Flow separation prediction in a simplified notchback car model by assimilating global luminescent oil film measurements

• GLOF-based data assimilation improves RANS prediction of separated flows. • Local EnKF optimization of SST aa 1 parameter aligns the simulation with GLOF data. • Twin-experiment validation confirms the reliability of the EnKF framework. • Improved flow and drag predictions show the promise of zonal aa 1 optimization. This study presents an enhancement of Reynolds-averaged Navier-Stokes (RANS) simulations for predicting the flow around a simplified notchback car model by incorporating experimental data from global luminescent oil film (GLOF) measurements. The simulations employ the SST k - ω turbulence model, with a particular focus on improving predictions of flow separation in the rear regions of the car model, including the rear window and rear side surfaces. To achieve this, the parameter a 1 of the SST k - ω turbulence model is zonally optimized using an ensemble Kalman filter (EnKF), ensuring that the predicted separation locations align locally with near-wall flow features captured by the GLOF measurements. The data assimilation framework is first validated through a numerical data assimilation experiment (twin experiment) that mimics GLOF observations within the RANS simulation. Following this validation, the system is applied to actual GLOF measurements. The resulting GLOF-informed a 1 distribution yields near-wall flow patterns that closely match those observed in the experiment, demonstrating the effectiveness of the proposed approach.