Data-driven RANS closures using a relative importance term analysis based classifier for 2D and 3D separated flows
Tyler Buchanan (Williams Racing Formula One Team, TU Delft - Aerodynamics)
Monica Lăcătuş (TU Delft - Numerical Analysis)
Alastair West (Williams Racing Formula One Team)
Richard P. Dwight (TU Delft - Aerodynamics)
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Abstract
This study presents a novel approach for enhancing Reynolds-averaged Navier-Stokes (RANS) turbulence modeling through the application of a Relative Importance Term Analysis (RITA) methodology to develop a new zonally-augmented k−ω SST model. Traditional Linear Eddy Viscosity Models often struggle with separated flows. Our approach introduces a physics-based binary classifier that systematically identifies separated shear layers requiring correction by analyzing the relative magnitudes of terms in the turbulent kinetic energy equation. Using symbolic regression, we develop compact correction terms for Reynolds stress anisotropy and turbulent kinetic energy production. Trained on 2D configurations, our model demonstrates significant improvements in predicting separation dynamics while maintaining baseline performance in fully attached flows. Generalization tests on Ahmed body and Faith hill 3D configurations confirm robust transferability, establishing an effective methodology for targeted enhancement of RANS predictions in separated flows.
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