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School of Process, Environmental and Materials Engineering Faculty of Engineering

School of Process, Environmental and Materials Engineering Faculty of Engineering. LES and Experimental Data for RANS Model Validation in Particle-Laden Flows: Progress and Prospects at the University of Leeds Mike Fairweather. Fuel storage facility, open cooling pond.

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School of Process, Environmental and Materials Engineering Faculty of Engineering

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  1. School of Process, Environmental and Materials Engineering Faculty of Engineering LES and Experimental Data for RANS Model Validation in Particle-Laden Flows: Progress and Prospects at the University of Leeds Mike Fairweather

  2. Fuel storage facility, open cooling pond High active storage tanks (HAST) after reprocessing Motivation • To provide predictive capability for transport and processing of nuclear waste sludges stored in ponds and process equipment • Much of ILW stored in tailings ponds as solid-liquid sludge, HLW as suspension of particles in tanks (although settling) • Current plans involve processing at low solids content (increasing process costs, reducing options, producing unnecessary waste) due to concern about pipe and equipment blockages • Of particular interest is behaviour of wastes in terms of settling/non-settling characteristics of particles, propensity to form solid beds, and re-suspension characteristics of particles from bed

  3. Accelerated Clean-Up • Particulates often fine, and particle-particle interaction forces play significant role in their behaviour (van der Waals attractive force and electrostatic repulsion force – DLVO theory) • Flow properties can be manipulated through adjustment of these forces • Aggregate particles by polymer-induced flocculation or salt induced coagulation – greater attraction between particles results in higher yield stresses and viscosities • Discourage coagulation by adjusting pH to increase influence of electrical double layer – use to reduce propensity to form particle aggregates, reduce settling behaviour, improve re-suspension characteristics, and allow treatment of dense flows • Hence, improved process efficiencies, accelerated processing, smaller waste volumes, lower costs Derjaguin BV, Landau L, Acta Physiochim 1941;14:633 Verwey EJW, Overbeek JTG, Theory of Stability of Lyophobic Colloids, Elsevier, 1948

  4. Simulants of highly active particles showing size and morphology Modelling Requirements • Engineering models (generally RANS) used in industry to predict complex flows encountered in practice • Use of this approach requires modelling of solid-liquid flows with one-way (fluid phase influences particulate phase via drag / turbulence transfer), two-way (one-way plus particulate phase influences mean momentum / turbulence kinetic energy in fluid phase), and four-way (two-way plus particle-particle interaction) coupling • Extension to cover particle agglomeration, non-spherical particles, and distributions of particle size and shape

  5. Work Underway at Leeds • Engineering Properties: • System control through manipulation of inter-particle forces • Measurement of particle interactions between waste simulant particles • Flow rheology, compression and shear yield stress data gathering • Stability/instability tests on slurries • Hydraulic Behaviour: • Experimental studies of practically relevant flow geometries • Investigation of re-suspension, transport, sedimentation and mixing properties of sludges during transportation and in process equipment • Influence of manipulating inter-particle forces, assessment erosion effects • Theoretical Modelling: • Reynolds-averaged Navier-Stokes modelling and large eddy simulation with Lagrangian particle tracking • Plans to develop direct numerical simulation and transported pdf methods

  6. Modelling and Simulation • RANS Modelling: • Solves time-averaged equations, Newtonian fluid, BOFFIN code • Closed using Reynolds stress turbulence model including wall reflection effects • LES: • Solves instantaneous flow equations, Newtonian fluid, BOFFIN code • Sub-grid scale stress using dynamic model (Germano et al. (1991), implemented using approximate localization procedure (Piomelli and Liu, 1995), modifications for low Re and wall-bounded flows (di Mare and Jones, 2003) • Lagrangian Particle Tracking: • Track representative groups of particles solving equation of motion • Contains inertia, drag, pressure gradient, gravity, added mass and history terms • Currently looking at dilute flows, rigid spheres, one- and two-way coupling

  7. Yoshida H, Suenaga K, Echigo R, Int J Heat Mass Transfer 1990;33:859 – Slot jet of air, Re = 10,000 – Spherical glass beads – Mean diameter glass beads 48.9 μm, standard deviation 8.7 μm – Initial mass loading ratio particles = 0.1, i.e. mass flow rate particles / mass flow rate air d 10 mm Confinement Plate 150 mm y Symmetry Axis Fixed Pressure Boundary 80 mm x Impingement Plate RANS and LES of Impinging Flows • Impinging flows generic and encountered in many waste applications • Only two-data sets available, both gas-solid • Move to LES to provide information required for assessment and improvement of RANS approaches

