Wavelet-Based Optical Flow For High-Resolution Velocimetry In Wall-Bounded Fluid Flows

The performance of a wavelet-based optical flow velocimetry (wOFV) algorithm in extracting high accuracy and high resolution velocity fields from tracer particle images in wall-bounded flows is as sessed. wOFV is first evaluated using a DNS database of a turbulent boundary layer, which served as ground truth synthetic data. The sensitivity of wOFV to the regularization parameter (λ) is quantified and results are compared to commercially available PIV crosscorrelation-based processing. Results on synthetic data indicate a slight preference towards under-regularization in the viscous sublayer region to minimise error. Nonetheless, tests on synthetic data reveal that wOFV can modestly outperform PIV in terms of accuracy for a broad λ range. For the synthetic dataset, wOFV revealed clear advantages over PIV in terms of vector resolution, being able to resolve 29 vectors in the viscous sublayer (versus 5 for PIV) and provide 13 vectors closer to the wall than PIV. wOFV was also applied to experimental data of a turbulent boundary layer near the leading edge of a side-wall quenching burner setup and compared to PIV. Good agreement between the two velocimetry techniques was found, with wOFV exhibiting superior vector resolution. Based on theoretical relations of the viscous sublayer, a physically justified λ weighting for wOFV was d etermined. With this setting, wOFV was able to resolve further and more accurately into the viscous sublayer. Analysis of the turbulent velocity fluctuations revealed some spurious results for PIV in close proximity to the wall, leading to significantly exaggerated turbulence intensity in the viscous sublayer region. wOFV did not exhibit this same effect, revealing that it is less biased by the presence of the wall and decreased image signal-to-noise ratio. These aspects show that wOFV can provide improved accuracy and reliability in resolving turbulent motion occurring in the vicinity of physical boundaries.