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

基于深度神经网络的超声速隔离段湍流涡黏性系数辨识

杨茂桃 梁爽 易淼荣 田野 郭明明 乐嘉陵 张华

杨茂桃, 梁爽, 易淼荣, 等. 基于深度神经网络的超声速隔离段湍流涡黏性系数辨识[J]. 航空动力学报, 2023, 38(2):312-324 doi: 10.13224/j.cnki.jasp.20220168
引用本文: 杨茂桃, 梁爽, 易淼荣, 等. 基于深度神经网络的超声速隔离段湍流涡黏性系数辨识[J]. 航空动力学报, 2023, 38(2):312-324 doi: 10.13224/j.cnki.jasp.20220168
YANG Maotao, LIANG Shuang, YI Miaorong, et al. Identification of turbulence eddy viscosity coefficient in supersonic isolation section based on deep neural network[J]. Journal of Aerospace Power, 2023, 38(2):312-324 doi: 10.13224/j.cnki.jasp.20220168
Citation: YANG Maotao, LIANG Shuang, YI Miaorong, et al. Identification of turbulence eddy viscosity coefficient in supersonic isolation section based on deep neural network[J]. Journal of Aerospace Power, 2023, 38(2):312-324 doi: 10.13224/j.cnki.jasp.20220168

基于深度神经网络的超声速隔离段湍流涡黏性系数辨识

doi: 10.13224/j.cnki.jasp.20220168
基金项目: 国家自然科学基金(12002362)
详细信息
    作者简介:

    杨茂桃(1997-),男,硕士生,主要从事人工智能及超燃冲压发动机技术研究

    通讯作者:

    田野(1987-),男,副研究员,博士,主要从事超燃冲压发动机燃烧组织技术研究。E-mail:tianye@cardc.cn

  • 中图分类号: V235.21

Identification of turbulence eddy viscosity coefficient in supersonic isolation section based on deep neural network

  • 摘要:

    雷诺平均Navier-Stokes(RANS)方程由于计算成本较低,当前仍然在工程设计领域广泛应用。为了进一步提升计算精度和减少时间,应用深度神经网络(deep neural networks, DNN)方法自适应辨识稳态湍流涡黏性系数。以隔离段的激波串前缘位置检测流场生成为例,使用Wilcox-2006 $k$-$ \omega $湍流模型进行模拟。在不同的背压情况下,产生稳定状态的湍流涡黏性流场作为模型学习的训练数据集。最后在不同背压条件下开展了测试。结果表明:提出的DNN方法能够快速预测湍流涡黏性系数的值,预测值与计算流体力学数值模拟计算的参数值相比,方均根误差较小,可决系数大于99%,预测的流场结果与真实流场基本一致,进一步验证了深度学习技术在湍流模型参数辨识的可行性。

     

  • 图 1  地面直连式超燃冲压发动机实验台

    Figure 1.  Ground direct connect scramjet test bench

    图 2  网格无关性分析

    Figure 2.  Grid independence analysis

    图 3  网格模型

    Figure 3.  Grid model

    图 4  激波串的位置纹影图

    Figure 4.  Position schlieren diagram of shock train

    图 5  DNN湍流涡黏性系数辨识网络

    Figure 5.  DNN turbulent eddy viscosity coefficient identification network

    图 6  DNN训练损失下降曲线

    Figure 6.  DNN training loss decline curve

    图 7  8个背压下406组预测样本预测结果分析

    Figure 7.  Analysis of prediction results of 406 groups of prediction samples under 8 different pressures

    图 8  一个背压为预测样本的预测结果分析(0.23 MPa)

    Figure 8.  Analysis of prediction results with a back pressure of predicted sample (0.23 MPa)

    图 9  流场预测结果分析(0.23 MPa)

    Figure 9.  Analysis of prediction results of downstream flow field (0.23 MPa)

    图 10  速度场预测结果分析(0.23 MPa)

    Figure 10.  Analysis of velocity field prediction results (0.23 MPa)

    图 11  一个背压为预测样本的预测结果分析(0.28 MPa)

    Figure 11.  Analysis of prediction results with a back pressure of predicted sample (0.28 MPa)

    图 12  流场预测结果分析(0.28 MPa)

    Figure 12.  Analysis of prediction results of downstream flow field (0.28 MPa)

    图 13  速度场预测结果分析(0.28 MPa)

    Figure 13.  Analysis of velocity field prediction results (0.28 MPa)

    图 14  8个不同背压为训练集条件下预测结果分析(0.22 MPa)

    Figure 14.  Analysis of prediction results under 8 different back pressure training sets (0.22 MPa)

    图 15  8个不同背压为训练集条件下流场预测结果分析(0.22 MPa)

