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基于神经网络的翼型气动力计算和反设计方法

刘凌君 周越 高振勋

刘凌君, 周越, 高振勋. 基于神经网络的翼型气动力计算和反设计方法[J]. 气体物理, 2018, 3(5): 41-47. doi: 10.19527/j.cnki.2096-1642.2018.05.005
引用本文: 刘凌君, 周越, 高振勋. 基于神经网络的翼型气动力计算和反设计方法[J]. 气体物理, 2018, 3(5): 41-47. doi: 10.19527/j.cnki.2096-1642.2018.05.005
LIU Ling-jun, ZHOU Yue, GAO Zhen-xun. Aerodynamic Force Calculation and Inverse Design for Airfoil Based on Neural Network[J]. PHYSICS OF GASES, 2018, 3(5): 41-47. doi: 10.19527/j.cnki.2096-1642.2018.05.005
Citation: LIU Ling-jun, ZHOU Yue, GAO Zhen-xun. Aerodynamic Force Calculation and Inverse Design for Airfoil Based on Neural Network[J]. PHYSICS OF GASES, 2018, 3(5): 41-47. doi: 10.19527/j.cnki.2096-1642.2018.05.005

基于神经网络的翼型气动力计算和反设计方法

doi: 10.19527/j.cnki.2096-1642.2018.05.005
详细信息
    作者简介:

    刘凌君(1995-)男, 硕士, 主要研究方向为计算机科学、流体力学.E-mail:lingjun.liu18@imperial.ac.uk

    通讯作者:

    周越(1969-)女, 博士, 主要研究方向为计算流体力学.E-mail:yuezhou@buaa.edu.cn

  • 中图分类号: O355

Aerodynamic Force Calculation and Inverse Design for Airfoil Based on Neural Network

  • 摘要: 开展了机器学习在翼型气动力计算和反设计方法中的应用研究,实现了在更大翼型空间范围内,人工神经网络的训练和优化,建立了翼型气动力计算模型,和给定目标压力分布的翼型反设计优化模型.作为机器学习领域兴起的研究热点,人工神经网络的研究工作不断深入,有研究者尝试将其应用于流体力学的学科范畴内.文章实现人工神经网络在翼型计算领域中应用的方法如下:首先通过Parsec参数化方法,围绕基准翼型构造了一定翼型空间范围的翼型库,利用XFOIL进行数值模拟,搭建了和翼型库具有一一映射关系的流场信息库.通过训练和优化神经网络,实现了基于此模型的快速、高可信度的翼型气动力预测,以及新型的翼型优化设计方法.通过自动化编程实现样本库的批量生成,实现了不同翼型空间的样本量下,神经网络的训练和优化过程.实验结果表明,在机器学习领域中,基于神经网络的翼型反设计模型的精确性高度依赖于训练样本量的大小和覆盖范围.

     

  • 图  1  基于PARSEC参数化生成的部分翼型库范围

    Figure  1.  Partial airfoil library ranges based on PARSEC parameterization

    图  2  XFOIL计算结果和实验结果对比

    Figure  2.  Comparisons between XFOIL calculation and experiment result

    图  3  原翼型的压力分布和预测翼型的计算压力分布对比图

    Figure  3.  Comparisons of Cp distributions between the original airfoil and the predicted airfoil

    图  4  针对压力分布系数和翼型几何坐标, 原翼型和预测翼型的偏移大小

    Figure  4.  Deviation of Cp distribution and airfoil geometry

    表  1  翼型气动力计算工况

    Table  1.   Working conditions over the calculation of airfoil aerodynamic parameters

    aerodynamic parameters Re Ma α/(°)
    CL 3×105 0.2 0~8
    CD 3×105 0.2 0~8
    Cp 3×105 0.2 2
    下载: 导出CSV

    表  2  神经网络结构

    Table  2.   BP neural network structure

    input output sample size
    airfoil geometry aerodynamic parameters 4 000
    下载: 导出CSV

    表  3  不同训练和传递函数的计算结果

    Table  3.   Results based on different training and activation functions

    time/s training functions activation functions maximum absolute error Δ
    337 trainlm [tansig, tansig] 0.024
    12 traingdx [tansig, tansig] 0.025
    17 traingdx [tansig, purelin] 0.032
    23 traingdx [purelin, purelin] 0.056
    下载: 导出CSV

    表  4  翼型反设计的输出误差分布

    Table  4.   Output error distributions of airfoil inverse design

    PARSEC parameters definitions geometric parameters relevant error ΔA
    P1 leading edge radius of upper surface rle 0.14%
    P2 maximum thickness po-sition of upper surface Xup -0.039%
    P3 maximum thickness of upper surface Zup 0.095%
    P4 maximum thickness curvature of upper surface ZXXup 0.019%
    P5 maximum thickness po-sition of lower surface Xlo -0.079%
    P6 maximum thickness of lower surface Zlo 0.049%
    P7 maximum thickness cur-vature of lower surface ZXXlo 0.026%
    P8 trailing edge thickness ZTE 3.16%
    P9 trailing edge offset ΔZTE 0
    P10 trailing edge angle αTE 0.92%
    P11 trailing edge wedge angle βTE 0.26%
    下载: 导出CSV

    表  5  本文和Waqas组的计算结果对照

    Table  5.   Comparisons of the results with Waqas results

    BP neural network mse≤1×10-5 mse≥1×10-5
    count percentage count percentage
    known(present) 1 968 98.4% 32 1.6%
    known(Waqas) 273 54.6% 227 45.4%
    unknown(present) 191 95.5% 9 4.5%
    unknown(Waqas) 113 56.5% 87 43.4%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-05
  • 修回日期:  2018-08-10
  • 发布日期:  2018-09-20
  • 刊出日期:  2018-09-01

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