Research Papers: Multiphase Flows

Experimental Investigation of Sand Jets Passing Through Immiscible Fluids

[+] Author and Article Information
Niyousha Mohammadidinani

Department of Civil Engineering,
Lakehead University,
Thunder Bay P7B 5E1, ON, Canada
e-mail: nmohamm2@lakeheadu.ca

Amir H. Azimi

Department of Civil Engineering,
Lakehead University,
Thunder Bay P7B 5E1, ON, Canada
e-mail: azimi@lakeheadu.ca

Siamak Elyasi

Department of Chemical Engineering,
Lakehead University,
Thunder Bay P7B 5E1, ON, Canada
e-mail: selyasi@lakeheadu.ca

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received July 13, 2016; final manuscript received December 26, 2016; published online March 20, 2017. Assoc. Editor: Francine Battaglia.

J. Fluids Eng 139(5), 051303 (Mar 20, 2017) (13 pages) Paper No: FE-16-1450; doi: 10.1115/1.4035762 History: Received July 13, 2016; Revised December 26, 2016

Laboratory experiments were conducted to study the dynamics of sand jets passing through two immiscible fluids. Different oil layer thicknesses, nozzle diameters, and sand masses were employed. Evolution of oily sand jets with time was investigated using image processing and boundary visualization techniques. Different shapes of the frontal head and trailing wave section were observed and cloud formation was classified into different categories based on Reynolds number, normalized oil layer thickness, and evolution time. It was found that the effect of Reynolds number on evolution of oily sand jets was more significant than the other parameters. Width and frontal velocity of oily sand jets were measured at different times. It was observed that oily sand jets became unstable after a distance of ten times larger than the nozzle diameter. Instability of oily sand jets caused intense spreading with a spreading rate of 0.4. The thin layer of oil encapsulated sand cluster was ruptured due to excess shear stress and caused bursting of the frontal head into a cloud of sand particles. Three different bursting mechanisms were observed and a correlation was found between the densimetric Froude number and the normalized bursting time. Data mining and boundary visualization techniques were used to model oily sand jets. Model trees were developed to classify and predict the growth of oily sand jets at different conditions. Modeling results indicated that the Model tree can predict the growth of sand jets with an uncertainty of ±8.2%, ±6.8%, and ±8.7% for width, velocity, and distance, respectively.

