On errors of digital particle image velocimetry

Abstract

Abstract. The goal of the present study is to quantify and reduce, when possible, errors in two-dimensional digital particle image velocimetry (DPIV). Two major errors, namely the mean bias and root-mean-square (RMS) errors, have been studied. One fundamental source of these errors arises from the implementation of cross correlation (CC). Other major sources of these errors arise from the peak-finding scheme, which locates the correlation peak with a sub-pixel accuracy, and noise within the particle images. Two processing techniques are used to extract the particle displacements. First, a CC method utilizing the FFT algorithm for fast processing is implemented. Second, a particle image pattern matching (PIPM) technique, usually requiring a direct computation and therefore more time consuming, is used. Using DPIV on simulated images, both the mean-bias and RMS errors have been found to be of the order of 0.1 pixels for CC. The errors of PIPM are about an order of magnitude less than those of CC. In the present paper the authors introduce a peak-normalization method which reduces the error level of CC to that of PIPM without adding much computational effort. A peak-compensation technique is also introduced to make the mean-bias error negligible in comparison with the RMS error. Noise in an image suppresses the mean-bias error but, on the other hand, significantly amplifies the RMS error. A digital video signal usually has a lower noise level than that of an analogue one and therefore provides a smaller error in DPIV.

High-resolution numerical modelling of flow—vegetation interactions

“…In addition, an adaptive correlation method was used whereby initially, interrogation regions of size 32 x 32 pixels and subsequently 16 x 16 pixels were used to increase the accuracy of the eventual 8 x 8 pixel cross-correlation. With this methodology, the mean bias error (accuracy) and RMS error (precision) of the derived velocities is in the order of 0.1 pixels (Huang et al 1997) …”

Multi-frame pyramid correlation for time-resolved PIV

“…The absolute random error e R , rather independent of the time separation Dt between the snapshots (for small out-of-plane motion, F O [ 0.8, Keane and Adrian 1992) and of the particle image displacement. The error is mainly not correlated in time, related to the image quality and the flow characteristics (Huang et al 1997). 2.…”

Recent advances in digital particle image velocimetry methods for flow motion analysis

“…PIV is a non-invasive, full field optical measuring technique, utilizing the particle-based visualization method. Due to the high accuracy of results and wide area of application from laminar to turbulent flow in different geometrical complexes, this method has become the dominant tool for obtaining velocity information in fluid motion [11][12][13][14][15].…”