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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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Published on: February 27, 2016

Lagrangian statistics in forced two-dimensional turbulence.

O Kamps¹, R Friedrich

¹Institute of Theoretical Physics, University of Münster, Wilhelm-Klemm-Strasse 9, 48149 Münster, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|October 15, 2008

Summary

Simulations of 2D turbulence reveal how tracer particles transition from non-Gaussian to Gaussian behavior. This study analyzes velocity fluctuations and acceleration dynamics in the inverse energy cascade regime.

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Area of Science:

Fluid Dynamics
Computational Physics

Background:

Turbulence is a complex phenomenon characterized by chaotic fluid motion.
Understanding turbulence, especially in reduced dimensions, is crucial for various scientific and engineering applications.
The inverse energy cascade regime in two-dimensional turbulence leads to the formation of large-scale structures.

Purpose of the Study:

To investigate the scaling behavior of probability density functions for velocity fluctuations of Lagrangian tracer particles in two-dimensional turbulence.
To compare the statistical properties of tracer particles in two-dimensional turbulence with those in three-dimensional turbulence.
To elucidate the underlying dynamics governing tracer particle motion through correlation function analysis.

Main Methods:

Numerical simulations of two-dimensional turbulence were performed.
Lagrangian tracer particle statistics were analyzed.
Probability density functions and compensated cumulants were computed to characterize velocity fluctuations.
Correlation functions for acceleration components were calculated.

Main Results:

The study observed a transition in the behavior of tracer particles from strongly non-Gaussian with heavy tails to Gaussian.
Scaling behavior of probability density functions was investigated.
Comparisons were made between two-dimensional and three-dimensional turbulence results.
Analysis of acceleration correlation functions provided insights into particle dynamics.

Conclusions:

The transition to Gaussianity in tracer particle statistics is a key finding in the inverse energy cascade regime.
Compensated cumulant analysis effectively reveals non-Gaussian to Gaussian transitions.
The dynamics of tracer particles are influenced by the turbulent flow characteristics.