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Area of Science:

  • Fluid Dynamics
  • Computational Physics
  • Artificial Intelligence

Background:

  • Turbulence modeling is crucial for fluid dynamics simulations.
  • Existing large-eddy simulation models often face stability issues.
  • Artificial intelligence offers new avenues for discovering turbulence closures.

Purpose of the Study:

  • To discover a novel closed-form closure for 2D turbulence.
  • To improve the accuracy and stability of large-eddy simulations.
  • To explore the application of AI in fluid physics.

Main Methods:

  • Combining artificial intelligence with fluid physics principles.
  • Utilizing small direct numerical simulation datasets.
  • Employing sparse-equation discovery with a fourth-order Taylor expansion.

Main Results:

  • A new, accurate, and stable closed-form closure for 2D turbulence was discovered.
  • Large-eddy simulations using the new closure reproduced direct numerical simulation statistics, including extreme events.
  • The closure was derived from a fourth-order Taylor expansion, unlike prior second-order methods.

Conclusions:

  • The discovered closure enhances the predictive capabilities of large-eddy simulations.
  • Considering interscale energy transfer is key to developing improved turbulence models.
  • This work demonstrates the potential of AI in advancing fluid dynamics research.