论文标题
多族顺序数据同化，用于大涡模拟的大型悬崖体流动
Multigrid sequential data assimilation for the large-eddy simulation of a massively separated bluff-body flow
论文作者
论文摘要
在本文中评估了数据驱动的数据驱动应用程序对湍流的尺度解析模拟的潜力。多移民顺序数据同化算法已用于校准大型涡流模拟的求解器，以分析纵横比5：1的矩形圆柱体周围的高雷诺数流量。之所以选择此测试案例，是因为许多物理复杂性使用降低的数值模拟来避免准确表示。速度和压力流场的统计矩的结果表明，基于集合卡尔曼滤波器所采用的数据驱动技术能够显着提高求解器的预测特征，以减少网格分辨率。此外，观察到，尽管观察过程中观察过程的稀疏和不对称分布，但数据增强结果表现出完美的对称统计数据，并且远离传感器位置的准确性也显着提高。
The potential for data-driven applications to scale-resolving simulations of turbulent flows is assessed herein. Multigrid sequential data assimilation algorithms have been used to calibrate solvers for Large Eddy Simulation for the analysis of the high-Reynolds-number flow around a rectangular cylinder of aspect ratio 5:1. This test case has been chosen because of a number of physical complexities which elude accurate representation using reduced-order numerical simulation. The results for the statistical moments of the velocity and pressure flow field show that the data-driven techniques employed, which are based on the Ensemble Kalman Filter, are able to significantly improve the predictive features of the solver for reduced grid resolution. In addition, it was observed that, despite the sparse and asymmetric distribution of observation in the data-driven process, the data augmented results exhibit perfectly symmetric statistics and a significantly improved accuracy also far from the sensor location.
