论文标题
数据增强的湍流建模，用于三维分离流
Data augmented turbulence modeling for three-dimensional separation flows
论文作者
论文摘要
本研究实施了现场反转和机器学习，以描述轴对称山丘周围的三维分离流并增强Spart-Allmaras模型。离散的伴随方法用于解决现场反转问题，并将人工神经网络用作机器学习模型。提出了针对现场反转的验证过程，以调整超参数并获得物理上可接受的解决方案。场反转结果表明，平均分离线上游的边界层和分离剪切层中的非平衡湍流效应在3-D分离流中占主导地位，这与先前的物理知识一致。但是，湍流各向异性对平均流量的影响似乎受到限制。在机器学习阶段提出并实施了两种方法，以克服样本不平衡问题，同时降低培训期间的计算成本。结果都是令人满意的，这证明了拟议方法的有效性。
Field inversion and machine learning are implemented in this study to describe three-dimensional separation flow around an axisymmetric hill and augment the Spart-Allmaras 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 3-D 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.
