Integration and Validation of Flow Image Quantification (Flow-IQ) System

Abstract

The first aim of this work was to integrate, validate, and document, a digital particle image quantification (Flow-IQ) software package developed in conjunction with and supported by Aeroprobe Corporation. The system is tailored towards experimental fluid mechanics applications. The second aim of this work was to test the performance of DPIV algorithms in wall shear flows, and to test the performance of several particle sizing algorithms for use in spray sizing and average diameter calculation. Several particle sizing algorithms which assume a circular particle profile were tested with DPIV data on spray atomization, including three point Guassian, four point Gaussian, and least squares algorithms. A novel elliptical diameter estimation scheme was developed which does not limit the measurement to circular patterns. The elliptic estimator developed in this work is able to estimate the diameter of a particle with an elliptic shape, and assumes that the particle is axisymmetric about the x or y axis. Two elliptical schemes, the true and averaged elliptical estimators, were developed and compared to the traditional three point Gaussian diameter estimator using theoretical models. If elliptical particles are theoretically used, the elliptical sizing schemes perform drastically better than the traditional scheme, which is limited to diameter measurements in the x-direction. The error of the traditional method in determining the volume of an elliptical particle increases dramatically with the eccentricity. Monte Carlo Simulations were also used to characterize the error associated with wall shear measurements using DPIV. Couette flow artificial images were generated with various shear rates at the wall. DPIV analysis was performed on these images using PIV algorithms developed by other researchers, including the traditional multigrid method, a dynamically-adaptive DPIV scheme, and a control set with no discrete window offset. The error at the wall was calculated for each data set. The dynamically adaptive scheme was found to estimate the velocity near the wall with less error than the no discrete window offset and traditional multigrid algorithms. The shear rate was found to be the main factor in the error in the velocity measurement. In wall shear velocity measurement, the mean (bias) error was an order of magnitude greater than the RMS (random) error. A least squares scheme was used to correct for this bias error with favorable results. The major contribution of this effort stems from providing a novel elliptical particle sizing scheme for use in DPIV, and quantifies the error associated with wall shear measurements using several DPIV algorithms. A test bed and comprehensive user's manual for Flow-IQ v2.2 was also developed in this work.