Water Science and Engineering 2018, 11(3) 243-249 DOI:   https://doi.org/10.1016/j.wse.2018,09.005  ISSN: 1674-2370 CN: 32-1785/TV

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Finite Reynolds number turbulence
Reverse structure function
Lévy distribution
Probability density function
Multiscaling extended self-similarity (ESS)

Statistical description of depth-dependent turbulent velocity measured in Taihu Lake, China

Lin Yuan a, b, Hong-guang Sun a, b*, Yong Zhang c, Yi-ping Li d, Bing-qing Lu c

a State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
b College of Mechanics and Materials, Hohai University, Nanjing 210098, China
c Department of Geological Sciences, University of Alabama, Tuscaloosa 35487, USA
d College of Environment, Hohai University, Nanjing 210098, China


Quantitative description of turbulence using simple physical/mathematical models remains a challenge in classical physics and hydrologic dynamics. This study monitored the turbulence velocity field at the surface and bottom of Taihu Lake, in China, a large shallow lake with a heterogeneous complex system, and conducted a statistical analysis of the data for the local turbulent structure. Results show that the measured turbulent flows with finite Reynolds numbers exhibit properties of non-Gaussian distribution. Compared with the normal distribution, the Lévy distribution with meaningful parameters can better characterize the tailing behavior of the measured turbulence. Exit-distance statistics and multiscaling extended self-similarity (ESS) were used to interpret turbulence dynamics with different scale structures. Results show that the probability density function of the reverse structure distance and the multiscaling ESS can effectively capture the turbulent flow dynamics varying with water depth. These results provide an approach for quantitatively analyzing multiscale turbulence in large natural lakes.

Keywords Finite Reynolds number turbulence   Reverse structure function   Lévy distribution   Probability density function   Multiscaling extended self-similarity (ESS)  
Received 2017-07-19 Revised 2018-05-10 Online: 2018-07-30 
DOI: https://doi.org/10.1016/j.wse.2018,09.005

This work was supported by the National Natural Science Foundation of China (Grants No. 11572112 and 41330632).

Corresponding Authors: Hong-guang Sun
Email: shg@hhu.edu.cn
About author:


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