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Related Experiment Video

Updated: Apr 28, 2026

The Diffusion of Passive Tracers in Laminar Shear Flow
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Published on: May 1, 2018

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CoNFiLD-inlet: Synthetic turbulence inflow using generative latent diffusion models with neural fields.

Xin-Yang Liu1, Meet Hemant Parikh1, Xiantao Fan1

  • 1Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana, USA.

Physical Review Fluids
|April 27, 2026
PubMed
Summary

This study introduces CoNFiLD-inlet, a new deep learning method for generating realistic turbulence inflow conditions. It overcomes limitations of previous methods, offering robust and scalable synthetic turbulence generation for simulations.

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Last Updated: Apr 28, 2026

The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

9.8K

Area of Science:

  • Fluid dynamics
  • Computational science
  • Artificial intelligence

Background:

  • Eddy-resolving turbulence simulations need accurate stochastic inflow conditions.
  • Existing methods like recycling or synthetic generators have limitations in realism and computational cost.
  • Deep learning (DL) offers potential but often struggles with robustness and error accumulation.

Purpose of the Study:

  • To develop a novel deep learning-based inflow turbulence generator.
  • To address the limitations of current methods in producing realistic and robust synthetic turbulence.
  • To create a versatile tool for various Reynolds numbers without retraining.

Main Methods:

  • Integration of diffusion models with a conditional neural field.
  • Parametrization of inflow conditions using Reynolds numbers.
  • Validation using a priori and a posteriori tests in direct numerical simulation and large-eddy simulation.

Main Results:

  • CoNFiLD-inlet successfully generates realistic, stochastic inflow turbulence.
  • The method demonstrates effective generalization across a wide range of Reynolds numbers (10^3 to 10^4).
  • High fidelity, robustness, and scalability were confirmed through comprehensive validation.

Conclusions:

  • CoNFiLD-inlet provides an efficient and versatile solution for synthetic inflow turbulence.
  • The novel approach overcomes common challenges in turbulence simulation setup.
  • This method advances the capability of deep learning in computational fluid dynamics.