Volume 11 Issue 3
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Article Navigation >  Water Science and Engineering  > 2018  >  11(3): 243-249
Lin Yuan, Hong-guang Sun, Yong Zhang, Yi-ping Li, Bing-qing Lu. 2018: Statistical description of depth-dependent turbulent velocity measured in Taihu Lake, China. Water Science and Engineering, 11(3): 243-249. doi: 10.1016/j.wse.2018,09.005
Citation: Lin Yuan, Hong-guang Sun, Yong Zhang, Yi-ping Li, Bing-qing Lu. 2018: Statistical description of depth-dependent turbulent velocity measured in Taihu Lake, China. Water Science and Engineering, 11(3): 243-249. doi: 10.1016/j.wse.2018,09.005
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Statistical description of depth-dependent turbulent velocity measured in Taihu Lake, China

doi: 10.1016/j.wse.2018,09.005

  • 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

Funds:  This work was supported by the National Natural Science Foundation of China (Grants No. 11572112 and 41330632).
More Information
  • Corresponding author: Hong-guang Sun
  • Received Date: 2017-07-19
  • Rev Recd Date: 2018-05-10
    Abstract
  • 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.

     

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