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Related Experiment Video

Updated: Dec 14, 2025

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

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Turbulence model reduction by deep learning.

R A Heinonen1, P H Diamond1

  • 1Department of Physics, University of California San Diego, La Jolla, California 92093, USA.

Physical Review. E
|July 22, 2020
PubMed
Summary
This summary is machine-generated.

A new data-driven method models turbulent fluxes in magnetic confinement, revealing a strong vorticity gradient effect on particle flux and spontaneous zonal flow generation.

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Last Updated: Dec 14, 2025

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

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

Published on: February 27, 2016

12.8K

Area of Science:

  • Plasma physics
  • Turbulence theory
  • Computational fluid dynamics

Background:

  • Turbulent fluxes are crucial for understanding plasma dynamics in magnetic confinement.
  • Predictive models for these fluxes are a central challenge in turbulence theory.
  • Drift-wave turbulence in magnetic confinement generates anomalous fluxes through fluctuation cross-correlations.

Purpose of the Study:

  • To introduce a novel data-driven approach for parametrizing turbulent fluxes.
  • To infer a reduced mean-field model using deep supervised learning from numerical simulations.
  • To analyze the dynamics of a simple drift-wave turbulence system.

Main Methods:

  • Deep supervised learning to infer a reduced mean-field model.
  • Application to a simplified drift-wave turbulence system.
  • Analysis of flux-gradient relationships and flow generation mechanisms.

Main Results:

  • Identification of a significant coupling between particle flux and local vorticity gradient.
  • Demonstration that the vorticity gradient effect is stronger than shear suppression.
  • Recovery of a model for spontaneous zonal flow generation, stabilized by nonlinear and hyperviscous terms.

Conclusions:

  • The data-driven method successfully parametrizes turbulent fluxes and reveals new physical insights.
  • Vorticity gradient coupling significantly influences particle flux and density profiles.
  • The method highlights the importance of symmetry in developing predictive turbulence models.