An examination of parameters affecting the control of a jet vectoring technique used in the Coanda-assisted Spray Manipulation (CSM) is presented. The CSM makes use of an enhanced Coanda effect on axisymmetric geometries through the interaction of a high volume primary jet flowing through the center of a collar and a secondary high-momentum jet parallel to the first and adjacent to the convex collar. The control jet attaches to the convex wall and vectors according to known Coanda effect principles, entraining and vectoring the primary jet, resulting in controllable r-θ directional spraying. Several control slots (both annular and unique sizes) and expansion radii were tested over a range of momentum flux ratios to determine the effects of these variables on the vectored jet angle and profile. Two- and three-component Particle Image Velocimetry (PIV) was used to determine the vectoring angle and the profile of the primary jet in each experiment. The experiments show that the control slot and expansion radius, along with the momentum ratios of the two jets, predominantly affected the vectoring angle and profile of the primary jet. The Reynolds number range for the primary jet at the exit plane was between 20,000 and 80,000. The flow was in the incompressible Mach number range (Mach< 0.3).

The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle Image Velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty due to the PIV calculation of each individual velocity measurement is presented. The relationship between four error sources and their contribution to PIV error is first determined. The sources, or parameters, considered are particle image diameter, particle density, particle displacement, and velocity gradient, although this choice in parameters is arbitrary and may not be complete. This information provides a four-dimensional "uncertainty surface" for the PIV algorithm used. After PIV processing, our code "measures" the value of each of these parameters and estimates the velocity uncertainty for each vector in the flow field. The reliability of the methodology is validated using known flow fields so the actual error can be determined. Analysis shows that, for most flows, the uncertainty distribution obtained using this method fits the confidence interval. The method is general and can be adapted to any PIV analysis.

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A left ventricular assist device is a mechanical pump implanted in patients with heart failure that continuously takes blood from the left ventricle and delivers it to the aorta, thus decreasing ventricular load. The device is typically considered as a ‘bridge to transplant’, i.e. as a temporary therapy, and involves several risks. Modified ventricular hemodynamics due to a heart pump implantation is studied in-vitro using an elastic ventricle. The ventricle is incorporated into a pulse duplicator setup, which prescribes realistic pulsatile inflow/outflow to mimic a weak ejection fraction. A continuous axial pump mimics a ventricular assist device and its effect on the ventricular hemodynamics is investigated as a function of the pump flow suction. Using particle image velocimetry, pump flow effectiveness at providing unloading on the ventricle and increasing ejection is observed and understanding if proper recirculation of the myocardium down to the apex is restored under varying flow rate.

Experiments have been performed using the Air Force Research Laboratory (AFRL) Blade-Row Interaction (BRI) rig to investigate interactions between a loaded stator and transonic rotor. The BRI rig is a high-speed, highly loaded compressor consisting of a swirler/deswirler, a transonic rotor and a stator. The swirler/deswirler of the BRI rig is used to simulate an embedded transonic fan stage with realistic geometry which produces a wake through diffusion. Details of the unsteady flow field between the stator and rotor were obtained using Particle Image Velocimetry (PIV). Flow visualization images and PIV data that facilitate analysis of vortex shedding, wake motion, and wake-shock-interactions in the blade row are analyzed for three stator/rotor axial spacings and two stator loadings. The data analysis focuses on measuring and comparing, for the different spacings and loading, the vortex size, strength, and location as it forms on the stator trailing edge and propagates downstream into the rotor passage. It was observed that more than one vortex was shed with the passing of a rotor bow shock. These vortices were categorized as small and large vortices with a ~20% decrease in strength. The large vortices were compared at similar location and results show that vortex strength increased as spacing between stator and rotor decreased due to the increased strength of the rotor bow shock impacting the stator trailing edge. Changes in stator loading also affected shed vortex strength. A decrease in stator loading resulted in a decrease in the strength of the vortex shed. The smaller vortices were not affected by a change in spacing but strength was directly related to the loading.

