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A turbulence model for pulsatile arterial flows.

B A Younis1, S A Berger

  • 1Department of Civil and Environmental Engineering, University of California, Davis, California 95616, USA. bayounis@ucdavis.edu

Journal of Biomechanical Engineering
|January 15, 2005
PubMed
Summary
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This study developed a new turbulence model for predicting blood flow in arteries. The model accurately captures transitional flow and near-wall behavior, improving predictions of arterial hemodynamics.

Area of Science:

  • Fluid Dynamics
  • Biomedical Engineering
  • Computational Fluid Dynamics

Background:

  • Predicting high Reynolds number flows in the circulatory system is challenging due to turbulence model limitations.
  • Standard turbulence models struggle with nonequilibrium effects, laminar-turbulent transition, and near-wall flow in arteries.

Purpose of the Study:

  • Develop an improved turbulence model specifically for arterial flows.
  • Address the limitations of existing models in capturing complex hemodynamic phenomena.

Main Methods:

  • Developed a two-equation eddy-viscosity turbulence model with specific wall treatments.
  • Incorporated laminar-turbulent transition effects by coupling the model with intermittency.
  • Validated the model using oscillatory transitional flows in tubes and pulsatile flow in a diseased human carotid artery.

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Main Results:

  • The new model shows substantial improvements over existing closures in predicting transitional flows.
  • Intermittency-modified model predictions for diseased carotid artery flow reveal significantly reduced wall shear stress.
  • The model accurately captures near-wall flow behavior where the law of the wall is invalid.

Conclusions:

  • The developed turbulence model is well-suited for predicting arterial flows, including transitional phases.
  • The model's ability to account for intermittency and near-wall effects leads to more accurate hemodynamic predictions.
  • This advancement aids in understanding and predicting cardiovascular diseases influenced by blood flow dynamics.