一种基于深度卷积神经网络的燃气轮机叶片气动阻尼预测方法

By training samples using a deep convolutional neural network, a prediction network for blade surface parameters and aerodynamic damping was established. This solved the problem of complex and time-consuming calculations for gas turbine blade flutter analysis, and achieved fast and efficient aerodynamic damping prediction, applicable to blades with different structures and operating conditions.

CN115292843BActive Publication Date: 2026-07-17XI AN JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
XI AN JIAOTONG UNIV
Filing Date
2022-08-11
Publication Date
2026-07-17

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Abstract

本发明公开了一种基于深度卷积神经网络的燃气轮机叶片气动阻尼预测方法，包括：1)建立三维叶片气动计算域几何模型并划分网格；2)三维叶片颤振特性气动分析；3)训练样本计算和处理；4)叶片表面参数预测网络建立；5)气动阻尼预测网络建立；6)网络的训练和气动阻尼预测。本发明以足量三维叶片的颤振分析结果作为样本，训练基于叶片表面参数分布预测网络和气动阻尼预测网络，实现从几何参数和工况参数到气动阻尼的快速预测。
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