Measuring particle dynamics in a fluidized bed using digital in-line holography

Li, Cheng; Panday, Rupendranath; Gao, Xi; Hong, Jiarong; Rogers, William A.

doi:10.1016/j.cej.2020.126824

Cited by 13 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in volumetric and tomographic imaging are opening promising avenues to obtain three-dimensional concentration and velocity measurements, e.g. by magnetic resonance imaging (Müller et al 2008), magnetic particle tracking (Buist et al 2014), electrical capacitance tomography (Weber et al 2018), digital in-line holography (Li et al 2021b) and X-rays (Heindel 2011;Drake & Heindel 2012;Aliseda & Heindel 2021). These techniques, however, require specialised equipment and/or pose limitations in terms of the usable materials.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of particle clustering in moderately dense gas–solid flow

Fong

Coletti

2021

J. Fluid Mech.

View full text Add to dashboard Cite

In collisional gas–solid flows, dense particle clusters are often observed that greatly affect the transport properties of the mixture. The characterisation and prediction of this phenomenon are challenging due to limited optical access, the wide range of scales involved and the interplay of different mechanisms. Here, we consider a laboratory setup in which particles fall against upward-moving air in a square vertical duct: a classic configuration in riser reactors. The use of non-cohesive, monodispersed, spherical particles and the ability to independently vary the solid volume fraction ( $\varPhi _V = 0.1\,\% - 0.8\,\%$ ) and the bulk airflow Reynolds number ( $Re_{bulk} = 300 - 1200$ ) allows us to isolate key elements of the multiphase dynamics, providing the first laboratory observation of cluster-induced turbulence. Above a threshold $\varPhi _V$ , the system exhibits intense fluctuations of concentration and velocity, as measured by high-speed imaging via a backlighting technique which returns optically depth-averaged fields. The space–time autocorrelations reveal dense and persistent mesoscale structures falling faster than the surrounding particles and trailing long wakes. These are shown to be the statistical footprints of visually observed clusters, mostly found in the vicinity of the walls. They are identified via a percolation analysis, tracked in time, and characterised in terms of size, shape, location and velocity. Larger clusters are denser, longer-lived and have greater descent velocity. At the present particle Stokes number, the threshold $\varPhi _V \sim 0.5$ % (largely independent from $Re_{bulk}$ ) is consistent with the view that clusters appear when the typical interval between successive collisions is shorter than the particle response time.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental analysis of particle clustering in moderately dense gas–solid flow

Fong

Coletti

2021

J. Fluid Mech.

View full text Add to dashboard Cite

show abstract

“…They found that the usual opaque disk model is not valid for measuring droplets in liquid phase, while a thin lens model can help to improve measurement accuracy. Li et al [163] developed a Hough-transform-based hologram processing algorithm, which was then applied to quantify the solid circulation rate, size distribution, 3D position and velocity of particles in a fluidized bed. In addition, Wu et al [19] measured the bubble size, 3D velocity, and acceleration in an electrolytic cell with digital holographic PTV.…”

Section: Characterization Of Droplets and Spraymentioning

confidence: 99%

Recent advances and applications of digital holography in multiphase reactive/nonreactive flows: a review

Huang

Cai

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

In various multiphase flows, the characterization of particle dynamics is of great significance to understand the interaction between particles and the surrounding flows. Digital holography (DH) is a versatile 3D imaging technique, which has shown great advantages in quantitative analysis and non-intrusive diagnosis of various particle fields. This review focuses on the advances and applications of DH in multiphase reactive/non-reactive flows in the last two decades. The basic principles of DH are introduced firstly, including its mathematical background and representative experimental configurations. Then, the image processing algorithms for hologram reconstruction and automatic focusing are summarized, along with the methods for separating overlapping particles and tracking moving particles. As a prevailing and powerful tool, the recent applications of deep learning in processing holographic images is also included in this review. Furthermore, the applications of DH in the characterization of particle dynamics in multiphase reactive/non-reactive flows are surveyed in detail. Lastly, the review concludes with the discussion of the technical limits of DH and provides insights into its promising future research directions.

show abstract

“…In 3D PIV, it is crucial to recover the 3D information of particles from images captured at different perspectives by multiple cameras. In holographic PIV techniques, particle point information is often reconstructed based on the characteristics of optical holographic imaging [5][6][7] . Tomo PIV techniques typically employ iterative particle reconstruction methods to obtain the 3D spatial positions and grayscale information of particles [8,9] .…”

Section: Introductionmentioning

confidence: 99%

3D tracer particle field reconstruction based on 3D CNN in SAPIV

Qu,

Zhang,

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

View full text Add to dashboard Cite

The three-dimensional particle image velocimetry (3D PIV) technique, as a non-intrusive method for three-dimensional full-field velocity measurement, has garnered extensive utilization across diverse domains including biomimetic dynamics, combustion diagnostics, and the structural design of aerospace equipment. Synthetic Aperture Particle Image Velocimetry (SAPIV), based on camera arrays, digitally merges images obtained from different perspectives to simulate the imaging effects of large-aperture cameras. This approach allows for large-scale, high-resolution flow field measurements. In this study, the three-dimensional intensity characteristics of particles within sequences of refocused images are investigated. Leveraging the spatial distribution patterns of grayscale information for individual focused particles, we designed a three-dimensional convolutional neural network (3DCNN) capable of extracting focused particle positions. Throughout the particle extraction procedure, this three-dimensional CNN network systematically analyzes the sequence of refocused images and subsequently derives both particle positions and grayscale information for focused particles based on their distinctive characteristics. The tracer particle field in simulated experiments were reconstructed and the reconstruction quality was evaluated. The results demonstrate the high precision of our proposed method in reconstructing three-dimensional tracer particle information in SAPIV.

show abstract

Measuring particle dynamics in a fluidized bed using digital in-line holography

Cited by 13 publications

References 47 publications

Experimental analysis of particle clustering in moderately dense gas–solid flow

Experimental analysis of particle clustering in moderately dense gas–solid flow

Recent advances and applications of digital holography in multiphase reactive/nonreactive flows: a review

3D tracer particle field reconstruction based on 3D CNN in SAPIV

Contact Info

Product

Resources

About