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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

廖鹏, 姚磊江, 白国栋, 赵航. 基于深度学习的混合翼型前缘压力分布预测[J]. 航空动力学报, 2019, 34(8): 1751-1758. doi: 10.13224/j.cnki.jasp.2019.08.014
引用本文: 廖鹏, 姚磊江, 白国栋, 赵航. 基于深度学习的混合翼型前缘压力分布预测[J]. 航空动力学报, 2019, 34(8): 1751-1758. doi: 10.13224/j.cnki.jasp.2019.08.014
Prediction of hybrid airfoil leading edge pressure distribution based on deep learning[J]. Journal of Aerospace Power, 2019, 34(8): 1751-1758. doi: 10.13224/j.cnki.jasp.2019.08.014
Citation: Prediction of hybrid airfoil leading edge pressure distribution based on deep learning[J]. Journal of Aerospace Power, 2019, 34(8): 1751-1758. 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

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

  • 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。

     

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出版历程
  • 收稿日期:  2019-01-21
  • 刊出日期:  2019-08-28

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