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基于深度神经网络的横流转捩预测

胡震宇 王子路 陈坚强 袁先旭 向星皓

胡震宇, 王子路, 陈坚强, 袁先旭, 向星皓. 基于深度神经网络的横流转捩预测. 力学学报, 2023, 55(1): 38-51 doi: 10.6052/0459-1879-22-448
引用本文: 胡震宇, 王子路, 陈坚强, 袁先旭, 向星皓. 基于深度神经网络的横流转捩预测. 力学学报, 2023, 55(1): 38-51 doi: 10.6052/0459-1879-22-448
Hu Zhenyu, Wang Zilu, Chen Jianqiang, Yuan Xianxu, Xiang Xinghao. Prediction of crossflow transition based on deep neural networks. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 38-51 doi: 10.6052/0459-1879-22-448
Citation: Hu Zhenyu, Wang Zilu, Chen Jianqiang, Yuan Xianxu, Xiang Xinghao. Prediction of crossflow transition based on deep neural networks. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 38-51 doi: 10.6052/0459-1879-22-448

基于深度神经网络的横流转捩预测

doi: 10.6052/0459-1879-22-448
基金项目: 国家重点研发计划 (2019YFA0405204), 国家自然科学基金 (92052301, 12002355)和空气动力学国家重点实验室创新课题 (JBKYC190110)资助项目
详细信息
    通讯作者:

    向星皓, 助理研究员, 主要研究方向为高超声速流动数值模拟. E-mail: xhxiang@skla.cardc.cn

  • 中图分类号: V211.3

PREDICTION OF CROSSFLOW TRANSITION BASED ON DEEP NEURAL NETWORKS

  • 摘要: 基于深度神经网络DNN构建了从层流流场无量纲速度梯度、流向涡强度等物理量到横流转捩模态下间歇因子间的映射关系, 获得一种新的数据驱动转捩模型. 通过将数据驱动转捩模型与SST k-ω湍流模型耦合, 有效简化了转捩模型输运方程求解, 实现高效、准确的亚音速三维边界层横流转捩流场计算. DNN训练数据来自变雷诺数的NLF(2)-0415无限展长后掠翼计算结果, 并以两种工况进行测试, 数据驱动转捩模型预测精度与γ-Reθ转捩模型近似. 将数据驱动转捩模型用于其他典型横流转捩算例的计算, 以验证其泛化能力. 对于变后掠角的NLF(2)-0415后掠翼, 数据驱动转捩模型与γ-Reθt-CF模型预测的转捩位置几乎一致, 并且能够预测出后掠角从45°增长到65°的过程中, 转捩位置先向前再向后移动的现象; 对于标准椭球体, 使用低分辨率网格进行计算, 数据驱动转捩模型依然能够实现转捩位置预测, 对椭球体表面Cf的计算结果与多个平台的横流转捩模型、实验结果基本一致. 研究表明, 以横流转捩相关物理量作为输入对DNN进行训练, 并将获得的数据驱动转捩模型与SST k-ω湍流模型耦合, 可以实现对横流转捩的有效预测, 且具有较强的泛化能力. 数据驱动转捩模型对网格分辨率要求更低, 在保证计算精度的前提下, 具有较高的计算效率.

     

  • 图  1  深度神经网络监督学习框架

    Figure  1.  Supervised learning framework of DNN

    图  2  NLF(2)-0415后掠翼计算网格

    Figure  2.  Computing mesh for NLF(2)-0415 airfoil

    图  3  转捩位置判定

    Figure  3.  Determination of transition location

    图  4  转捩位置对比

    Figure  4.  Comparison of transition location

    图  5  深度神经网络正向传播

    Figure  5.  Positive propagation of DNN

    图  6  Sigmoid函数

    Figure  6.  Activation function of sigmoid

    图  7  特征距离s计算

    Figure  7.  Calculation method of characteristic length s

    图  8  训练数据所处区域

    Figure  8.  The zone of data for DNN

    图  9  5折交叉验证

    Figure  9.  5-fold cross validation

    图  10  间歇因子预测值与计算值对比

    Figure  10.  Comparison between predicted value and calculated value of intermittency

    图  11  近壁区域Δγ分布

    Figure  11.  Distribution of Δγ near the wall

    图  12  数据驱动转捩模型与γ-Reθ转捩模型Cf计算结果

    Figure  12.  Cf of data driven transition model and γ-Reθ transition model

    图  13  上翼面流线

    Figure  13.  Streamline of upper wing

    图  14  椭球体计算网格

    Figure  14.  Computing mesh for ellipsoid

    图  15  不同流动状态的Cf分布

    Figure  15.  Cf distribution in different flow states

    图  16  y + ≈30处间歇因子分布

    Figure  16.  Distribution of γ in y + ≈30

    图  17  算法限制原理

    Figure  17.  The theory of algorithm

    图  18  补充算法限制后间歇因子分布

    Figure  18.  Distribution of γcorrect

    图  19  不同算法对Cf的预测结果以及实验结果

    Figure  19.  Cf of different methods and test

    表  1  NFL(2)-0415后掠翼计算条件

    Table  1.   Conditions of NFL(2)-0415 airfoil

    CaseRe/106 m−1h/μmAoA
    /(o)
    Λ
    /(o)
    Tuνt/ν
    11.923.3−4450.15
    22.19
    32.37
    42.73
    53.27
    下载: 导出CSV

    表  2  交叉验证结果

    Table  2.   Result of cross validation

    DNNLtrain/10-4Ltest/10-4Ltest average/10-4
    12.684.834.31
    21.993.68
    32.544.41
    42.524.87
    52.373.74
    下载: 导出CSV

    表  3  数据驱动转捩模型与γ-Reθt-CF转捩模型对转捩位置的计算结果对比

    Table  3.   Comparison of xtr between data driven transition model and γ-Reθt-CF transition model

    ΛData driven
    transition model
    γ-Reθt-CF
    transition model
    45o0.4160.424
    55 o0.3910.395
    65 o0.4080.413
    下载: 导出CSV

    表  4  椭球体计算工况

    Table  4.   Conditions of ellipsoid

    CaseRe/mMah/μmAoAνt/ν
    16.5×1060.1363.315o5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-25
  • 录用日期:  2022-11-11
  • 网络出版日期:  2022-11-12
  • 刊出日期:  2023-01-18

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