Uncertainty on PIV mean and fluctuating velocity due to bias and random errors

Abstract

Particle image velocimetry is a powerful and flexible fluid velocity measurement tool. In spite of its widespread use, the uncertainty of PIV measurements has not been sufficiently addressed to date. The calculation and propagation of local, instantaneous uncertainties on PIV results into the measured mean and Reynolds stresses are demonstrated for four PIV error sources that impact uncertainty through the vector computation: particle image density, diameter, displacement and velocity gradients. For the purpose of this demonstration, velocity data are acquired in a rectangular jet. Hot-wire measurements are compared to PIV measurements with velocity fields computed using two PIV algorithms. Local uncertainty on the velocity mean and Reynolds stress for these algorithms are automatically estimated using a previously published method. Previous work has shown that PIV measurements can become ‘noisy’ in regions of high shear as well as regions of small displacement. This paper also demonstrates the impact of these effects by comparing PIV data to data acquired using hot-wire anemometry, which does not suffer from the same issues. It is confirmed that flow gradients, large particle images and insufficient particle image displacements can result in elevated measurements of turbulence levels. The uncertainty surface method accurately estimates the difference between hot-wire and PIV measurements for most cases. The uncertainty based on each algorithm is found to be unique, motivating the use of algorithm-specific uncertainty estimates.