Surface-Pressure-Based Estimation of the Velocity Field in a Separation Bubble

Abstract

The effectiveness in estimating the velocity field in a laminar separation bubble using surface-pressure-based stochastic estimation methods is examined. A separation bubble is formed over a NACA 0018 airfoil at a Reynolds number of 125,000 and an angle of attack of 4°, while the velocity field and surface pressure fluctuations are measured simultaneously. Single-time-delay and multi-time-delay estimation techniques, in both single-point and multipoint formulations, are employed and compared, showing that the accuracy of the estimates increases notably through the inclusions of additional predictor events in both space and time. The multipoint, multi-time-delay estimation technique is shown to produce the most accurate estimates, with the reconstructed velocity fields capturing all essential flow features across the scales of interest. The accuracy of the estimates is shown to depend on location within the bubble, with the best results found in the region of the mean maximum bubble height, whereas performance decreases near the mean separation point. The latter is due to the transition to turbulence increasing the randomness of fluctuations and can be mitigated through more advanced stochastic estimation, whereas the former is a result of low disturbance amplitudes that fall within the noise level of the measurements.