Uncertainty quantification in particle image velocimetry

Quantification of uncertainty in a stereoscopic particle image velocimetry measurement

In Stereoscopic Particle Image Velocimetry (Stereo-PIV), the three velocity components are obtained by illuminating a planar region in the fl ow fi eld and recording the region of interest using two cameras at an angle. Calibration, planar velocity estimation, and velocity reconstruction are the three essential steps involved in the process. Earlier efforts to quantify the accuracy in a Stereo-...

Particle Image Velocimetry Correlation Signal-to-noise Metrics, Particle Image Pattern Mutual Information and Measurement uncertainty Quantification

Holography and particle image velocimetry

The contribution of holographic techniques to particle velocity measurements is discussed. Two-dimensional holographic particle image velocimetry and three-dimensional holographic velocimetry are described and compared. Their advantages and limits are discussed and the latest progress in this domain is presented, together with perspectives for the near future.

Recent Advances in Micro-Particle Image Velocimetry

Microfluidic devices are becoming increasingly common and are seen in applications ranging from biology to nanotechnology and manufacturing. Flow behavior in these small domains can often be counterintuitive because of the low Reynolds number or the relative importance of surface forces. Microparticle image velocimetry (μPIV) is a quantitative method that can be used to characterize the perform...

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) 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 sc...