DDES-Informed Development of a Helicity-Based Turbulence Model: Validation on Corner Separation and Aeronautical Flows

Accurate prediction of separated flows remains a critical challenge for Reynolds-Averaged Navier–Stokes (RANS) simulations, primarily due to the tendency of standard turbulence models to overpredict separation. To address this limitation, this study develops and validates a helicity-augmented variant of Menter’s Shear Stress Transport (SST) model within a high-fidelity, data-guided framework. First, a scale-resolving database, capturing the physics of corner separation, is established via an improved Delayed Detached Eddy Simulation (DDES) of a linear compressor cascade. Insights from this database directly inform the integration of a normalized helicity parameter into the SST formulation, enabling dynamic modulation of the turbulent eddy viscosity to account for non-equilibrium turbulence and energy backscatter in three-dimensional (3D) vortical flows. The enhanced SST model is subsequently validated against experimental data for two benchmark aerodynamic configurations: ARA M100 wing–fuselage and DLR-F6 aircraft models. Results demonstrate that the proposed correction significantly improves the prediction of separation topology and aerodynamic coefficients, delays the predicted onset of stall, and achieves closer agreement with measurements. These findings confirm the DDES-guided helicity correction as an effective strategy for enhancing the predictive fidelity of RANS models in simulating the complex separated flows encountered in practical aeronautical applications.