Enhancing the k– ω shear stress transport turbulence model through renormalization group theory for flows separated from a continuous surface

Flow separation from a continuous surface under adverse pressure gradient remains one of the most challenging problems for turbulence modeling within the Reynolds-Averaged Navier–Stokes (RANS) framework. Although the Shear Stress Transport (SST) model performs well for a wide range of flows, its predictive capability deteriorates in wake regions dominated by complex turbulent structures. To enhance its accuracy, a modified SST model incorporating the Renormalization Group (RNG) theory of turbulence is proposed. The RNG correction is introduced as an additional source term in the dissipation equation and is limited to the reattachment region through a blending function to ensure computational efficiency and numerical stability. The model is validated using several canonical problems, including the curved backward-facing step, vertical and inclined backward-facing steps, and the periodic hills case at various Reynolds numbers. The effects of the inflow boundary condition are carefully examined to isolate the intrinsic performance of the modified model. For this purpose, an experimental campaign is performed. Comparisons with benchmark Large Eddy Simulation studies and experimental data show that the RNG-enhanced SST model substantially improves the prediction of mean velocity, turbulence kinetic energy, and wall quantities such as pressure coefficient, wall shear stress, and boundary-layer parameters. In particular, the prediction of the reattachment position is improved, yielding reduced relative errors that are nearly half of the SST model. These findings demonstrate that integrating RNG theory provides a computationally cost-effective, numerically robust, and physically consistent improvement to the SST model for separated flows from a continuous surface.