Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures

doi:10.3390/aerospace11080677

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 987654321/126902

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題名:	Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures
作者:	Wang, Yi-ren;Ma, Yu-han
關鍵詞:	machine learning;panel flutter;long short-term memory (LSTM)
日期:	2024-08-16
上傳時間:	2025-03-20 09:29:55 (UTC+8)
出版者:	MDPI
摘要:	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.
關聯:	Aerospace 11(8),　677
DOI:	10.3390/aerospace11080677
顯示於類別:	[航空太空工程學系暨研究所] 期刊論文

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