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Dynamic Mode Decomposition of Backward Facing Step Flow Simulation Data

机译:后向步流仿真数据的动态模式分解

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Improved delayed detached eddy simulation (IDDES) results of a backward facing step flow are analyzed using dynamic mode decomposition (DMD). Different flow variables and the time-resolved skin friction coefficient are investigated and compared to a spectral analysis of the wall pressure fluctuations. Although the flow field does not contain single dominant modes, two distinct flow features can be extracted and visualized using the DMD mode shapes. A low frequency flapping motion of the shear layer is found in the mode decomposition of the pressure, the wall-normal velocity and the skin friction coefficient. At higher frequencies, a wake mode similar to a von Karman vortex street is identified in the streamwise velocity, the pressure and the vorticity field. This work uses a modified version of the original dynamic mode decomposition algorithm that enforces a sparse solution with a user-defined number of modes. It is shown that the algorithm extracts the most important flow features reliably across different flow variables and that sparse DMD can be applied in situations where no single dominant mode is present.
机译:使用动态模式分解(DMD)分析了后向步进流的改进的延迟分离涡模拟(IDDES)结果。研究了不同的流量变量和时间分辨的皮肤摩擦系数,并将其与壁压力波动的频谱分析进行了比较。尽管流场不包含单个主导模式,但可以使用DMD模式形状提取两个不同的流动特征并将其可视化。在压力,壁法向速度和皮肤摩擦系数的模态分解中发现了剪切层的低频拍打运动。在更高的频率下,在水流速度,压力和涡度场中识别出类似于von Karman涡街的唤醒模式。这项工作使用原始动态模式分解算法的修改版本,该算法强制执行具有用户定义数量的模式的稀疏解决方案。结果表明,该算法能够可靠地提取出不同流量变量之间最重要的流量特征,并且稀疏DMD可应用于不存在单一主导模式的情况。

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