湍流
物理
人工神经网络
反演(地质)
边界层
旋转对称性
平均流量
超参数
流动分离
算法
机械
统计物理学
机器学习
人工智能
计算机科学
地质学
构造盆地
古生物学
作者
Chongyang Yan,Yufei Zhang,Haixin Chen
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2022-06-10
卷期号:34 (7)
被引量:25
摘要
Field inversion and machine learning are implemented in this study to describe three-dimensional (3D) separation flow around an axisymmetric hill and augment the Spalart–Allmaras (SA) model. The discrete adjoint method is used to solve the field inversion problem, and an artificial neural network is used as the machine learning model. A validation process for field inversion is proposed to adjust the hyperparameters and obtain a physically acceptable solution. The field inversion result shows that the non-equilibrium turbulence effects in the boundary layer upstream of the mean separation line and in the separating shear layer dominate the flow structure in the 3D separating flow, which agrees with prior physical knowledge. However, the effect of turbulence anisotropy on the mean flow appears to be limited. Two approaches are proposed and implemented in the machine learning stage to overcome the problem of sample imbalance while reducing the computational cost during training. The results are all satisfactory, which proves the effectiveness of the proposed approaches.
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