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Combining PIV, POD and vortex identification algorithms for the study of unsteady turbulent swirling flows

机译:结合PIV,POD和涡流识别算法来研究非定常湍流

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摘要

Particle image velocimetry (PIV) measurements are made in a highly turbulent swirling flow. In this flow, we observe a coexistence of turbulent fluctuations and an unsteady swirling motion. The proper orthogonal decomposition (POD) is used to separate these two contributions to the total energy. POD is combined with two new vortex identification functions, Γ_(1) and Γ_(2). These functions identify the locations of the centre and boundary of the vortex on the basis of the velocity field. The POD computed for the measured velocity fields shows that two spatial modes are responsible for most of the fluctuations observed in the vicinity of the location of the mean vortex centre. These two modes are also responsible for the large-scale coherence of the fluctuations. The POD computed from the Γ_(2) scalar field shows that the displacement and deformation of the large-scale vortex are correlated to these modes. We suggest the use of such a method to separate pseudo-fluctuations due to the unsteady nature of the large-scale vortices from fluctuations due to small-scale turbulence.
机译:粒子图像测速(PIV)测量是在高度湍流的旋流中进行的。在此流中,我们观察到湍流波动和不稳定涡旋运动并存。适当的正交分解(POD)用于分离这两个对总能量的贡献。 POD与两个新的涡旋识别函数Γ_(1)和Γ_(2)结合在一起。这些函数根据速度场确定涡旋中心和边界的位置。为测得的速度场计算的POD表明,在平均涡旋中心位置附近观察到的大多数波动都是由两种空间模式引起的。这两种模式还负责波动的大范围连贯性。从Γ_(2)标量场计算出的POD表明,大尺度涡旋的位移和变形与这些模式相关。我们建议使用这种方法将由于大尺度涡旋的不稳定性质而引起的假波动与由小规模湍流引起的波动分开。

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