For wing in medium or deep stalled configuration, strong vortices occur whereas the boundary layer still affects the aerodynamic coefficients' results. In the past recent years, RANS' model (Reynolds-Averaged Navier-Stokes) has been widely used to predict aerodynamic phenomena, but it showed its weakness in predicting the modulation in vortex shedding (Forsythe, Squires, Wurtzler, & Spalart, 2004; Liang & Xue, 2014). Concomitantly Large Eddy Simulation (LES) succeeds in modelling eddy phenomena, while it fails predicting boundary-layer's phenomena with the current computation's power (Mockett, 2009). Using the advantage of both methods, Delayed Detached Eddy-Simulation (DDES) shows better results, but the solutions given seem to show more sensitivity to grid refinement than RANS or LES (Forsythe et al., 2004). In order to spare time and resources while increasing the results' accuracy of the stalled wing configuration's aerodynamic coefficients, this study offers a parametric grid study for the DDES model. For three different grid refinements, characteristics of lift and eddy phenomena are presented and compared to determine, for an infinite wing, the best compromise between time and resources' consumption, and results' accuracy. Using the open software SU2 6.1 (Stanford University Unstructured), we generate three different types of grid refinements around an airfoil, developed spanwise to obtain a straight wing. On the same stalled configuration for each mesh, CFD solutions are ran with the DDES model, and the raw data are postprocessed with the open software ParaView 5.6. We then compare the aerodynamic coefficients' distributions obtained by the three mesh. The general modelling of vortex shedding's topology and turbulence viscosity are compared with the literature to ensure the right rendering of vortex structures. Chordwise pressure and friction coefficients' distributions as well as the spanwise lift coefficient are also compared. We conclude with the optimum mesh in term of results and resources' consumption.