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基于卷积神经网络气动力降阶模型的翼型优化方法

王沐晨 李立州 张珺 黄钰棋 张林 石玥

王沐晨,李立州,张珺,黄钰棋,张林,石玥. 基于卷积神经网络气动力降阶模型的翼型优化方法 [J]. 应用数学和力学,2022,43(1):77-83 doi: 10.21656/1000-0887.420137
引用本文: 王沐晨,李立州,张珺,黄钰棋,张林,石玥. 基于卷积神经网络气动力降阶模型的翼型优化方法 [J]. 应用数学和力学,2022,43(1):77-83 doi: 10.21656/1000-0887.420137
WANG Muchen, LI Lizhou, ZHANG Jun, HUANG Yuqi, ZHANG Lin, SHI Yue. An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model[J]. Applied Mathematics and Mechanics, 2022, 43(1): 77-83. doi: 10.21656/1000-0887.420137
Citation: WANG Muchen, LI Lizhou, ZHANG Jun, HUANG Yuqi, ZHANG Lin, SHI Yue. An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model[J]. Applied Mathematics and Mechanics, 2022, 43(1): 77-83. doi: 10.21656/1000-0887.420137

基于卷积神经网络气动力降阶模型的翼型优化方法

doi: 10.21656/1000-0887.420137
基金项目: 国家自然科学基金(51775518)
详细信息
    作者简介:

    王沐晨(1995—),女,硕士生(E-mail:wangmuchenll@163.com)

    李立州(1977—),男,教授,博士,博士生导师(通讯作者. E-mail:lilizhou@163.com)

  • 中图分类号: V211.3

An Airfoil Optimization Method Based on the Convolutional Neural Network Aerodynamic Reduced Order Model

  • 摘要:

    针对非线性大扰动翼型气动力优化问题,提出了基于卷积神经网络气动力降阶模型的优化方法。该方法用不同形状参数下翼型的气动力数据作为训练信号,训练卷积神经网络翼型气动力降阶模型。采用该气动力降阶模型,以最大升阻比为目标,对翼型进行优化,结果表明该方法可用于大扰动下翼型气动力的预测和优化。该文同时还讨论了池化法和径向基法的训练信号数据降维方法对降阶模型精度的影响,结果表明训练信号数据降维能够提高气动力降阶模型的精度。其原因在于训练信号数据降维可以减少神经网络模型的待定参数的个数,在相同数据量下神经网络模型收敛得更好。

  • 图  1  卷积神经网络结构图

    Figure  1.  The convolutional neural networks’ structure diagram

    图  2  不同参数的翼型

    Figure  2.  Airfoils with different parameters

    图  3  不同参数翼型的气动力:(a) 压力系数p;(b) 摩擦因数$ \tau $

    Figure  3.  Aerodynamics of the airfoils with different parameters: (a) pressure coefficient p; (b) skin friction coefficient $ \tau $

    图  4  大扰动的翼型

    Figure  4.  Airfoils of large disturbance

    图  5  翼型气动力:(a) 压力系数p;(b) 表面摩擦因数τ

    Figure  5.  Airfoil aerodynamics: (a) pressure coefficient p; (b) skin friction coefficient τ

    图  6  翼型优化结果

    Figure  6.  Airfoil optimization results

    图  7  气动力降阶模型的优化结果:(a) 压力系数p;(b) 摩擦因数τ

    Figure  7.  Aerodynamic ROM optimization results:(a) pressure coefficient p;(b) skin friction coefficient τ

    表  1  翼型优化结果

    Table  1.   Airfoil optimization results

    parameterlower boundupper boundoptimized
    upper surface a1
    a2
    a3
    a4
    a5
    a6
    a7
    0
    0
    0
    0
    0
    0
    0
    0.025
    0.030
    0.030
    0.030
    0.025
    0.020
    0.015
    0.025
    0.030
    0.030
    0.030
    0.025
    0.020
    0.015
    lower surface b1
    b2
    b3
    b4
    b5
    b6
    b7
    0
    0
    0
    0
    0
    0
    0
    0.025
    0.030
    0.030
    0.030
    0.025
    0.020
    0.015
    0.025
    0.030
    0.030
    0.030
    0.025
    0.020
    0.015
    lift-drag ratio $ {C_{\text{l}}}/{C_{\text{d}}} $ 32.41
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-17
  • 修回日期:  2021-06-24
  • 网络出版日期:  2021-12-16
  • 刊出日期:  2022-01-01

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