    Figure 15.  Analysis of prediction results of downflowfield under 8 different back pressure training sets (0.22 MPa)

    图 16  8个不同背压为训练集条件下速度场预测结果分析(0.22 MPa)

    Figure 16.  Analysis of velocity field prediction results under 8 different back pressures training sets (0.22 MPa)

    图 17  8个不同背压为训练集条件下预测结果分析(0.29 MPa)

    Figure 17.  Analysis of prediction results under 8 different back pressure training sets (0.29 MPa)

    图 18  8个不同背压为训练集条件下流场预测结果分析(0.29 MPa)

    Figure 18.  Analysis of prediction results of downflow field under 8 different back pressure training sets (0.29 MPa)

    图 19  8个不同背压为训练集条件下速度场预测结果分析(0.29 MPa)

    Figure 19.  Analysis of velocity field prediction results under 8 different back pressures training sets (0.29 MPa)

    表  1  参数的物理意义

    Table  1.   Physical meaning of parameters

    参数物理意义
    $ {T_{\text{t}}} $总温
    $ {p_{\text{t}}} $总压
    $\,\rho$点的密度
    $ {k_{{\text{turb}}}} $湍流模型$ k $的值
    $x (x)$,$y (x)$, ${\textit{z}} (x)$流场中点的网格坐标
    $Ma$马赫数
    $t$温度
    $ p $点的压力
    ${v_{\text{t} } } (x)$涡黏性系数
    $ {w_{{\text{turb}}}} $湍流模型$ w $的值
    $u (x)$,$v (x)$, $w (x)$流场中点的速度分量
    $ {C_{{\text{lim}}}} $应力限制系数
    $ \kappa $卡门常数
    下载: 导出CSV
  • [1] 陈植. 超燃冲压发动机隔离段流动机理及其控制的试验研究[D]. 合肥: 国防科学技术大学, 2015.

    CHEN Zhi. Experimental study on flow mechanism and control of isolation section of scramjet engine[D]. Hefei: National University of Defense Technology, 2015. (in Chinese)
    [2] DOLLING D S. Fifty years of shock-wave/boundary-layer interaction research: what next?[J]. AIAA Journal,2001,39(8): 1517-1531. doi: 10.2514/2.1476
    [3] 贺武生. 超燃冲压发动机研究综述[J]. 火箭推进,2005(1): 29-32. doi: 10.3969/j.issn.1672-9374.2005.01.006

    HE Wusheng. A review of scramjet research[J]. Journal of Rocket Propulsion,2005(1): 29-32. (in Chinese) doi: 10.3969/j.issn.1672-9374.2005.01.006
    [4] MATSUO K. Shock train and pseudo-shock phenomena in supersonic internal flows[J]. Journal of Thermal Science,2003(3): 204-208.
    [5] 詹王杰,严红. 隔离段内激波串受稳态和脉动背压影响的数值研究[J]. 推进技术,2021,42(5): 980-990. doi: 10.13675/j.cnki.tjjs.190671

    ZHAN Wangjie,YAN Hong. Numerical study on the influence of shock string on steady-state and pulsating back pressure in isolation section[J]. Journal of Propulsion Technology,2021,42(5): 980-990. (in Chinese) doi: 10.13675/j.cnki.tjjs.190671
    [6] 吴正园,莫凡,高振勋,等. 湍流边界层与高温气体效应耦合的直接数值模拟[J]. 空气动力学学报,2020,38(6): 1111-1119. doi: 10.7638/kqdlxxb-2020.0132

    WU Zhengyuan,MO Fan,GAO Zhenxun,et al. Direct numerical simulation of coupling of turbulent boundary layer and hot gas effect[J]. Acta Aerodynamica Sinica,2020,38(6): 1111-1119. (in Chinese) doi: 10.7638/kqdlxxb-2020.0132
    [7] XIAO Heng,CINNELLA P. Quantification of model uncertainty in RANS simulations: a review[J]. Progress in Aerospace Science,2019,108: 1-31. doi: 10.1016/j.paerosci.2018.10.001
    [8] 尹宇辉,李浩然,张宇飞,等. 机器学习辅助湍流建模在分离流预测中的应用[J]. 空气动力学学报,2021,39(2): 23-32. doi: 10.7638/kqdlxxb-2020.0155

