留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] 安效民, 徐敏, 陈士橹. 多场耦合求解非线性气动弹性的研究综述[J]. 力学进展, 2009, 39(3): 284-298. doi: 10.3321/j.issn:1000-0992.2009.03.004

    AN X M, XU M, CHEN S L. An overview of CFD/CSD coupled solution for nonlinear aeroelasticity[J]. Advances in Mechanics, 2009, 39(3): 284-298(in Chinese). doi: 10.3321/j.issn:1000-0992.2009.03.004
    [2] 李亚东, 张子军, 张钧尧, 等. 电动飞机气动焦点辨识及飞行试验研究[J]. 航空工程进展, 2021, 12(3): 78-84. doi: 10.16615/j.cnki.1674-8190.2021.03.10

    LI Y D, ZHANG Z J, ZHANG J Y, et al. Research on aerodynamic center identification and flight test of electric aircraft[J]. Advances in Aeronautical Science and Engineering, 2021, 12(3): 78-84(in Chinese). doi: 10.16615/j.cnki.1674-8190.2021.03.10
    [3] 原智杰, 张公平, 崔茅, 等. 基于神经网络的导弹气动参数预测[J]. 航空兵器, 2020, 27(5): 28-32. doi: 10.12132/ISSN.1673-5048.2019.0164

    YUAN Z J, ZHANG G P, CUI M, et al. Prediction of missile’s aerodynamic parameters based on neural network[J]. Aero Weaponry, 2020, 27(5): 28-32(in Chinese). doi: 10.12132/ISSN.1673-5048.2019.0164
    [4] SURESH S, OMKAR S N, MANI V, et al. Lift coefficient prediction at high angle of attack using recurrent neural network[J]. Aerospace Science and Technology, 2003, 7(8): 595-602. doi: 10.1016/S1270-9638(03)00053-1
    [5] CARPENTER M, HARTFIELD R, BURKHALTER J. A comprehensive approach to cataloging missile aerodynamic performance using surrogate modeling techniques and statistical learning[C]// 29th AIAA Applied Aerodynamics Conference. Reston: AIAA, 2011: 3029.
    [6] 刘昕. 基于神经网络的机翼气动参数预测仿真研究[J]. 计算机仿真, 2015, 32(12): 67-71. doi: 10.3969/j.issn.1006-9348.2015.12.014

    LIU X. Simulation of airfoil plunging aerodynamic parameter prediction based on neural network[J]. Computer Simulation, 2015, 32(12): 67-71(in Chinese). doi: 10.3969/j.issn.1006-9348.2015.12.014
    [7] BALLA K, RUBEN S, OUBAY H, et al. An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings[J]. Applied Mathematical Modelling, 2021, 96: 456-479. doi: 10.1016/j.apm.2021.03.019
    [8] 叶舒然, 张珍, 王一伟, 等. 基于卷积神经网络的深度学习流场特征识别及应用进展[J]. 航空学报, 2021, 42(4): 524736.

    YE S R, ZHANG Z, WANG Y W, et al. Progress in deep convolutional neural network based flow field recognition and its applications[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524736(in Chinese).
    [9] 陈海, 钱炜祺, 何磊. 基于深度学习的翼型气动系数预测[J]. 空气动力学学报, 2018, 36(2): 294-299.

    CHEN H, QIAN W Q, HE L. Aerodynamic coefficient prediction of airfoils based on deep learning[J]. Acta Aerodynamica Sinica, 2018, 36(2): 294-299(in Chinese).
    [10] 吴正文. 卷积神经网络在图像分类中的应用研究[D]. 成都: 电子科技大学, 2015: 8-12.

    WU Z W. Application research of convolution neural network in image classification[D]. Chengdu: University of Electronic Science and Technology of China, 2015 : 8-12 (in Chinese).
    [11] 李宏伟, 吴庆祥. 智能传感器中神经网络激活函数的实现方案[J]. 传感器与微系统, 2014, 33(1): 46-48. doi: 10.3969/j.issn.1000-9787.2014.01.012

    LI H W, WU Q X. Implementation scheme for activated function of neutral networks in intelligent sensors[J]. Transducer and Microsystem Technologies, 2014, 33(1): 46-48(in Chinese). doi: 10.3969/j.issn.1000-9787.2014.01.012
    [12] 尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015, 41(1): 48-59. doi: 10.11936/bjutxb2014100026

    YIN B C, WANG W T, WANG L C. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 41(1): 48-59(in Chinese). doi: 10.11936/bjutxb2014100026
    [13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386
    [14] NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning, 2010: 807- 814.
    [15] 聂雪媛, 郑冠男, 杨国伟. 含间隙非线性机翼跨声速颤振时滞反馈控制[J]. 北京航空航天大学学报, 2021, 47(10): 1980-1988. doi: 10.13700/j.bh.1001-5965.2020.0356

    NIE X Y, ZHENG G N, YANG G W. Time delay feedback control for transonic flutter of airfoil with free-play nonlinearity[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 1980-1988(in Chinese). doi: 10.13700/j.bh.1001-5965.2020.0356
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  389
  • HTML全文浏览量:  72
  • PDF下载量:  44
  • 被引次数: 0
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

目录

    /

    返回文章
    返回
    常见问答