Aerodynamics significantly influences race car performance, with wheels contributing 35–50% of total drag and affecting underbody downforce. This study investigates the flow around a rotating motorsport slick wheel using CFD to evaluate various turbulence models, validated against wind tunnel measurements. Simulations are conducted in a wind tunnel configuration using RANS, URANS, and DDES models in OpenFOAM. Results show that k-w SST outperforms EB Lag k-e in predicting near-wake features. In addition, DDES offers the highest accuracy overall compared to RANS and URANS, especially in capturing flow separation. Indeed, a trade-off must be made between solution accuracy and computational cost. Three main effects are analyzed in RANS. First, the wind tunnel rig induces wake asymmetry, increasing both lift and drag. Second, increasing Reynolds number delays flow separation from the wheel top, raising lift and reducing drag. Third, yaw angle alters wake symmetry and vortex strength, increasing drag and causing non-monotonic changes in lift.