  8. x/d=3 x/d=4 x/d=1 x/d=5 x/d=6 x/d=7 x/d=3 x/d=4 x/d=1 x/d=4 x/d=3 x/d=1 x/d=4 x/d=3 x/d=1 x/d=6 x/d=7 x/d=5 x/d=5 x/d=7 x/d=6 x/d=5 x/d=6 x/d=7 RANS and LES of Impinging Flows — RANS --- LES Single-Phase

  9. Single-Phase — RANS --- LES Two-Phase y/d=4 y/d=2 y/d=1 y/d=4 y/d=2 y/d=1 RANS and LES of Impinging Flows

  10. – Duct flows dimensions hh4πh – Re = 4,410, 36,500 and 250,000 – Particle sizes from 5 to 1000 μm Instantaneous cross-stream velocity contours Instantaneous cross-stream velocity vectors Mean cross-stream velocity vectors Mean cross-stream velocity contours Zoom of lower left quadrant LES of Duct Flows

  11. (a) (b) (c) LES of Duct Flows Predictions and data for streamwise mean velocity along lower wall bisector for (a) 2z/h = 8, 16, 24, 40 and 84 (bottom to top), and (b) x/h = 0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3 and 0.5 (bottom to top); and (c) predicted mean secondary motions on cross-stream plane ○ Gessner et al. (1979); ● Gessner and Emery (1981); — LES

  12. (a) (b) (c) (d) (e) LES of Duct Flows Accumulation of 1000 μm particles at corners of duct floor Instantaneous distribution of particles on (x, y) planes perpendicular to streamwise direction: (a) 5 μm; (b) 10 μm; (c) 50 μm; (d) 100 μm; (e) 500 μm

  13. (a) (b) (c) x+ (e) (f) (d) up+ LES of Duct Flows Instantaneous distribution of particle dimensionless vertical distance (wall units) and dimensionless particle velocity: (a) 100 μm; (b) 500 μm; (c) 1000 μm (t+ = 13,954); and (d) 100 μm; (e) 500 μm; (f) 1000 μm (t+ = 200,000)

  14. (a) (b) LES of Duct Flows Dispersion function in normal-to-wall direction for particles nt = total number particles in domain at time t Xi(t) = particle displacement in normal direction at time t Xm(t) = mean value Particle dispersion with time: (a) mean displacement and (b) dispersion function in transverse direction ( □ 5 μm; ∆ 10 μm; ◊ 50 μm; ○ 100 μm; - □ - 500 μm; - ∆ - 1000 μm).

  15. Experimental Work – Pipe Flows • Measurement of detailed fluid and particle flow characteristics in laminar, transitional and turbulent flows in straight pipes • Focus on settling and re-suspension behaviour, and erosion • System control through modification of inter-particle forces • Measurements using particle imaging velocimetry, laser Doppler anemometry, ultrasound velocity profiling, pressure drop, high speed imaging Flow-loop for study of pipe flows

  16. Experimental Work – Pipe Flows Bed erosion for various flow velocities Minimum transport velocity as a function of shear yield stress Increasing attractive potential Variation of bed height with flow velocity Left to right: 1M KNO3, 10-4M KCl, 10-4M KNO3 Similar suspension volume fraction Effect due to particle interaction characteristics which influence sediment packing network

  17. Experimental Work – Impinging Jets • Variables of interest: • Particle size and size distribution, density, shape, concentration • Re, jet-to-nozzle spacing • One-, two- and four-way coupling, agglomeration • Bed erosion and re-suspension, cavity formation, erosion of tank

  18. Conclusions and Further Work • Requirement for detailed experimental data for validation of RANS models used in industry • Data are available, but there are few (if any) systematic studies of generic flows of interest • Experimental programmes planned and underway, but these will take significant amount of time and funding • In meantime, LES can be used to provide input for RANS validation and improvement • Further work on LES required, including effect particles on SGS model • DNS would be useful in providing input for cases where data are difficult or impossible to gather

  19. Acknowledgements • Colleagues at Leeds and Imperial College: • Jun Yao • Jonathan Adams,David Harbottle,Jon-Paul Hurn, Bo Lin,Donna McKendrick,Derek Njobuenwu, Simon Rhea • Bill Jones • Funding: • EPSRC under grant EP/C549465/1 (TSEC programme KNOO) • BNFL, Nexia Solutions Ltd. and Nuclear Decommissioning Agency

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