Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

大涡模拟 雷诺应力 各向同性 湍流 人工神经网络 张量(固有定义) 雷诺数 数学 直接数值模拟 滤波器(信号处理) 统计物理学 机械 计算机科学 物理 人工智能 几何学 量子力学 计算机视觉
作者
Zhideng Zhou,Guowei He,Shizhao Wang,Guodong Jin
出处
期刊:Computers & Fluids [Elsevier BV]
卷期号:195: 104319-104319 被引量:130
标识
DOI:10.1016/j.compfluid.2019.104319
摘要

An artificial neural network (ANN) is used to establish the relation between the resolved-scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for large-eddy simulation (LES) of isotropic turbulent flows. The data required for training and testing of the ANN are provided by performing filtering operations on the flow fields from direct numerical simulations (DNSs) of isotropic turbulent flows. We use the velocity gradient tensor together with filter width as input features and the SGS stress tensor as the output labels for training the ANN. In the a priori test of the trained ANN model, the SGS stress tensors obtained from the ANN model and the DNS data are compared by computing the correlation coefficient and the relative error of the energy transfer rate. The correlation coefficients are mostly larger than 0.9, and the ANN model can accurately predict the energy transfer rate at different Reynolds numbers and filter widths, showing significant improvement over the conventional models, for example the gradient model, the Smagorinsky model and its dynamic version. A real LES using the trained ANN model is performed as the a posteriori validation. The energy spectrum computed by the improved ANN model is compared with several SGS models. The Lagrangian statistics of fluid particle pairs obtained from the improved ANN model almost approach those from the filtered DNS, better than the results from the Smagorinsky model and dynamic Smagorinsky model.
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