Measuring the velocity fields of granular flows – Employment of a multi-pass two-dimensional particle image velocimetry (2D-PIV) approach

Sarno, Luca; Carravetta, Armando; Tai, Yih-Chin; Martino, Riccardo; Papa, Maria Nicolina; Kuo, Cheng-Tzu

doi:10.1016/j.apt.2018.08.014

Cited by 58 publications

(32 citation statements)

References 46 publications

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“…The 2D velocity field is determined thanks to the PIVlab® module of Matlab® which provides useful tools for Particle Image Velocimetry (PIV). In our study we employed a Fast Fourier Transformation (FFT) correlation algorithm with multiple passes and deforming windows of 64x64 and 32x32 pixels [31]. The images were captured using the camera at a frame rate…”

Section: Particle Image Velocimetrymentioning

confidence: 99%

Forces and flow induced by a moving intruder in a granular packing: coarse-graining and DEM simulations versus experiments

et al. 2020

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The study of stress transmission and particles' mobility in powder beds constitutes a challenging issue to improve processes such as milling, agglomeration and kneading. In this paper the ability to promote particle mobilities by means of an intruder is investigated. An experimental setup was developed to characterize the particles' mobility at the voxel scale. A Particle Image Velocimetry (PIV) algorithm was employed to determine the velocity fields in the vicinity of an intruder in ascendant vertical motion. A Discrete Element Method (DEM) model was developed to simulate the same system at the particle scale. Velocity maps were determined relying on a coarse-graining procedure and compared to PIV results. During the rise of the intruder the experimental and simulated drag forces are in good agreement. Spatiotemporal correlation between the granular mobility and the state of the force network are analyzed as a function of the height of the intruder. The drag force profile exhibits a relaxation trend, noised by fluctuations whose origin are in successive loading/rupture events. An interpretation of these fluctuations is proposed with regard to the development of preferential paths in the normal force network, and to the localization of sliding at the contacts scale. Finally, we show that global strains are a consequence of the ejection of particles directly in contact with the ends of the intruder.

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Section: Particle Image Velocimetrymentioning

confidence: 99%

Forces and flow induced by a moving intruder in a granular packing: coarse-graining and DEM simulations versus experiments

et al. 2020

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“…The popularization of PIV (Particle Image Velocimetry) in fluid mechanics shows promise as being a better instrumentation for measurement of curved flows [22][23][24][25][26]. In particular, some novel multi-pass window deformation approaches have been used to improve the performance of PIV in measurement of various flows [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Laboratory Study of Secondary Flow in an Open Channel Bend by Using PIV

Bai

Zhu

Chen

et al. 2019

Water

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The present paper aims to gain deeper insight into the evolution of secondary flows in open channel bend. A U-shaped open channel with long straight inflow/outflow reaches was used for experiments. Efforts were made to precisely specify flow conditions and to achieve high precision measurement of quasi-three-dimensional velocities with a multi-pass, two-dimensional PIV (Particle Image Velocimetry) method. The experimental results show that the flow begins to redistribute before entering the bend and it takes a long distance to re-establish to uniform conditions after exiting the bend. Complex secondary flow patterns were found to be present in the bend, as well as in the straight inflow and outflow reaches. A “self-breaking” (process was identified, which correlates stream-wise velocity with the intensity of flow circulation.

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“…2019, 9, x FOR PEER REVIEW 2 of 21 PIV was originally developed from laser speckle velocimetry [10]. Then, PIV was gradually digitized into digital particle image velocimetry (DPIV) with the advent of digital technology [11][12][13] and different illumination solutions, like LED or flash lamps were proposed for specific applications, where a laser source is inadequate due to the high tracers density (e.g., multiphase flows and liquid-granular flows). The intrinsic principle of DPIV is to capture the pattern movements by means of a cross-correlation optimization, as shown in Figure 1a [14].…”

Section: Introductionmentioning

confidence: 99%

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

et al. 2019

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Flow velocity field measurement is important for analyzing flow characteristics of oil-water two-phase immiscible flow in vertical well. Digital particle image velocimetry (DPIV) is an effective velocity field measurement method that has overcome single point measurement limitation of traditional instruments. However, multiphase flow velocity fields generated by DPIV are often accompanied by local false vectors caused by image mismatching, which leads to measurement results with low accuracy. In this paper, the reasons for oil-water two-phase immiscible flow image mismatching in inner diameter 125 mm vertical pipe is identified by studying the DPIV calculation process. This is mainly caused by image noise and poor window following performance that results from poor deformation performance of the interrogation window. To improve deformation performance of the interrogation window, and thus improve the accuracy of the algorithm, iterative closest point (ICP) and moving least squares (MLS) are introduced into the window deformation iterative multigrid algorithm in DPIV postprocessing algorithm. The simulation showed that the improved DPIV algorithm had good matching performance, and thus the false vector was reduced. The experimental results showed that, in light of the present investigation, on average, the improved DPIV algorithm is found to yield an accuracy improvement of~6%; the measurement uncertainty and reproducibility of the improved DPIV algorithm were 0.149 × 10 −3 m/s and 1.98%, respectively.

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Measuring the velocity fields of granular flows – Employment of a multi-pass two-dimensional particle image velocimetry (2D-PIV) approach

Cited by 58 publications

References 46 publications

Forces and flow induced by a moving intruder in a granular packing: coarse-graining and DEM simulations versus experiments

Forces and flow induced by a moving intruder in a granular packing: coarse-graining and DEM simulations versus experiments

Laboratory Study of Secondary Flow in an Open Channel Bend by Using PIV

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

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