Wall-shear stress measurement via oil-film interferometry being enhanced by quasi-bivariate variational mode decomposition

Characterized by high accuracy and operational simplicity, oil-film interferometry (OFI) has served as an effective wall-shear stress (WSS) measurement technique over the past decades. It utilizes the monochromatic light interference principle to measure the temporal variation of oil-film thickness caused by WSS, and calculates time-averaged WSS based on the variation of interference fringe width. However, small-scale noise, which is caused by defects on the target surface, ambient dust, and local oil-film non-uniformity, contaminates the interference fringe patterns and directly increases the measurement uncertainty. One practical way is to apply denoising methods to improve the accuracy of identifying the centroids of fringes. In the present study, quasi-bivariate variational mode decomposition (QBVMD) is proposed as a self-adaptive denoising method to remove small-scale noise. Since no characteristic information of fringe patterns is required in the QBVMD-based denoising method, it has higher accuracy and lower uncertainty than the conventional OFI denoising methods, which need to pre-set the mask signal or the bandpass frequency, i.e., cross-correlation or spectral filtering. Thus, it facilitates the automatic identification of time-varying inhomogeneous fringes. Two sets of experiments, i.e., WSS measurement on either a canonical flat-plate turbulent boundary layer (TBL) or a TBL perturbed by micro vortex generators (MVG), were conducted to validate the applicability of this QBVMD-OFI method. The former experiment shows that the accuracy of QBVMD-OFI is equivalent to near-wall high-resolution particle image velocimetry, and is considerably higher than that of a dual hot-film sensor. As for the latter, QBVMD-OFI provides sufficient spatial resolution to resolve fine WSS structures generated by MVG.