4.5 Article

Tomographic PIV investigation of coherent structures in a turbulent boundary layer flow

期刊

ACTA MECHANICA SINICA
卷 28, 期 3, 页码 572-582

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10409-012-0082-y

关键词

Tomographic particle image velocimetry; Turbulent boundary layer; Coherent structures; Hairpin vortex; Very large scale motion

资金

  1. National Natural Science Foundation of China [10832001, 10872145]
  2. State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences

向作者/读者索取更多资源

Tomographic particle image velocimetry was used to quantitatively visualize the three-dimensional coherent structures in the logarithmic region of the turbulent boundary layer in a water tunnel. The Reynolds number based on momentum thickness is Re (theta) = 2 460. The instantaneous velocity fields give evidence of hairpin vortices aligned in the streamwise direction forming very long zones of low speed fluid, which is flanked on either side by high-speed ones. Statistical support for the existence of hairpins is given by conditional averaged eddy within an increasing spanwise width as the distance from the wall increases, and the main vortex characteristic in different wall-normal regions can be reflected by comparing the proportion of ejection and its contribution to Reynolds stress with that of sweep event. The pre-multiplied power spectra and two-point correlations indicate the presence of large-scale motions in the boundary layer, which are consistent with what have been termed very large scale motions (VLSMs). The three dimensional spatial correlations of three components of velocity further indicate that the elongated low-speed and high-speed regions will be accompanied by a counter-rotating roll modes, as the statistical imprint of hairpin packet structures, all of which together make up the characteristic of coherent structures in the logarithmic region of the turbulent boundary layer (TBL).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据