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Pignatel, F. , Nicolas, M. , and Guazzelli, E. , 2011, “ A Falling Cloud of Particles at a Small but Finite Reynolds Number,” J. Fluid Mech., 671, pp. 34–51. [CrossRef]
Hall, N. , Elenany, M. , Zhu, D. Z. , and Rajaratnam, N. , 2010, “ Experimental Study of Sand and Slurry Jets in Water,” J. Hydraul. Eng., 136(10), pp. 727–738. [CrossRef]
Noh, Y. , and Fernando, H. J. S. , 1993, “ The Transition in the Sedimentation Pattern of a Particle Cloud,” Phys. Fluids A, 5(12), pp. 3049–3055. [CrossRef]
Noh, Y. , 2000, “ Sedimentation of a Particle Cloud Across a Density Interface,” Fluid Dyn. Res., 27(3), pp. 129–142. [CrossRef]
Rahimipour, H. , and Wilkinson, D. , 1992, “ Dynamic Behavior of Particle Clouds,” Eleventh Australasian Fluid Mechanics Conference, University of Tasmania, Hobart, Australia, pp. 743–746.
Bush, J. W. M. , Thurber, B. A. , and Blanchette, F. , 2003, “ Particle Clouds in Homogeneous and Stratified Environments,” J. Fluid Mech., 489, pp. 29–54. [CrossRef]
Azimi, A. , Zhu, D. Z. , and Rajaratnam, N. , 2012, “ Experimental Study of Sand Jet Front in Water,” Int. J. Multiphase Flow, 40, pp. 19–37. [CrossRef]
Azimi, A. , Zhu, D. Z. , and Rajaratnam, N. , 2012, “ Computational Investigation on Vertical Slurry Jets,” Int. J. Multiphase Flow, 47, pp. 94–114. [CrossRef]
Azimi, A. H. , Zhu, D. Z. , and Rajaratnam, N. , 2014, “ Experimental Study of Subaqueous Sand Deposition From Slurry Wall Jets,” ASCE J. Eng. Mech., 140(2), pp. 296–314. [CrossRef]
Azimi, A. H. , Zhu, D. Z. , and Rajaratnam, N. , 2015, “ An Experimental Study of Circular Sand–Water Wall Jets,” Int. J. Multiphase Flow, 74, pp. 34–44. [CrossRef]
Giraut, F. , Carazzo, G. , Tait, S. , Ferrucci, F. , and Kaminski, E. , 2014, “ The Effect of Total Grain-Size Distribution on the Dynamics of Turbulent Volcanic Plumes,” Earth Planet. Sci. Lett., 394, pp. 124–134. [CrossRef]
Miller, W. G. , Scott, J. D. , and Sego, D. C. , 2009, “ Flume Deposition Modeling of Caustic and Noncaustic Oil Sand Tailings,” Can. Geotech. J., 46(6), pp. 679–693. [CrossRef]
Friedman, P. D. , and Katz, J. , 1999, “ The Flow and Mixing Mechanisms Caused by the Impingement of an Immiscible Interface With a Vertical Jet,” Phys. Fluids, 11(9), pp. 2598–2606. [CrossRef]
Geyer, A. , Phillips, J. C. , Mier-Torrecilla, M. , Idelsohn, S. R. , and Onate, E. , 2011, “ Flow Behaviour of Negatively Buoyant Jets in Immiscible Ambient Fluid,” Exp. Fluids., 52(1), pp. 261–271. [CrossRef]
Brush, L. M. J. , 1962, “ Exploratory Study of Sediment Diffusion,” J. Geophys. Res., 67(4), pp. 1427–1433. [CrossRef]
Mazurek, K. A. , Christison, K. , and Rajaratnam, N. , 2002, “ Turbulent Sand Jet in Water,” J. Hydraulic. Res., 40(4), pp. 527–530. [CrossRef]
Singamsetti, S. R. , 1966, “ Diffusion of Sediment in Submerged Jet,” ASCE, J. Hydraul. Div., 92(2), pp. 153–168.
Nicolas, M. , 2002, “ Experimental Study of Gravity-Driven Dense Suspension Jets,” Phys. Fluids, 14(10), pp. 3570–3576. [CrossRef]
Subramanian, G. , and Koch, D. L. , 2008, “ Evolution of Clusters of Sedimenting Low-Reynolds-Number Particles With Oseen Interactions,” J. Fluid Mech., 603, pp. 63–100. [CrossRef]
Lai, A. C. H. , Wang, R. , Law, A. W. K. , and Adams, E. E. , 2016, “ Modeling and Experiments of Polydisperse Particle Clouds,” Environ. Fluid Mech., 16(4), pp. 875–898. [CrossRef]
Moghadaripour, M. , 2016, “ Experimental Study of Sand Jets and Particle Clouds in Water,” M.Sc. thesis, Lakehead University, Thunder Bay, ON, Canada, p. 116.
Moghadaripour, M. , Azimi, A. H. , and Elyasi, S. , 2016, “ Experimental Study of Oblique Particle Clouds in Water,” Int. J. Multiphase Flow, pp. 193–213.
Zhao, B. , Law, A. W. K. , Adams, E. E. , and Er, J. W. , 2014, “ Formation of Particle Clouds,” J. Fluid Mech., 746, pp. 193–213. [CrossRef]
Bond, D. , and Johari, H. , 2005, “ Effect of Initial Geometry on the Development of Thermals,” Exp. Fluids, 39(3), pp. 591–601. [CrossRef]
Webster, D. R. , and Longmire, E. K. , 2001, “ Jet Pinch-Off and Drop Formation in Immiscible Liquid–Liquid Systems,” Exp. Fluids, 30(1), pp. 47–56. [CrossRef]
Rao, K. K. , and Nott, P. R. , 2008, An Introduction to Granular Flow, Cambridge University Press, Cambridge, UK, p. 512.
Cai, J. , Hall, N. , Elenany, M. , Zhu, D. Z. , and Rajaratnam, N. , 2010, “ Observations on Sand Jets in Air,” ASCE J. Eng. Mech., 136(9), pp. 1181–1186. [CrossRef]
Zhao, B. , Law, A. W. K. , Adams, E. E. , Shao, D. , and Huang, Z. , 2012, “ Effect of Air Release Height on the Formation of Sediment Thermals in Water,” J. Hydraul. Res., 50(2), pp. 532–540. [CrossRef]
Lai, A. C. H. , Chan, S. N. , Law, A. W. K. , and Adams, E. E. , 2016, “ Spreading Hypothesis of a Particle Plume,” ASCE J. Hydraul. Eng., 142(12), p. 04016065. [CrossRef]
Witten, I. H. , and Frank, E. , 2005, Data Mining-Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, CA, p. 525.
Bhattacharya, B. , Price, R. K. , and Solomatine, D. P. , 2007, “ Machine Learning Approach to Modeling Sediment Transport,” ASCE J. Hydraul. Eng., 133(4), pp. 440–450. [CrossRef]
Jung, N. C. , Popescu, I. , Keldeman, P. , Solomatine, D. P. , and Price, R. K. , 2010, “ Application of Model Trees and Other Machine Learning Techniques for Algal Growth Prediction in Yongdam Reservoir, Republic of Korea,” J. Hydroinf., 12(3), pp. 262–274. [CrossRef]
Quinlan, J. R. , 1992, “ Learning With Continuous Classes,” 5th Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, pp. 343–348.
Etemad-Shahidi, A. , and Taghipour, M. , 2012, “ Predicting Longitudinal Dispersion Coefficient in Natural Streams Using M5 Model Tree,” ASCE J. Hydrau. Eng., 138(6), pp. 542–554. [CrossRef]
Wang, Y. , and Witten, I. H. , 1997, “ Induction of Model Trees for Predicting Continues Classes,” European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague, Czech Republic, pp. 128–137.
Pedrycz, W. , and Sosnowski, Z. A. , 2001, “ The Design of Decision Trees in the Frame Work of Granular Data and Their Application to Software Quality Models,” Fuzzy Sets Syst. 123(3), pp. 271–290. [CrossRef]
Turner, J. S. , 1969, “ Buoyant Plumes and Thermals,” Annu. Rev. Fluid Mech., 1(1), pp. 29–44. [CrossRef]
Lee, J. H. W. , and Chu, V. H. , 2003, Turbulent Jets and Plums, A Lagrangian Approach, Kluwer Academic Publishers Group, Dordrecht, The Netherlands, p. 390.


