Matthew Giarra

Nanoparticle flow velocimetry with image phase correlation for confocal laser scanning microscopy

We present a new particle image correlation technique for resolving nanoparticle flow velocity using confocal laser scanning microscopy (CLSM). The two primary issues that complicate nanoparticle scanning laser image correlation (SLIC)–based velocimetry are (1) the use of diffusion-dominated nanoparticles as flow tracers, which introduce a random decorrelating error into the velocity estimate, and (2) the effects of the scanning laser image acquisition, which introduces a bias error. To date, no study has quantified these errors or demonstrated a means to deal with them in SLIC velocimetry. In this work, we build upon the robust phase correlation (RPC) and existing methods of SLIC to quantify and mitigate these errors. First, we implement an ensemble RPC instead of using an ensemble standard cross-correlation, and develop a SLIC optimal filter that maximizes the correlation strength in order to reliably and accurately detect the correlation peak representing the most probable average displacement of the nanoparticles. Secondly, we developed an analytical model of the SLIC measurement bias error due to image scanning of diffusion-dominated tracer particles. We show that the bias error depends only on the ratio of the mean velocity of the tracer particles to that of the laser scanner and we use this model to correct the induced errors. We validated our technique using synthetic images and experimentally obtained SLIC images of nanoparticle flow through a micro-channel. Our technique reduced the error by up to a factor of ten compared to other SLIC algorithms for the images tested in this study. Moreover, our optimized RPC filter reduces the number of image pairs required for the convergence of the ensemble correlation by two orders of magnitude compared to the standard cross correlation. This feature has broader implications to ensemble correlation methods and should be further explored in depth in the future.

Measurement of fluid rotation, dilation, and displacement in particle image velocimetry using a Fourier–Mellin cross-correlation

Traditional particle image velocimetry (PIV) uses discrete Cartesian cross correlations (CCs) to estimate the displacements of groups of tracer particles within small subregions of sequentially captured images. However, these CCs fail in regions with large velocity gradients or high rates of rotation. In this paper, we propose a new PIV correlation method based on the Fourier?Mellin transformation (FMT) that enables direct measurement of the rotation and dilation of particle image patterns. In previously unresolvable regions of large rotation, our algorithm significantly improves the velocity estimates compared to traditional correlations by aligning the rotated and stretched particle patterns prior to performing Cartesian correlations to estimate their displacements. Our algorithm, which we term Fourier?Mellin correlation (FMC), reliably measures particle pattern displacement between pairs of interrogation regions with up to??180? of angular misalignment, compared to 6?8? for traditional correlations, and dilation/compression factors of 0.5?2.0, compared to 0.9?1.1 for a single iteration of traditional correlations.We apply our FMC algorithm to synthetic computer-generated PIV images with known velocity and vorticity fields, and to an experimentally measured flow field. Our results show that combining FMC with discrete window offset (DWO) or iterative image deformation (IID) algorithms decreases the mean and variance of displacement and vorticity errors compared to traditional correlations, and that FMC accelerates the convergence of IID.

Fluid Motion Induced by Spark Plasma: Development of Particle Image Velocimetry Measurements

There has been increased interest in the use of plasma actuators for flow control in aerodynamics and combustion to improve efficiency and reduce emissions. Spark plasma actuators have capabilities of inducing heat and momentum to the flow field. The flow field generated by this plasma induces complex pressure and temperature gradients that lead to the development of complex flow structures. The experiment described in this research is particularly difficult due to its small scale, and the dynamic range of velocities that are induced by the flow field. This flow field is yet to be quantified by previous research. The purpose of this experiment is to develop a method of capturing and processing the flow field generated to present accurate results of the flow induced by the spark. A 2-D PIV system is used to capture the images and the appropriate pulse separation and a 48 x 48 pixel interrogation window size are chosen to analyze the flow field induced by 3 different electrode configurations. The repeatability of the flow field is assessed and the turbulent nature of the flow field is revealed. Voltage measurements show that there is varying deviation in the voltage during the spark. Analysis of the flow field shows 70-90% deviations in magnitude of velocity. The effects of using ensemble averaging, ensemble correlation and correlation of ensemble images on maximizing signal to noise ratio (SNR) is assessed. Preliminary results show flow concentrated in the center of the electrode gap at initial times, followed by an outward flow toward the surrounding gas.