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Turbulence generation from a stochastic wavelet model.

Y Du1, G Lin1,2

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Proceedings. Mathematical, Physical, and Engineering Sciences
|October 19, 2018
PubMed
Summary
This summary is machine-generated.

A novel stochastic wavelet method generates turbulence fields efficiently, requiring smaller bases than Fourier methods. This approach enables adaptive, cost-effective generation of inhomogeneous turbulence with high accuracy.

Keywords:
stochastic methodsturbulence generationwavelets

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Area of Science:

  • Fluid Dynamics
  • Computational Physics
  • Turbulence Modeling

Background:

  • Accurate turbulence generation is crucial for simulating complex fluid flows.
  • Existing methods like synthetic Fourier methods have limitations in computational cost and adaptability.
  • The need for efficient and accurate turbulence generation for both homogeneous and inhomogeneous cases persists.

Purpose of the Study:

  • To introduce a new turbulence generation method utilizing stochastic wavelets.
  • To evaluate the efficiency and accuracy of this new method compared to existing techniques.
  • To demonstrate the capability of generating adaptive inhomogeneous and anisotropic turbulence fields.

Main Methods:

  • Development of a turbulence generation method based on stochastic wavelets.
  • Testing turbulence field properties in both homogeneous and inhomogeneous scenarios.
  • Implementation of a scale reduction algorithm for adaptive generation of inhomogeneous turbulence.
  • Adjustment of the generating formula for fully inhomogeneous and anisotropic turbulence with RANS data.

Main Results:

  • Turbulence fields generated with significantly smaller bases compared to synthetic Fourier methods.
  • Comparable accuracy achieved with the stochastic wavelet method.
  • Adaptive generation of inhomogeneous turbulence via scale reduction proved computationally inexpensive and accurate.
  • The generating formula successfully produced fully inhomogeneous and anisotropic turbulence under divergence-free constraints.
  • Generated turbulence fields showed good agreement with input data and theoretical predictions.

Conclusions:

  • The stochastic wavelet method offers a more computationally efficient alternative for turbulence generation.
  • The method provides a robust framework for generating accurate homogeneous and inhomogeneous turbulence.
  • This approach advances the capability to model complex turbulent flows, particularly those requiring anisotropic and inhomogeneous characteristics.