Microscale, scanning defocusing volumetric particle-tracking velocimetry

Abstract

We present a novel defocusing particle tracking velocimetry (PTV) method for micro-fluidic systems. This method delivers 3-dimensional 3-component (3D3C) flow measurements, and does not require an additional calibration procedure to obtain the relationship between particle out-of-plane position and its diameter/intensity. A micro-fluidic device is mounted on a nano-positioning piezo stage that sweeps periodically in the out-of-plane direction. A high-speed camera is synchronized with the stage to capture oversampled two-dimensional microscopy images at different out-of-plane positions. 3D intensity volume is formed by stacking those 2D images. Flow tracers are identified from the intensity volume by a 3D Hessian filter, and segmented by erosion–dilation dynamic thresholding. Fitting of each identified-particle to a defocusing intensity model gives the parameters used in the hybrid algorithm of particle image velocimetry and a generalized multi-parametric PTV. Artificial image data, generated from direct numerical simulations of flow through porous media, are used for error analysis. When compared with classic nearest neighbor tracking our method shows improvements on tracking reliability by 2.5–12%, with seeding density as high as 1.6e−3 particles per voxel. Both mean and rms errors are improved by 80–95% and 49–74%, respectively. An application to micro-fluidic devices is presented by measuring the steady-state flow through a refractive-index-matched randomly-packed glass bead channel. The presented method will serve as a powerful tool for probing flow physics in micro-fluidics with complex geometries.