The need exists for finding a quantitative method for validating crowd simulations. One approach is to use optical flow of videos of real crowds to obtain velocities that can be used for comparison to simulations. Optical flow, in turn, needs to be calibrated to be useful. It is essential to show that optical flow velocities obtained from crowd videos can be mapped into the spatially averaged velocities of the observed trajectories of crowd members, and to quantify the extent of the correlation of the results. This research investigates methods to uncover the best conditions for a good correlation between optical flow and the average motion of individuals in crowd videos, with the aim that this will help in the quantitative validation of simulations. The first approach was to use a simple linear proportionality relation, with a single coefficient, alpha, between velocity vector of the optical flow and observed velocity of crowd members in a video or simulation. Since there are many variables that affect alpha, an attempt was made to find the best possible conditions for determining alpha, by varying experimental and optical flow settings. The measure of a good alpha was chosen to be that alpha does not vary excessively over a number of video frames. Best conditions of low coefficient of variation of alpha using the Lucas-Kanade optical flow algorithm were found to be when a larger aperture of 15x15 pixels was used, combined with a smaller threshold. Adequate results were found at cell size 40x40 pixels; the improvement in detecting details when smaller cells are used did not reduce the variability of alpha, and required much more computing power. Reduction iii in variability of alpha can be obtained by spreading the tracked location of a crowd member from a pixel into a rectangle. The Particle Image Velocimetry optical flow algorithm had better correspondence with the velocity vectors of manually tracked crowd members than results obtained using the Lukas-Kanade method. Here, also, it was found that 40x40 pixel cells were better than 15x15. A second attempt at quantifying the correlation between optical flow and actual crowd member velocities was studied using simulations. Two processes were researched, which utilized geometrical correction of the perspective distortion of the crowd videos. One process geometrically corrects the video, and then obtains optical flow data. The other obtains optical flow data from video, and then geometrically corrects the data. The results indicate that the first process worked better. Correlation was calculated between sets of data obtained from the average of twenty frames. This was found to be higher than calculating correlations between the velocities of cells in each pair of frames. An experiment was carried out to predict crowd tracks using optical flow and a calculated parameter, beta, seems to give promising results.

Micro particle image velocimetry (microPIV) is a non-intrusive tool for visualizing flow in micron-scale conduits. Using this investigative instrument, two experimental studies were performed to understand flow behaviors in microfluidic channels - a sudden expansion step flow and laminar velocity profile variation in diffusion driven mixing. First, flow in a backward facing step feature (1:5 expansion ratio) in a microchannel was taken as the subject of microPIV flow visualization. The onset and development of a recirculation flow was studied as a function of flow rate. This flow pattern was further used to investigate two major parameters affecting microPIV measurements; the depth-of-focus and recording time-intervals between images in a microPIV image pair. The onset of recirculation was initiated at flow rates that correspond to Reynolds numbers, Re > 95, which is well beyond the typical working range of microfluidic devices (Re=0.01-10). The recirculation flow has a 3D structure due to the dimensions of the microchannel and the effect of no slip condition on the walls. Ensemble cross-correlation was found not to be sensitive to variations of depth-of-focus and the output flow fields were similar as long as the overall optical focus remained within the upper and lower bounds of the microchannel. However, variations of time intervals between images in a microPIV pair, resulted in quantitatively and qualitatively different flow patterns for a given constant flow rate and depth-of-focus. In the second experiment, the effect of the laminar velocity profile and its variation on mixing phenomena at the reduced scale is studied. It is shown that the diffusive mass flux between two miscible streams, flowing in a laminar regime in a microchannel, is enhanced if the velocity at their diffusion interface is increased. Based on this idea, an in-plane passive micromixing concept is proposed and implemented in a working device (sigma micromixer). This mixer shows considerable mixing performance by periodically varying the flow velocity profile, such that the maximum of the profile coincides with the transversely progressing diffusion fronts repeatedly throughout the mixing channel. microPIV has been used to visualize the behavior of laminar flow inside the micromixer device and to confirm the periodic variation of the velocity profile through the mixing channel.

