A super-resolution approach for uncertainty estimation of PIV measurements

A super-resolution approach is proposed for the a posteriori uncertainty estimation of PIV measurements. The measured velocity field is employed to determine the displacement of individual particle images. A disparity set is built from the residual distance between paired particle images of successive recordings. Within each interrogation window, the disparity set is treated with a statistical analysis to infer the measurement uncertainty: the mean disparity is ascribed to bias errors due to poor particle image sampling or spatial modulation effect; the dispersion of the set is related to precision errors, mainly due to random noise in the recordings and to errors in the PIV interrogation. The performance of the estimator is first assessed via Monte Carlo simulation on a uniform flow field with varying out-of-plane displacement. The uncertainty is accurately estimated in optimal imaging condition, while it is underestimated when the imaging conditions are suboptimal. The experimental assessment is conducted on a water jet experiment. For evaluating the performance of the estimator, the actual measurement error is computed as the difference between measured and a reference displacement field; the latter is built with an advanced processing algorithm that exploits the time redundancy of highly oversampled data to reduce the error of one order of magnitude. The capability of the super-resolution technique to quantify the uncertainty within 0.1 px accuracy is proven.Aerodynamics, Wind Energy and PropulsionAerospace Engineerin