace 11(8), 677 |
| DOI: | 10.3390/aerospace11080677 |
| Appears in Collections: | [航空太空工程學系暨研究所] 期刊論文
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