基于深度学习的混合翼型前缘压力分布预测

廖鹏; 姚磊江; 白国栋; 赵航

doi:10.13224/j.cnki.jasp.2019.08.014

基于深度学习的混合翼型前缘压力分布预测

doi: 10.13224/j.cnki.jasp.2019.08.014

廖鹏¹,
姚磊江²,
白国栋^2,3,
赵航^2,3

1.
西北工业大学无人机特种技术重点实验室,西安 710072；西北工业大学航空学院,西安 710072
2.
西北工业大学无人机特种技术重点实验室,西安 710072
3.
西北工业大学航空学院,西安 710072

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- 被引次数: 0
出版历程
- 收稿日期: 2019-01-21
- 刊出日期: 2019-08-28

Prediction of hybrid airfoil leading edge pressure distribution based on deep learning

摘要

摘要: 提出了一种基于深度学习的混合翼型前缘压力分布预测方法，通过对翼型几何特征提取、压力分布曲线的参数化，建立了卷积神经网络模型（CNN），并利用计算流体力学（CFD）的计算结果作为其训练样本，实现对混合翼型前缘压力分布的预测。结果表明:两种方法计算结果的拟合优度大于0.98，基于深度学习的计算方法耗时1.7 s，CFD方法耗时大于50 s，计算时间大大缩短。该方法能够在满足计算精度的条件下提高计算效率并可应用于其他的翼型设计过程。
- 混合翼型 /
- 深度学习 /
- 卷积神经网络(CNN) /
- 参数化方法 /
- 压力分布
Abstract: A prediction model on the leading edge pressure distribution of the hybrid airfoil based on deep learning was proposed. A convolutional neural network model (CNN) was established on the basis of the geometric feature extraction of the hybrid airfoil and the parameterization of the pressure distribution curve. A group of hybrid airfoils with different trailing edges were analyzed by a verified CFD method. The CFD results were used as the training set of the CNN. Results show that the goodness of fit of the calculation results of the two methods exceeds 0.98. The proposed prediction method based on deep learning takes 1.7 s and CFD method takes more than 50 s, computation time is greatly reduced. The proposed method can improve the computational efficiency with satisfying the calculation accuracy and it can be applied to the design processes of other airfoils.
- hybrid airfoil /
- deep learning /
- convolutional neural network(CNN) /
- parameterization method /
- pressure distribution

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参考文献(24)

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