Elimination of peak-locking error in PIV analysis using the correlation mapping method

摘要: The peak-locking effect causes mean bias in most of the existing cross-correlation based algorithms for PIV data analysis. This phenomenon is inherent to smooth curve-fitting through discrete correlation values, which used obtain sub-pixel part displacement. Almost all effective methods solve this problem require iterations. In paper we introduce a new technique obtaining accuracy, bypasses curve fitting, and eliminates effect, but does not principles 'correlation mapping method' (CMM) are on following logic: if one uses bi-cubic interpolation express second image first unknown displacement, between them becomes third-order polynomial whose coefficients depend image. Matching with measured provides an equation displacement each point map. A least-squares fit values vicinity peak (e.g. 5 ? points) estimate particle including its part. We combine method corrections distortion (PID) further reduce uncertainty velocity measurements. Three iterations typically achieve converged results. CMM-PID tested using synthetic experimental data. disappears cases. Even 'random' error substantially smaller than that obtained conventional fit. Issues related streamline curvature estimates gradients also discussed.