Particle Tracking Thesis 2021

2D/3D PTV Application and Technique

Particles settling dynamics PIV/PTV
Abstract: The dynamics of heavy inertial particles evolving in a fluid are of interest in many fields. They are found both in nature (water droplets in clouds, sediments in rivers and in the oceans, planetary accretion disks) and in human activities and technological applications (fuel drops in combustion chambers, chemical reactors). These systems are complex, their modelling using often strong simplifying hypotheses, and experimental data is still required in their study. A large range of behaviours can be found in such dispersed two-phase flows. This work focuses on two of these. The first is clustering, or the observation that particles accumulate in specific regions and leave others void. The second is settling velocity alteration, as particles have been observed to fall either faster or slower than in a quiescent fluid. These two phenomena are intertwined, and depend on parameters like the size and density of the particles, what the carrier phase is (water or air usually) and whether it is in a quiescent or turbulent state. An experimental device was built in which small (diameters of at most 200 µm) solid particles settle in water. Particles of various densities have been separated by size by sieving. This allows access to a large variety of particle properties. An easy to implement double-measurement technique allowing simultaneous measurements of particle and fluid velocities was developed, providing insight into particle-fluid interactions that was seldomly achieved in previous works. Increases of the settling velocity of particles falling in a quiescent fluid have been observed and could be attributed to the development of a flow that pushes the particles down. Voronoï analysis were also performed, but could not confirm with certainty whether particles formed clusters or not. This work gives interesting data, relevant in the study of particles settling in quiescent fluids in closed spaces. It also provides a reference point for future works where turbulence will be added to the system.
Keywords: Inertial particles, Simultaneous PIV and PTV, settling speed
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Thesis Author: David De Souza
Clustering and settling dynamics of inertial particles under turbulence
Abstract: Turbulent particle-laden flows are widespread in industrial applications, and natural phenomena. Over the last decades, two observations: preferential concentration, and particle settling velocity modification have stood out as the most relevant consequences of such particle - turbulence interactions. Given the complexity of the problem, this work is composed of four work packages. The first package involves a pitfall analysis of the Vorono" tessellation method, which is widely used to quantify preferential concentration. We found some pitfalls that compromise the results of the analysis using uni-dimensional records. In addition, we propose a new method to disentangle turbulence driven clusters from random spatial fluctuations, a common problem reported by other researchers. The second package involve the analysis of the carrier phase turbulence in our wind tunnel facility. In this regard, we conjecture that the different turbulence generators (active, open, and passive grids) do change the turbulence cascade, and thereby, they could impact the particles preferential concentration and settling behavior. To this aim, we have analysed active grid generated flows, and found that an active grid left open (with minimum blockage) exhibits scalings similar to those found in fractal grids. Moreover, The integral length scale could not be easily computed for active grid generated flows using triple random protocols due to the behavior of the autocorrelation function in such flows, which does not cross zero. We propose a new method to tackle this problem which could be easily applied in a myriad of situations. The third package consist of estimating the turbulent dissipation rate on the carrier phase due to the particle presence. By means of an extension of the Rice theorem, which relates the Taylor length scale with the average distance between zero crossings, we have proposed a method to estimate the carrier phase turbulence in the presence of particles. This method uses particle datasets recorded by phase doppler interferometry. Our results are consistent with previous experiments, and numerical simulations. The fourth package refers to the particle settling modification. We found that the Taylor Reynolds number Re_lambda is the leading order contributor the particles settling modification: at increasing values of Re_lambda the settling velocity of the particles is reduced. Also, at increasing values of Re_lambda the boundaries between positive, and negative particle settling modification shifts to smaller values of the Rouse number Ro=V_T/u.
Keywords: Inertial particles, clustering, high-speed imaging
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Thesis Author: Daniel Mora-Paiba
Abstract: This thesis will investigate particle identification in the 2D noiseless image. The modified cascade cross-correlation method (MCCM) is first introduced. The idea of it is to perform cross-correlation on images and apply a nonlinear least-square solver to give sub-pixel accuracy. Next is the support set method, which uses a morphological way to segment images. An iterative scheme is applied to each segment to acquire particle location. Finally, the technique developed from 3D tomographic PIV which is projected to 2D then utilizes nonnegative least-square (NNLS) to retrieve particle location. This method is superior in the ability to quantitatively find particles and the accuracy in constant particle size images. For images that the particle size varied, we proposed a method that required MCCM to determine the size to apply in NNLS and the result from NNLS to reconstruction image. After getting the reconstructed image, the residual can be calculated. The particle position corresponded to the lowest residual is selected to be the result that is shown better than other methods.
Keywords: Particle Identification Technique for Particle Tracking Velocimetry Application in Noiseless Image
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Thesis Author: Yi-Lin Liu
Abstract: For Particle Tracking Velocimetry (PTV), the presence of digital image noise deteriorates both the particle localization and identification performance. In this thesis, a proposed workflow combines a state-of-the-art deep-learning based denoising architecture, U-Net image segmentation technique, and particle reconstruction through linear model inversion. A number of simulation tests under different noise conditions and particle density using synthetically generated images are performed in order to evaluate the performance improvement against traditional methods. At the particle density of 0.10 particle per pixel and 5 percent image noise, the proposed workflow reduces the Mean Localization Error by 24 percent compared to clean image. The workflow requires no prior knowledge in noise level nor the particle density. Also, the Gaussian residual image noise optimization for particle reconstruction technique is proposed for non-overlapping particle image in presence of image noise.
Keywords: Particle Identification Technique for Particle Tracking Velocimetry Application in Noiseless Image
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Thesis Author: Shinaphadh Damrongsiri