    YIN Yuhui,LI Haoran,ZHANG Yufei,et al. Application of machine learning-assisted turbulence modeling in separated flow prediction[J]. Acta Aerodynamica Sinica,2021,39(2): 23-32. (in Chinese) doi: 10.7638/kqdlxxb-2020.0155
    [9] CRAFT T J,LAUNDER B E,SUGA K. Development and application of a cubic eddy-viscosity model of turbulence[J]. International Journal of Heat and Fluid Flow,1996,17(2): 108-115. doi: 10.1016/0142-727X(95)00079-6
    [10] SPALART P R. Strategies for turbulence modelling and simulations[J]. International Journal of Heat and Fluid Flow,2000,21(3): 252-263. doi: 10.1016/S0142-727X(00)00007-2
    [11] STEFANO M,HOSDER S,BAURLE R A. Effect of turbulence model uncertainty on scramjet isolator flowfield analysis[J]. Journal of Propulsion and Power,2019,36(1): 1-14.
    [12] SCHAEFER J,HOSDER S,WEST T,et al. Uncertainty quantification of turbulence model closure coefficients for transonic wall-bounded flows[J]. AIAA Journal,2017,55(1): 195-213. doi: 10.2514/1.J054902
    [13] XIAO H,WU J L,WANG J X,et al. Quantifying and reducing model form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics informed Bayesian approach[J]. Journal of Computational Physics,2016,324: 115-136. doi: 10.1016/j.jcp.2016.07.038
    [14] MAULIK R,SAN O,RASHEED A,et al. Data-driven deconvolution for large eddy simulations of Kraichnan turbulence[J]. Physics of Fluids,2018,30(12): 125109.1-125109.15.
    [15] YARLANKI S, RAJENDRAN B, HAMANN H. Estimation of turbulence closure coefficients for data centers using machine learning algorithms[R]. San Diego, Canada: 13th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, 2012.
    [16] ZHANG Zejia, DURAISAM K. Machine learning methods for data-driven turbulence modeling[R]. Dallas, US: 22nd AIAA Computational Fluid Dynamics Conference, 2015.
    [17] KAANDORP M. Machine learning for data-driven RANS turbulence modelling[D]. Delft, US: Delft University of Technology, 2018.
    [18] SAAKAAR B,YASER A,SHAOWU P,et al. Prediction of aerodynamic flow fields using convolutional neural networks[J]. Computational Mechanics,2019,64(2): 525-545. doi: 10.1007/s00466-019-01740-0
    [19] ZHU Linyang,ZHANG Weiwei,KOU Jiaqing,et al. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Fluids,2019,31(1): 015105.1-015105.14.
    [20] PANDA J P,WARRIOR H V. Data-driven prediction of complex flow field over an axisymmetric body of revolution using machine learning[J]. Fluid Dynamics (preprint),2021,arXiv: 2111.07937.
    [21] SHUVAYAN B,ANANTHAKRISHNAN B,HIDEAKI O. Fast estimation of internal flowfields in scramjet intakes via reduced-order modeling and machine learning[J]. Physics of Fluids,2021,33(10): 106110.1-106110.24.
    [22] SUN Xuxiang,CAO Wenbo,LIU Yilang,et al. High Reynolds number airfoil turbulence modeling method based on machine learning technique[J]. Computers and Fluids,2022,236: 105298.1-105298.30.
    [23] 何创新,邓志文,刘应征. 湍流数据同化技术及应用[J]. 航空学报,2021,42(4): 167-184.

    HE Chuangxin,DENG Zhiwen,LIU Yingzheng. Turbulent data assimilation technology and its application[J]. Chinese Journal of Aeronautics,2021,42(4): 167-184. (in Chinese)
    [24] LI Yunfei,CHANG Juntao,KONG Chen,et al. Recent progress of machine learning in flow modeling and active flow control[J]. Chinese Journal of Aeronautics,2021,35(4): 14-44.
    [25] KONG Chen,CHANG Juntao,LI Yunfei,et al. Flowfield reconstruction and shock train leading edge detection in scramjet isolators[J]. AIAA Journal,2020,58(9): 4068-4080. doi: 10.2514/1.J059302
    [26] KONG Chen,CHANG Juntao,LI Yunfei,et al. A deep learning approach for the velocity field prediction in a scramjet isolator[J]. Physics of Fluids,2021,33(2): 026103.1-026103.18.
    [27] KONG Chen,CHANG Juntao,WANG Ziao,et al. Prediction model of flow field in an isolator over various operating conditions[J]. Aerospace Science and Technology,2021,111: 106576.1-106576.14.
    [28] LI Nan,CHANG Juntao,XU Kejing,et al. Prediction dynamic model of shock train with complex background waves[J]. Physics of Fluids,2017,29: 116103.1-116103.16.
    [29] KUKREJA H,BHARATH N,SIDDESH C S,et al. An introduction to artificial neural network[J]. International Journal of Advance Research and Innovative Ideas in Education,2016,1: 27-30.
  • 加载中
图(19) / 表(1)
计量
  • 文章访问数:  169
  • HTML浏览量:  97
  • PDF量:  80
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-30
  • 网络出版日期:  2022-11-23

目录

    /

    返回文章
    返回