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基于神经网络的SST湍流代理模型研究及应用

梁爽 郭明明 易淼荣 田野 宋文艳 杨茂桃 张依 乐嘉陵

梁爽, 郭明明, 易淼荣, 等. 基于神经网络的SST湍流代理模型研究及应用[J]. 航空动力学报, 2024, 39(10):20220759 doi: 10.13224/j.cnki.jasp.20220759
引用本文: 梁爽, 郭明明, 易淼荣, 等. 基于神经网络的SST湍流代理模型研究及应用[J]. 航空动力学报, 2024, 39(10):20220759 doi: 10.13224/j.cnki.jasp.20220759
LIANG Shuang, GUO Mingming, YI Miaorong, et al. Research and application of SST turbulence surrogate model based on neural network[J]. Journal of Aerospace Power, 2024, 39(10):20220759 doi: 10.13224/j.cnki.jasp.20220759
Citation: LIANG Shuang, GUO Mingming, YI Miaorong, et al. Research and application of SST turbulence surrogate model based on neural network[J]. Journal of Aerospace Power, 2024, 39(10):20220759 doi: 10.13224/j.cnki.jasp.20220759

基于神经网络的SST湍流代理模型研究及应用

doi: 10.13224/j.cnki.jasp.20220759
基金项目: 国家自然科学基金(12002362)
详细信息
    作者简介:

    梁爽(1999-),男,硕士生,主要从事人工智能及超燃冲压发动机技术的研究。E-mail:liang199966@163.com

    通讯作者:

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

  • 中图分类号: V211.3

Research and application of SST turbulence surrogate model based on neural network

  • 摘要:

    针对传统湍流模型参数众多且获取复杂流动数据慢的问题,研究多种神经网络算法用于求解超声速流动中雷诺平均(Navier-Stokes)求解器的湍流代理模型。以超声速流动条件下的凹槽为例,应用拉丁超立方抽样方法,获取标准SST湍流模型的9个参数样本空间;采用自主研发的高超声速内外流耦合数值模拟软件AHL3D,在来流马赫数为2.92下开展数值模拟,获得壁面压力数据,构建数据集;搭建了深度神经网络(deep neural networks, DNN)、残差神经网络(residual neural network, ResNet)、长短时记忆网络(long short-term memory, LSTM)等多种模型对数据集进行训练,从而形成SST湍流代理模型。实验结果表明:在给定SST湍流模型系数下,3种神经网络代理模型均能高精度地预测壁面压力,可决系数达到了0.99以上,与数值模拟求解器结果基本一致,可用于快速获取不同湍流模型参数下的壁面压力。

     

  • 图 1  超声速凹槽构型

    Figure 1.  Supersonic cavity configuration

    图 2  网格无关性分析

    Figure 2.  Mesh Independence Analysis

    图 3  网格模型

    Figure 3.  Grid model

    图 4  马赫数分布云图

    Figure 4.  Mach number distribution cloud map

    图 5  壁面压力计算结果与实验结果对比

    Figure 5.  Comparison between the calculated results and the experimental results for wall pressure

    图 6  摩阻系数计算结果与实验结果对比

    Figure 6.  Comparison between the calculated results and the experimental results for frictional coefficient

    图 7  流场速度剖面计算结果与实验结果的对比

    Figure 7.  Comparison between the calculated results and the experimental results for velocity profile of flow field

    图 8  拉丁超立方抽样结果

    Figure 8.  Latin hypercube sampling results

    图 9  110组壁面压力曲线

    Figure 9.  110 groups of wall pressure curves

    图 10  不同位置点的Sobol

    Figure 10.  Sobol for different locations

    图 11  模型系数的对壁面压力的影响大小

    Figure 11.  Influence of turbulence model parameters on wall pressure

    图 12  DNN基本结构

    Figure 12.  Basic structure of DNN

    图 13  残差块基本结构

    Figure 13.  Residual block basic structure

    图 14  LSTM基本结构

    Figure 14.  LSTM basic structure

    图 15  在M1下的损失函数下降曲线

    Figure 15.  Loss function descent curve under M1

    图 16  未参与训练的10组壁面压力曲线

    Figure 16.  10 wall pressure curves without training

    图 17  在M1下的基于DNN的壁面压力曲线预测结果

    Figure 17.  Prediction results of wall pressure curve based on DNN under M1

    图 18  在M1下的基于ResNet的壁面压力曲线预测结果

    Figure 18.  Prediction results of wall pressure curve based on ResNet under M1

    图 19  在M2下的损失函数下降曲线

    Figure 19.  Loss function descent curve under M2

    图 20  在M2下的基于DNN的壁面压力曲线预测结果

    Figure 20.  Prediction results of wall pressure curve based on DNN under M2

    图 21  在M2下的基于ResNet的壁面压力曲线预测结果

    Figure 21.  Prediction results of wall pressure curve based on ResNet under M2

    图 22  在M2下的基于LSTM的壁面压力曲线预测结果

    Figure 22.  Prediction results of wall pressure curve based on LSTM under M2

    图 23  在M3下的损失函数下降曲线

    Figure 23.  Loss function descent curve under M3

    图 24  在M3下的基于DNN的壁面压力曲线预测结果

    Figure 24.  Prediction results of wall pressure curve based on DNN under M3

    图 25  在M3下的基于ResNet的壁面压力曲线预测结果

    Figure 25.  Prediction results of wall pressure curve based on ResNet under M3

    图 26  在M3下的基于LSTM的壁面压力曲线预测结果

    Figure 26.  Prediction results of wall pressure curve based on LSTM under M3

    表  1  SST湍流模型系数及区间

    Table  1.   SST turbulence model parameters and intervals

    系数 标称值 最小值 最大值
    $ {\sigma _{{\text{k1}}}} $ 0.85 0.7 1.0
    $ {\sigma _{{\text{k2}}}} $ 1.0 0.8 1.2
    $ {\sigma _{{\text{w1}}}} $ 0.5 0.3 0.7
    $ {\sigma _{{\text{w2}}}} $ 0.856 0.7 1.0
    $ {\beta ^*}/{\beta _1} $ 1.20 1.19 1.31
    $ {\beta ^*}/{\beta _2} $ 1.0870 1.05 1.45
    $ \,{\beta ^*} $ 0.09 0.0776 0.1016
    $ \kappa $ 0.41 0.38 0.42
    $ {a_1} $ 0.31 0.31 0.40
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-01
  • 网络出版日期:  2024-04-17

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