Data-driven Reconstruction of Velocity Field around Airfoil using Unsteady Surface Pressure Measurement

View Video Presentation: https://doi.org/10.2514/6.2022-0139.vid In this study, a liner reduced-order model based observer which estimates flow velocity fields from unsteady pressure sensors on the surface of an airfoil based on the experimental data is proposed, and the accuracy of the model is evaluated. Synchronous measurement data of flow velocity fields around the airfoil and unsteady pressure on the surface of the airfoil were obtained in the wind tunnel test with NACA0015 airfoil at the chord Reynolds number of 6.0×104 and the angle of attack of 18 deg. A proper orthogonal decomposition (POD) is applied to the PIV data, and truncated the limited POD modes and the number of variables to be estimated is reduced. After that, a linear dynamical model for the POD mode coefficients is also constructed by the least square method using the time history of the POD modes amplitudes. The linear estimation based on the instantaneous pressure measurement without the dynamical model and the Kalman filter with the dynamical model are applied to the estimation of the amplitudes of the POD modes. Flow velocity field was reconstructed with estimated amplitudes and POD modes. The result shows that the Kalman filter estimation could work and reduce significant error compared with the linear estimation without the dynamical model.