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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
Quantum turbulent velocity statistics and quasiclassical limit
1School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 7, 2012
Summary
Numerical modeling of quantum turbulence in superfluid helium resolves conflicting experimental results. Different length scales probed explain the observed Gaussian distributions versus power laws, revealing the average vortex distance as a key transition marker.
Area of Science:
- Quantum turbulence
- Superfluid helium dynamics
- Vortex dynamics
Background:
- Experimental measurements of turbulent velocity statistics in superfluid helium yielded conflicting results.
- One group reported Gaussian distributions, typical of classical turbulence, while another found power-law distributions.
- This discrepancy posed a puzzle regarding the nature of quantum turbulence.
Purpose of the Study:
- To numerically model quantum turbulence as a network of vortex filaments.
- To reconcile the conflicting experimental findings on superfluid helium turbulence.
- To identify the underlying physical mechanism responsible for the observed statistical differences.
Main Methods:
- Numerical simulation of quantum turbulence using a vortex filament model.
- Analysis of turbulent velocity statistics across various length scales.
- Comparison of simulation results with experimental data from different techniques.
Main Results:
- The study found no contradiction between the conflicting experimental results.
- A transition from Gaussian to power-law distributions was observed.
- This transition is attributed to the different length scales probed by the experimental techniques.
Conclusions:
- The average distance between quantum vortices acts as a critical length scale.
- This scale separates quantum and quasiclassical regimes in superfluid turbulence.
- The numerical model successfully explains the apparent paradox in experimental observations.
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