Quasi-bivariate variational mode decomposition as a tool of scale analysis in wall-bounded turbulence

The identification and separation of multi-scale coherent structures is a critical task for the study of scale interaction in wall-bounded turbulence. Here, we propose a quasi-bivariate variational mode decomposition (QB-VMD) method to extract structures with various scales from instantaneous two-dimensional (2D) velocity field which has only one primary dimension. This method is developed from the one-dimensional VMD algorithm proposed by Dragomiretskiy and Zosso (IEEE Trans Signal Process 62:531–544, 2014) to cope with a quasi-2D scenario. It poses the feature of length-scale bandwidth constraint along the decomposed dimension, together with the central frequency re-balancing along the non-decomposed dimension. The feasibility of this method is tested on both a synthetic flow field and a turbulent boundary layer at moderate Reynolds number (\(Re_{\tau }\) = 3458) measured by 2D particle image velocimetry (PIV). Some other popular scale separation tools, including pseudo-bi-dimensional empirical mode decomposition (PB-EMD), bi-dimensional EMD (B-EMD) and proper orthogonal decomposition (POD), are also tested for comparison. Among all these methods, QB-VMD shows advantages in both scale characterization and energy recovery. More importantly, the mode mixing problem, which degrades the performance of EMD-based methods, is avoided or minimized in QB-VMD. Finally, QB-VMD analysis of the wall-parallel plane in the log layer (at \(y/\delta\) = 0.12) of the studied turbulent boundary layer shows the coexistence of large- or very large-scale motions (LSMs or VLSMs) and inner-scaled structures, which can be fully decomposed in both physical and spectral domains.