Determination of unsteady wing loading using tuft visualization

Unsteady separated flow affects the aerodynamic performance of many large-scale objects, posing challenges for accurate assessment through low-fidelity simulations. Full-scale wind tunnel testing is often impractical due to the object’s physical scale. Small-scale wind tunnel tests can approximate the aerodynamic loading, with tufts providing qualitative validation of surface flow patterns. This investigation demonstrates that tufts can quantitatively estimate unsteady integral aerodynamic lift and pitching moment loading on a wing. We present computational and experimental data for a NACA0012 wing, capturing unsteady surface flow and force coefficients beyond stall. Computational data for varying angles of attack and Reynolds numbers contain the lift coefficient and surface flow. Experimental data, including lift and moment coefficients for a tuft-equipped NACA0012 wing, were obtained at multiple angles of attack and constant Reynolds number. Our results show that a data-driven surrogate model can predict lift and pitching moment fluctuations from visual tuft observations.