Holographic astigmatic particle tracking velocimetry (HAPTV)

Abstract

The formation of twin images in digital inline holography (DIH) prevents the placement of the focal plane in the center of a sample volume for DIH-based particle tracking velocimetry (DIH-PTV) with a single camera. As a result, it is challenging to apply DIH-PTV for flow measurements in large-scale laboratory facilities or many field applications where it would otherwise be desirable due to the low cost and compact setup. Here we introduce holographic astigmatic PTV (HAPTV) by inserting a cylindrical lens in the optical setup of DIH-PTV, generating distorted holograms. Such distortion is subsequently utilized in a customized reconstruction algorithm to distinguish tracers positioned on different sides of the focal plane which can in turn be placed in the middle of a sample volume. Our HAPTV approach is calibrated under high (1 µm/pixel) and low (10 µm/pixel) magnifications with an error standard deviation of 4.2 µm (one particle diameter) and 120.7 µm (~5 times the particle diameter), respectively. We compare the velocity field of a laminar jet flow obtained using HAPTV and conventional PIV to illustrate the accuracy of the technique when applied to practical flow measurement applications. The work demonstrates that HAPTV improves upon the depth of field of conventional astigmatic PTV and enables the implementation of DIH-based PTV for in situ applications.

A method combining half width of point spread function with maximum gradient of image gray for single-camera three-dimensional spray velocity detection

“…V M3D calculated from the average 116 images of each area by 3D-PTV measurement and that from the CFD result are shown in figure 9(a), which shows that the velocity distribution calculated from 3D-PTV is similar to that from the CFD results. The relative error versus locations with error bars are shown in figure 9(b); the max uncertainty is 5.647 ± 2.93% at a location of 1 mm, which is a little higher than the result of 5.5% in the study of Zhou et al [35]. However, their method is an improved holographic PTV, which requires a more complex optical equipment layout than ours, so this method can be used as a low-cost alternative to other 3D speed detection methods when certain errors are acceptable.…”

“…To verify the accuracy of the method applied for 3D-PTV velocity measurement, a verification experiment similar to the validation in the study of Zhou et al [35] is designed. We apply the 3D-PTV to measure the 3D velocity of an air jet flow with 50 µm hollow glass spheres as tracer particles and compare the results with CFDs.…”

A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

“…Using μPTV techniques can allow researchers to characterize parameters based on localized flow patterns [15]. As a result, three-dimensional (3D) velocity profiles can be computed through methods such as particle labeling in conjunction with an excitation laser [1,16], deconvolution microscopy [17], hologram μPTV using particle scattering from an excitation laser [18], and defocusing particle image techniques [11]. These methods of μPTV are very effective methods to visualize microfluidic flow profiles.…”

“…One major drawback is that they are all computationally expensive [19]. Additionally, they require a great amount of optical instrumentation, such as lenses, detectors, and light sources, to compute the velocity profiles [18,19].…”

On the calibration of Astigmatism particle tracking velocimetry for suspensions of different volume fractions

“…Efforts are also undertaken to further adapt and extend the classical APTV technique. Zhou et al (2020) introduced a modification utilizing Holographic imaging principles to establish Holographic Astigmatic Particle Tracking Velocimetry (HAPTV). Using a nozzle flow as a test case, they could successfully validate their measurement technique.…”