论文标题
一种新的数据同化方法，用于恢复高雷诺数的湍流数字，用于湍流机学习
A new data assimilation method of recovering turbulent flow field at high-Reynolds numbers for turbulence machine learning
论文作者
论文摘要
本文提出了一种新的数据同化方法，用于根据实验数据在高雷诺数下在高雷诺数下恢复高保真性湍流场，这称为适当的正交分解反转（POD Inversion）数据同化方法。针对包括冲击波不连续性或以高攻击角度分离的流动的流动，该方法可以重建高保真湍流场与实验分布式力系数结合。我们首先对SA模型计算的湍流涡流粘度字段进行POD分析并获得基本POD模式。然后通过全局优化算法与Navier-Stokes方程求解器相连，优化了POD系数。高保真的湍流通过几种主要模式恢复，这可以大大降低系统的尺寸。该方法的有效性通过在高雷诺数字上的RAE2822机翼周围的跨多气流验证，并在高攻击角度下分离流动。结果表明，所提出的同化方法可以恢复最佳匹配实验数据的湍流场，并显着减少压力系数的误差。提出的数据同化方法可以基于机器学习为湍流模型提供高保真现场数据。
This paper proposes a new data assimilation method for recovering high fidelity turbulent flow field around airfoil at high Reynolds numbers based on experimental data, which is called Proper Orthogonal Decomposition Inversion (POD-Inversion) data assimilation method. Aiming at the flows including shock wave discontinuities or separated flows at high angle of attack, the proposed method can reconstruct high-fidelity turbulent flow field combining with experimental distributed force coefficients. We firstly perform the POD analysis to the turbulent eddy viscosity fields computed by SA model and obtain the base POD modes. Then optimized the POD coefficients by global optimization algorithm coupling with the Navier-Stokes equations solver. The high-fidelity turbulent flied are recovered by several main modes, which can dramatically reduce the dimensions of the system. The effectiveness of the method is verified by the cases of transonic flow around the RAE2822 airfoil at high Reynolds numbers and the separated flow at high angles of attack. The results demonstrate that the proposed assimilation method can recover the turbulent flow field which optimally match the experimental data, and significantly reduce the error of pressure coefficients. The proposed data assimilation method can offer high-fidelity field data for turbulent model based on machine learning.
