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Enhancing CFD with the γ-Reθ Transition Model for Accurate Flow Prediction

The γ-Reθ Transition Model provides a refined approach to Computational Fluid Dynamics (CFD) by integrating empirical correlations as local quantities, crucial for unstructured CFD applications. Compatible with the SST k-ω turbulence model, it incorporates two additional transport equations to address intermittency. Developed by Menter and Langtry from ANSYS, this model has undergone validation for various transition scenarios, including natural, bypass, and laminar separation. Critical unpublished correlations have been refined through collaboration between CD-adapco and researchers in Thailand, enhancing the model's predictive accuracy.

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Enhancing CFD with the γ-Reθ Transition Model for Accurate Flow Prediction

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  2. -ReTransition Model • Most empirical models unsuited to unstructured CFD • Use non-local quantities (e.g., streamwise distance, q, etc.) • -Re allows empirical correlations to be used as local quantities • Works with the SST k-w turbulence model • Solves two additional transport equations • Intermittency • Reqt • Developed by Menter and Langtry (of ANSYS) • Allows empirical correlations to be used as local quantities • but critical correlations hitherto unpublished • CD-adapco have reverse-engineered these correlations with assistance from university researchers in Thailand! • Validated for natural, bypass and laminar-separation transition

  3. -ReTransition Model Equations Intermittency: unpublished Local transition momentum thickness Reynolds number:

  4. Model Calibration Using Ercoftac Data Calibrated Correlations Ercoftac Flat Plate Test Results

  5. Practical Application: F1 Rear Wing Laminar separation Transition Turbulent separation Photo courtesy of Renault F1

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