We present two distinct projects in this article. In the first project, an experiment aiming to quantify the impacts of material acoustic impedance and thickness on single laser-induced cavitation bubble dynamics with measurements of exerted pressure on a specific material in order to identify the primary sources most responsible for material damages is presented in this article. Two types of major pressure sources have been identified. For bubble collapsing near a rigid wall, when standoff ratio γ < 0.6, the ring collapse is the most prominent pressure source. The jet takes the strongest effects at γ = 1.12. The pressure is minimal at γ = 0.913. After the first jet impingement, a second ring collapse will follow and input the maximum pressure to the wall. By further increasing γ, a similar pressure profile of the second collapse to the first collapse is achieved, during which the pressure for the second collapse is minimal at γ = 1.41 and for the jet is maximum at γ = 1.79. Compared with the maximum pressure dealt by the first jet, the second ring collapse and jet are increasing much faster with the bubble size and eventually overwhelm the first jet. However, the first ring collapse is still the most dominant pressure source responsible for material damages. For wall featuring smaller acoustic impedance or thickness that cannot be approximated to a rigid body, the ring collapse and jet occur at smaller standoff ratios. The cavity shrinking rate suggests the maximum pressure exerted on the wall at applicable standoff ratios should be smaller than that on a rigid wall. In the second project, a comprehensive collection of dynamics of one and two laser-induced cavitation bubbles collapsing near different boundaries is presented in this article by measuring the velocity fields using particle image velocimetry (PIV) techniques. Cases include a single bubble collapsing near the hard, medium, and soft walls characterized by acoustic impedance, free collapse of two bubbles, and two bubbles collapsing near the hard and soft walls. We implemented the most significant velocity and top velocity regions derived from each velocity field to analyze the features of these cases. Before converging to free collapse, the bubble near the hard wall experienced a significant velocity decrease before collapse, the bubble near the medium wall was severely damped at a specific standoff distance, and the bubble near the soft wall collapsed much earlier and preserved a linear velocity region at low speed. Free collapse of two same bubbles underwent a decrease of acceleration before collapse. Decreasing the size of one bubble caused a jet in the other. With the presence of a hard wall near two bubbles, the bubble closer to it may be stretched to a cavity with a high aspect ratio, leading to very mild collapse. With a bigger bubble between a smaller one and the soft wall, the merging cavity may suppress the tendency of jet formation, making the velocity stay at low levels throughout the lifetime. For configurations regarding single bubbles collapsing near a wall and free collapse of two same bubbles, we performed data scaling to study the velocity variations for different bubble sizes by controlling the standoff ratios and assessed the data quality aided by curving fitting and statistics. Results indicated measured velocity regarding a single bubble collapsing near the wall over its diameter remained the same given a standoff ratio, while measured velocity did not change given a standoff ratio for free collapse of two same bubbles within the scope of the experiment. In addition, we detailed the experimental setup and water treatment for better signal-to-noise ratios as well as validated the system from both the PIV and high speed imaging approaches using free collapse of a single bubble to ensure the reliability of this experiment. / Doctor of Philosophy / The phenomenon of cavitation extensively exists. These small and transient bubbles are observed typically in fast moving fluids, e.g., shaking a bottle of water. Each bubble experi- ences a process of growth, collapse, rebound, and collapse again before it is gone. Although the bubble is tiny, the collapse of a bubble releases considerable pressure, which is intense enough to damage nearby objects over time. This interaction between bubbles and objects depends highly on the types of objects such as the materials and thickness. To study how the bubble behaves near a wall (object) and explain how the wall is damaged, we present two projects in this article. In the first project, we created a bubble near a wall at differ- ent bubble-to-wall distances and tracked how the bubble changed its shape until collapse with a fast speed camera. This work was repeated for multiple different wall materials and thickness. We then measured the pressure exerted by a bubble at a series of different bubble-to-wall distances on a specific wall equipped with a sensor. By comparing and sum- marizing results from both the bubble shape changes near different walls and the pressure measurement, we found the relationship between the magnitude of pressure and the distance between the bubble and the wall. In the second project, we implemented the particle image velocimetry (PIV) techniques to measure the velocity fields. By feeding particles into the fluid, PIV tracks the location differences of particles in two subsequent frames to determine the velocity of every point. Based on that, we obtained a collection of velocity fields of interaction between single bubbles and walls, two bubbles, and two bubbles and walls.

A unique, super spatio-temporal resolution Digital Particle Image Velocimetry (DPIV) system with capability of resolving velocities in a multi-phase flow field, using a very sophisticated novel Dynamically Adaptive Hybrid velocity evaluation algorithm has been developed The unique methodology of this powerful system is presented, its specific distinctions are enlightened, confirming its flexibility, and its superior performance is established by comparing it to the most established best DPIV software implementations currently available. Taking advantage of the most recent advances in imaging technology coupled with state of the art image processing tools, high-performing validation schemes including neural networks, as well as a hybrid digital particle tracking velocimeter (DPTV), the foundation for a unique system was developed. The presented software enables one to effectively resolve tremendously demanding flow-fields. The resolution of challenging test cases including high speed cavitating underwater projectiles as well as high pressure spray demonstrate the power of the developed device. / Master of Science

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