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首页> 外文期刊>Physics of fluids >Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence
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Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence

机译：可压缩各向同性湍流大型涡流模拟的人工神经网络混合模型

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摘要

In this work, the subgrid-scale (SGS) stress and the SGS heat flux of compressible isotropic turbulence are modeled by an artificial neural network (ANN) mixed model (ANNMM), which maintains both functional and structural performances. The functional form of the mixed model combining the gradient model and the Smagorinsky's eddy viscosity model is imposed, and the ANN is used to calculate the model coefficients of the SGS anisotropy stress, SGS energy, and SGS heat flux. It is shown that the ANNMM can reconstruct the SGS terms more accurately than the gradient model in the a priori test. Specifically, the ANNMM almost recovers the average values of the SGS energy flux and SGS energy flux conditioned on the normalized filtered velocity divergence. In an a posteriori analysis, the ANNMM shows advantage over the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the spectra of velocity and temperature, which almost overlap with the filtered direct numerical simulation data, while the DSM and DMM suffer from the problem of the typical tilted spectral distribution. Besides, the ANNMM predicts the probability density functions of SGS energy flux much better than DSM and DMM. ANN with functional model forms can enlighten and deepen our understanding of large eddy simulation modeling. Published under license by AIP Publishing.
机译：在这项工作中，子网格尺度（SGS）应力和SGS加热可压缩各向同性紊流通量是通过人工神经网络（ANN）的混合模型（ANNMM），其保持功能和结构性能建模。混合模型组合的梯度模型和Smagorinsky的涡粘度模型的函数形式施加，并且ANN用于计算的SGS的模型系数各向异性应力，SGS能量，和SGS的热通量。结果表明，该ANNMM可以更准确地重构SGS术语比在先验测试梯度模型。具体而言，几乎ANNMM回收SGS能量通量和空调上的归一化滤波的速度散度SGS能量通量的平均值。在后验分析，ANNMM显示优于在速度和温度的光谱，的预测的动态Smagorinsky模型（DSM）和动态混合模型（DMM），其几乎与过滤直接数值模拟数据重叠，而DSM和DMM从典型的倾斜光谱分布的问题的困扰。此外，ANNMM预测SGS能源的概率密度函数通量明显优于DSM和DMM。 ANN与功能模型的形式可以启发和深化我们的大涡模拟模型的理解。通过AIP发布在许可证下发布。

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  • 来源
    《Physics of fluids》 |2019年第8期|共24页
  • 作者

    Xie Chenyue; Wang Jianchun; Li Hui; Wan Minping; Chen Shiyi;

  • 作者单位

    Southern Univ Sci &

    Technol Dept Mech &

    Aerosp Engn Shenzhen Key Lab Complex Aerosp Flows Shenzhen 518055 Peoples R China;

    Southern Univ Sci &

    Technol Dept Mech &

    Aerosp Engn Shenzhen Key Lab Complex Aerosp Flows Shenzhen 518055 Peoples R China;

    Wuhan Univ Sch Power &

    Mech Engn Wuhan 430072 Hubei Peoples R China;

    Southern Univ Sci &

    Technol Dept Mech &

    Aerosp Engn Shenzhen Key Lab Complex Aerosp Flows Shenzhen 518055 Peoples R China;

    Southern Univ Sci &

    Technol Dept Mech &

    Aerosp Engn Shenzhen Key Lab Complex Aerosp Flows Shenzhen 518055 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 流体力学;
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