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Morphing continuum theory for turbulence: Theory, computation, and visualization.

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  • 1Multiscale Computational Physics Lab, Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, Kansas 66506, USA.

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A new morphing continuum theory (MCT) accurately models compressible turbulence using fewer computational resources. This advanced method offers superior predictions compared to traditional Navier-Stokes simulations, revealing detailed eddy structures objectively.

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Area of Science:

  • Computational fluid dynamics
  • Continuum mechanics
  • Turbulence modeling

Background:

  • Modeling highly compressible turbulence presents significant computational challenges.
  • Existing methods like Navier-Stokes (NS) simulations require substantial computational resources.
  • Objective criteria for analyzing turbulent structures are limited in traditional frameworks.

Purpose of the Study:

  • Introduce a high-order morphing continuum theory (MCT) for compressible turbulence.
  • Develop a computationally efficient and accurate simulation method.
  • Provide an objective criterion for analyzing turbulent eddy structures.

Main Methods:

  • Formulated MCT within rational continuum mechanics.
  • Derived linear constitutive equations and balance laws using Coleman-Noll procedure and Onsager's reciprocal relations.
  • Solved governing equations in conservation form using a finite volume method with a second-order Lax-Friedrichs scheme.

Main Results:

  • MCT-based direct numerical simulation (DNS) accurately predicted transonic flow over a 3D bump.
  • Achieved better predictions than NS-based DNS using less than 10% of the mesh count.
  • Developed a frame-indifferent MCT-based Q criterion to visualize coherent eddy structures objectively.

Conclusions:

  • MCT offers a computationally efficient and accurate alternative for modeling compressible turbulence.
  • The MCT-based Q criterion provides objective analysis of turbulence, overcoming Galilean invariance limitations.
  • This approach enhances the prediction capabilities for complex turbulent flows.