ON ERRORS OF DIGITAL PARTICLE IMAGE VELOCIMETRY

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) err ors, 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 part icle images. Two processing techniques are used to extract the particl e displacements. First, a CC method utilizing the FFT algorithm for fa st processing is implemented. Second, a particle image pattern matchin g (PIPM) technique, usually requiring a direct computation and therefo re 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 les s 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 PI PM without adding much computational effort. A peak-compensation techn ique is also introduced to make the mean-bias error negligible in comp arison 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.