Aerodynamic Calculation of Airfoil Dynamic Stall Based on Data-Driven Transition Model
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摘要:
低Reynolds数下层流分离和分离诱导转捩现象复杂，数值仿真难度大。基于全连接反向传播神经网络，建立了低Reynolds数转捩间歇因子的数据驱动模型，通过优化设计选择了能够反映转捩过程的数据驱动模型的流场输入参数，辨识了转捩间歇因子，据此修正了k-ω SST二方程湍流模型，求解二维翼型动态失速下的流场演化和非定常气动力特性。结果表明，数据驱动的转捩方程耦合二方程湍流模型具有一定的迎角泛化能力，能够反映动态失速下前缘涡增长与脱落、流动再附着等典型流动状态。基于数据驱动转捩模型的动态失速下非定常气动升力预测结果与基于SST-γ三方程模型的CFD计算结果相比，相对误差小于12%。
Abstract:The laminar flow separation and separation-induced transition at low Reynolds number are complex, and have great difficulty in numerical simulation. Based on fully-connected back-propagation neural network, a data-driven model of intermittency at low Reynolds number was established. The input parameters of the data-driven model to reflect transition process and predict intermittency were selected through optimization design. By modifying the k-ω SST two equation turbulence model with a data-driven transition equation, the flow field evolution and unsteady aerodynamic characteristics of a two-dimensional airfoil under dynamic stall were solved. Results show that the data-driven transition equation combined with two equation turbulence model has the generalization ability for the angle of attack, and clearly reflects the typical flow conditions such as the growth and shedding of the leading-edge vortex and the reattachment of the flow under dynamic stall. The relative error of unsteady aerodynamic lift in dynamic stall between the data-driven transition model and the SST-γ three equation model is lower than 12%.
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Key words:
- turbulence model /
- flow transition /
- data-driven /
- neural network /
- dynamic stall
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图 3 平板表面湍流度与摩阻系数分布曲线
Figure 3. Distribution curves of turbulence intensity and friction coeffiency on the flat plate
图 8 翼型上仰过程典型流动状态涡量场：(a) 三方程湍流模型；(b) 耦合二方程湍流模型
Figure 8. Vortex fields of typical flow conditions via upstroke: (a) 3 equation turbulence model; (b) coupled 2 equation turbulence model
图 9 翼型下俯过程典型流动状态涡量场：(a) 三方程湍流模型；(b) 耦合二方程湍流模型
Figure 9. Vortex fields of typical flow conditions via downstroke: (a) 3 equation turbulence model; (b) coupled 2 equation turbulence model
表 1 T3A平板入口条件
Table 1. Inlet conditions of T3A flat plate
U∞/(m/s) Tu∞/(%) Rμ ReL k ω 5.4 3.35 11.5 6.12×105 0.047 6 264.63 
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表 2 神经网络训练集与测试集迎角设置
Table 2. Angle of attack setting of training and testing datasets
αT/(°) αP/(°) 4，8 2，4，5，6，7，8
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