Investigation of three dimensional interstitial velocity, solids motion, and orientation in solid–liquid flow using particle tracking velocimetry

Zitoun, Khaled Bechir; Sastry, Sudhir K.; Guezennec, Yann

doi:10.1016/s0301-9322(01)00011-8

Cited by 33 publications

(14 citation statements)

References 14 publications

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“…Previous work relating to convective heat transfer coefficient measurement have suggested higher values than expected by a static fluid assumption (Balasubramaniam & Sastry, 1994a, 1994b, and studies on fluid interstitial velocity using Particle Tracking Velocimetry (Zitoun, Sastry, & Guezennec, 2001) have revealed the presence of significant interstitial motion even at Reynolds numbers as low as 9.4 and solids loading as high as 50%. Regardless of what situations actually occur, it is prudent to consider all scenarios in design of safe processes.…”

Section: Particle-more-conductive Casesmentioning

confidence: 95%

Experimental investigation of ohmic heating of solid–liquid mixtures under worst-case heating scenarios

Salengke

Sastry

2007

Journal of Food Engineering

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Section: Particle-more-conductive Casesmentioning

confidence: 95%

Experimental investigation of ohmic heating of solid–liquid mixtures under worst-case heating scenarios

Salengke

Sastry

2007

Journal of Food Engineering

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“…Since PTV requires independent particles to be distinguishable, it is typically used in dilute flows, for instance, in articles Zitoun et al (2001) and Peurrung et al (1995). In fluidized beds, the local volume fraction is rapidly changing, and therefore, the flow may become momentarily too dense for regular PTV algorithms.…”

Section: Introductionmentioning

confidence: 99%

Interference-based overlapping particle tracking velocimetry for fluidized beds

2014

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Fluidized beds are used in a wide number of applications, including power plant boilers and chemical facilities. This study proposes a novel particle tracking velocimetry algorithm for semi-dilute suspensions present in fluidized beds. The proposed algorithm is based on thresholding and profile matching algorithms. Image intensity thresholding is used to find regions which need additional image processing. These regions are then processed using interference-based profile matching algorithm to refine the solution quality. The key idea is to limit heavy profile matching computations only to identified clusters to save as much computation time as possible. The method was tested with simulated data and experimental data. The simulations showed that the proposed method was better than the previous boundary arc detection-based method in noisy conditions and cluster sizes ranging from 2 to 6 particles. The difference between the previous approach and the proposed method was small in cluster sizes larger than six particles, even though the proposed method was still slightly better. In low noise conditions with only two particles, the boundary arc detection could outperform the proposed method, but the difference was small. The method was also tested with experimental data from a small cold model fluidized bed. The velocity distributions obtained from the bed are shown for qualitative evaluation. The velocity distributions were realistic, which suggests the usability of the method. The proposed method was also compared to particle image velocimetry to see if both methods produce similar results. The results were divided into histogram intervals according to image intensity that was proportional to local solids volume fraction. The comparison showed that both methods produced similar results, in particular in the low-intensity range, which supports the ability of the method to produce realistic results also in the semi-dilute range where particles form small clusters.

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“…For the penetrated particles, deposition probability should be determined in order to evaluate Journal of Petroleum Engineering the plugging phenomena in the porous media caused by the precipitated asphaltene particles. In such systems, solidsto-tube diameter ratios are typically in the range of 0.1-0.3 [28,32]. In this range, according to the filtration theories, straining filtration is applied.…”

Section: Bundle Of Tubes Modelingmentioning

confidence: 99%

“…where is the exponential function, 0 is a constant dependent on ionic conditions [24], V is the flow mean velocity inside a tube known as interstitial velocity which is the superficial velocity divided by porosity [32], and V * is the minimum fluidization velocity that can be calculated by knowing the particle and fluid density [34]. It is obvious from (9) that higher flow velocities reduce the value of .…”

Section: Bundle Of Tubes Modelingmentioning

confidence: 99%

Asphaltene Formation Damage Stimulation by Ultrasound: An Analytical Approach Using Bundle of Tubes Modeling

Rabbani

Ghazanfari

Amani

2015

Journal of Petroleum Engineering

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This study presents a novel approach for bundle of tubes modeling of permeability impairment due to asphaltene-induced formation damage attenuated by ultrasound which has been rarely attended in the available literature. Model uses the changes of asphaltene particle size distribution (APSD) as a function of time due to ultrasound radiation, while considering surface deposition and pore throat plugging mechanisms. The proposed model predicts the experimental data of permeability reduction during coinjection of solvent and asphaltenic oil into core with reasonable agreement. Viscosity variation due to sonication of crude oil is used to determine the fluid mobility applied in the model. The results of modeling indicate that the fluid samples exposed to ultrasound may cause much less asphaltene-induced damage inside the porous medium. Sensitivity analysis of the model parameters showed that there is an optimum time period during which the best stimulation efficiency is observed. The results of this work can be helpful to better understand the role of ultrasound prohibition in dynamic behavior of asphaltene deposition in porous media. Furthermore, the present model could be potentially utilized for modeling of other time-dependent particle induced damages.

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Investigation of three dimensional interstitial velocity, solids motion, and orientation in solid–liquid flow using particle tracking velocimetry

Cited by 33 publications

References 14 publications

Experimental investigation of ohmic heating of solid–liquid mixtures under worst-case heating scenarios

Experimental investigation of ohmic heating of solid–liquid mixtures under worst-case heating scenarios

Interference-based overlapping particle tracking velocimetry for fluidized beds

Asphaltene Formation Damage Stimulation by Ultrasound: An Analytical Approach Using Bundle of Tubes Modeling

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