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Fig. 1

Regime classification of particle cloud based on the cloud Reynolds number Rep and the ratio of cloud diameter to the particle size Do/D50. Regime I is the Stokes cloud, regime II is the macroscale inertia, regime III is the microscale inertia, and regime IV is the turbulent thermal [2023].

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Fig. 2

A schematic view of the experimental setup with a cylindrical coordinate system

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Fig. 3

Effects of Reynolds number R on the evolution of oily sand jets: (a) bar shape [H = 19, R = 12.8, t/T = 1.5], (b) hook shape [H = 41, R = 12.8, t/T = 2.25], (c) separation [H = 41, R = 12.8, t/T = 5.62], (d) torsion [H = 10, R = 36.3, t/T = 0.8], (e) trialing wave [H = 54, R = 36.3, t/T = 0.8], and (f) ball shape [H = 54, R = 102.7, t/T = 0.21]

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Fig. 4

Effect of Reynolds number R on bursting of oily sand jets: (a) rear bursting (test no. 10, H = 10, R = 102.7), (b) multiple bursting (test no. 23, H = 19, R = 66.7), and (c) bifurcation bursting (test no. 5, H = 10, R = 36.3)

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Fig. 5

Correlation of the normalized bursting time t/T and densimetric Froude number F for different nondimensional oil layer thicknesses H

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Fig. 6

Variations of the normalized width of oily sand jets wf /do with the normalized distance x/do for m = 10 g, different oil layer thicknesses and Reynolds numbers R: (a) H = 19, (b) H = 54, and (c) H = 214

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Fig. 7

Effects of oil layer thickness on frontal velocity of oily sand jets with normalized time t/T: (a) R = 12.8, (b) R = 66.7, and (c) R = 102.7

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Fig. 8

Snapshot images show the trailing instability of oily sand jets at different normalized time for test no. 36 (H = 29, R = 36.3): (a) t/T = 0.48, (b) t/T = 0.52, and (c) t/T = 0.56

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Fig. 9

Correlations of the oily sand jets trailing with F for different nondimensional oil layer thickness H: (a) correlation of the normalized wavelength with F and (b) correlation of the normalized amplitude with F

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Fig. 10

Correlations of the critical instability of oily sand jets with F for different nondimensional oil layer thickness H: (a) correlation of the critical time for wavelengths with F and (b) correlation of the critical time for amplitude with F

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Fig. 11

Schematic of force balance at the early stage of evolution of oily sand jets

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Fig. 12

Variations of the average shear stress of the oil–water interface τ along the axis of the jet at the early stage of evolution of oily sand jets for m = 10 g

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Fig. 13

Histograms of the sand jets parameters using by Weka software: (a) histogram of shapes, (b) histogram of distance from nozzle, (c) histogram of the frontal width of sand jets, and (d) histogram of the frontal velocity

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Fig. 14

Boundary maps of various shapes of sand jets passing through an oil layer at different times. Naive Bayes model was used for boundary classification of Weka software: (a) effect of densimetric Froude number F on sand jets classification and (b) effect of the normalized oil layer thickness and Reynolds number H/R on classification of oily sand jets.

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Fig. 15

Structure of model trees constructed by M5P and linear models for hoil = 4, 6, 8, 10, 12, 30 mm, do = 4, 8, 12, 16 mm and m = 10, 25 g: (a) model tree for x, (b) model tree for wf, and (c) model tree for uf




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