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基于CNN机翼气动系数预测

吕召阳 聂雪媛 赵奥博

吕召阳,聂雪媛,赵奥博. 基于CNN机翼气动系数预测[J]. 北京航空航天大学学报,2023,49(3):674-680 doi: 10.13700/j.bh.1001-5965.2021.0276
引用本文: 吕召阳,聂雪媛,赵奥博. 基于CNN机翼气动系数预测[J]. 北京航空航天大学学报,2023,49(3):674-680 doi: 10.13700/j.bh.1001-5965.2021.0276
LYU Z Y,NIE X Y,ZHAO A B. Prediction of wing aerodynamic coefficient based on CNN[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):674-680 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0276
Citation: LYU Z Y,NIE X Y,ZHAO A B. Prediction of wing aerodynamic coefficient based on CNN[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):674-680 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0276

基于CNN机翼气动系数预测

doi: 10.13700/j.bh.1001-5965.2021.0276
详细信息
    通讯作者:

    E-mail:niexueyuan@imech.ac.cn

  • 中图分类号: V221+.3;TB553

Prediction of wing aerodynamic coefficient based on CNN

More Information
  • 摘要:

    随着机器学习的快速发展和其突出的非线性映射能力,越来越多的学者将机器学习方法应用到流体力学领域。为克服传统数学拟合不能很好的解决系统非线性问题,以及现有文献中所提及的一些基于神经网络的气动参数预测方法,需要进行参数化处理而带来的不便,同时为实现多变量多输出气动参数快速预测的目的,基于卷积神经网络考虑机翼变迎角和浮沉建立了一种多变量多输出的机翼气动参数预测模型,实现了机翼气动参数的快速预测。结果表明:所建模型具有较高且稳定的预测精度,并且计算效率较计算流体力学(CFD)提高了40倍。

     

  • 图 1  LeNet卷积神经网络经典结构

    Figure 1.  Classic structure of LeNet convolutional neural network

    图 2  基于RBF网格变形机翼状态图及升力和力矩系数

    Figure 2.  Deformed wing state diagram and lift drag coefficient based on RBF mesh

    图 3  二元翼型[15]

    Figure 3.  Two dimensional airfoil[15]

    图 4  样本工况分布点

    Figure 4.  Distribution points of sample working conditions

    图 5  CNN训练流程

    Figure 5.  CNN training process

    图 6  训练过程中误差变化曲线

    Figure 6.  Error variation curve during training

    图 7  预测结果

    Figure 7.  Prediction results

    图 8  预测误差曲线

    Figure 8.  Prediction error curve

    图 9  测试集平均误差

    Figure 9.  Average error of test set

    表  1  差积神经网络具体结构和参数设置

    Table  1.   Specific structure and parameter setting of CNN

    层名称参数
    输入层机翼状态改变后的原始图像(200×200×3)
    卷积层1卷积核(5×5×96)+relu
    池化层1滤波器大小(3×3)strides(步长)=2
    卷积层2卷积核(3×3×96)+relu
    池化层2滤波器大小(3×3)strides(步长)=2
    卷积层3卷积核(3×3×128)+relu
    池化层3滤波器大小(3×3)strides(步长)=2
    全连接层(并列2个)relu+dropout
    输出层(并列2个)激活函数tanh
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-27
  • 录用日期:  2021-07-09
  • 网络出版日期:  2021-08-16
  • 整期出版日期:  2023-03-30

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