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Lagrangian particle statistics in turbulent flows from a simple vortex model.

M Wilczek1, F Jenko, R Friedrich

  • 1Institute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Strasse 9, D-48149 Münster, Germany. mwilczek@uni-muenster.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 23, 2008
PubMed
Summary

This study uses a vortex model to analyze Lagrangian particle statistics in turbulent flows. It investigates the origin of small-scale intermittency, comparing model results with experimental data.

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Area of Science:

  • Fluid Dynamics
  • Turbulence Theory
  • Statistical Mechanics

Background:

  • Understanding Lagrangian particle statistics is crucial for modeling turbulent flows.
  • Intermittency, or the uneven distribution of energy dissipation, is a key feature of turbulence.
  • Previous models often simplify the complex nature of turbulent velocity fields.

Purpose of the Study:

  • To investigate the statistics of Lagrangian particles in turbulent flows using a simplified vortex model.
  • To analyze the origin and characteristics of small-scale intermittency within this model.
  • To compare the model's predictions with experimental findings in turbulence research.

Main Methods:

  • Representing turbulent velocity fields as a sequence of Burgers vortices.
  • Deriving velocity increment statistics based on vortex properties.
  • Employing both numerical simulations and analytical methods to study intermittency.
  • Comparing model results against experimental data from turbulent flow studies.

Main Results:

  • The study successfully derives velocity increment statistics from the vortex model.
  • It provides insights into the origins and nature of small-scale intermittency.
  • Quantitative and qualitative comparisons with experimental data are performed.

Conclusions:

  • The Burgers vortex model offers a valuable framework for understanding Lagrangian particle statistics and intermittency in turbulence.
  • The model's ability to reproduce experimental observations highlights its utility.
  • Further research can refine this model for more complex turbulent phenomena.