Research on data assimilation for turbulence model constants via airfoil wind tunnel experiments

• Recalibrating the Spalart-Allmaras turbulence model constants using the ensemble Kalman filter method. • Comparison of airfoil pressure distribution to evaluate the effects of model constants. • The variation patterns of the constants were analyzed based on changes in airfoil thickness, Reynolds number, and the installation of Gurney flaps. • Evaluation of the applicability of assimilated constants under different conditions. Data assimilation algorithms have been demonstrated to increase the accuracy of predictions in airfoil flow fields. However, slight changes in airfoil geometry and Reynolds number ( Re ) variations could lead to differences in aerodynamic characteristics and stall behavior, consequently affecting assimilation outcomes. Hence, this research uses the ensemble Kalman filter (EnKF) algorithm. The aerodynamic characteristics of two wind turbine airfoils obtained through wind tunnel experiments were investigated under varying degrees of stall by recalibrating the constants in the (S-A) model. The impacts of the airfoil thickness, Re variation, and Gurney flap installation on the assimilation results were subsequently examined. Verifying the applicability of the constants obtained via data assimilation under varying conditions might offer opportunities to reduce the demand for computational resources. The assimilation results indicate that at a Re on the order of magnitude of 10 5 , the original model tends to delay flow separation as the Re increases. Consequently, the recalibrated constant C b1 generally decreases with increasing Re . Despite belonging to the same airfoil family, discrepancies in the flow separation behavior predicted by the original model resulted in variations in the recalibrated constants. The constants derived from the thinner airfoil induce premature flow separation in the thicker YA-30 airfoil under stall conditions. When assimilated constants are applied to flow field calculations under analogous stall conditions, constants from another condition may demonstrate an optimization effect and substitute the self-assimilated constants, provided that simulations using default constants for both conditions consistently exhibit an experimental separation trend. However, practical implementation requires caution due to the risk of overadjustment.