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Deep learning velocity signals allow quantifying turbulence intensity.

Alessandro Corbetta1, Vlado Menkovski2, Roberto Benzi3

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Deep neural networks accurately estimate the Reynolds number in turbulent flows, even with limited data. This breakthrough enables quantitative study of chaotic fluid dynamics previously hindered by nonstationarities.

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Area of Science:

  • Fluid dynamics
  • Computational physics
  • Machine learning

Background:

  • Turbulence is a chaotic fluid motion with significant velocity fluctuations.
  • Quantifying turbulence using statistical averages is difficult due to strong nonstationarities.
  • Traditional methods struggle with statistical convergence in nonstationary flows.

Purpose of the Study:

  • To develop a method for accurately quantifying turbulent flows.
  • To overcome limitations of statistical convergence in nonstationary turbulence.
  • To enable the study of turbulence in natural and industrial settings.

Main Methods:

  • Utilizing deep neural networks (DNNs) for data analysis.
  • Training DNNs on statistical samples of turbulent flow data.
  • Comparing DNN performance against physics-based statistical estimators.

Main Results:

  • Deep neural networks achieved 15% accuracy in Reynolds number estimation.
  • DNNs required only two large-scale eddy turnover times for accurate estimation.
  • Physics-based methods showed significantly larger errors for the same data sample.

Conclusions:

  • Deep neural networks offer a powerful tool for quantifying nonstationary turbulence.
  • This approach overcomes limitations of traditional statistical methods.
  • Enables quantitative analysis of complex turbulent flows in real-world applications.