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Multiscale Simulations and Modeling of Particulate Flows in Oxycoal Reactors

Multiscale Simulations and Modeling of Particulate Flows in Oxycoal Reactors. Sourabh Apte Department of Mechanical Engineering Funding: DoE National Energy Technology Laboratory A Cihonski, M. Martin, E. Shams, J. Finn. National Energy Technology Lab. .

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Multiscale Simulations and Modeling of Particulate Flows in Oxycoal Reactors

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  1. Multiscale Simulations and Modeling of Particulate Flows in Oxycoal Reactors Sourabh Apte Department of Mechanical Engineering Funding: DoE National Energy Technology Laboratory A Cihonski, M. Martin, E. Shams, J. Finn

  2. National Energy Technology Lab. US Bureau of Mines---> Albany Metallurgy Research Center ---> Albany Research Center---> Now, NETL-Albany.

  3. Pulverized coal combustion in recirculated mixture of flue gas and oxygen (oxygen rich environment) • Nitrogen depleted environment eliminates NOx • Completion of combustion leading to products rich in water vapor and CO2 • Reduced CO and flue gases means efficient control of emissions Oxy-Coal Reactors • Need for carbon capture and sequestration • O2 enriched environments lead to increased reactor temperatures and thermal effects • Cost of production of pure O2 could be high

  4. Combustion/Gasification Hybrid http://fossil.energy.gov/programs/powersystems/combustion/combustion_hybridschematic.html • Flue gases from coal gasifier linked with a combustor • Char from gasification burned in a Fluidized Bed for steam

  5. Multiphase, multiple species, multicomponent heat transfer and turbulent flow problem • Multiple spatio-temporal scales • Particle-turbulence interactions • Coal volatization • Turbulent combustion • Modeling of ash, soot particles • Complex geometry • Radiative heat transfer through participating media • Burnout => Metals Modeling Needs

  6. Dilute and dense clusters of coal particles • Arbitrary shapes • Particle dispersion and interactions with turbulence • Particle-particle interactions, preferential concentrations and structure formation • Spatio-temporal variations in solid volume fractions • Detailed experimental data for validation Modeling Needs: Particulate Flows Grace et al.

  7. Grid Based Classification • Fully Resolved: particles larger than the grid • Sub-grid: particles smaller than the grid resolution • Partially resolved: particles resolved in one or more directions and under-resolved in others • Temporally evolving regions Modeling Challenges: Particulate Flows Physics-Based Classification • Particle size smaller than smallest resolved scale (Kolmogorov scale for DNS or filter size for LES) • Particle size comparable to energetic eddies

  8. Under-resolved discrete particle Resolved Bubbles Resolved Particles Molecular Dynamics Two-Fluid Simulation Techniques: Particulate Flows Van der Hoeff et al. Annual Review of Fluid Mechanics, 2008

  9. Fully resolved Subgrid Fully Resolved Direct Numerical Simulation • Develop an efficient approach for fully resolved simulation (FRS) of particle-laden turbulent flows (heavier-than fluid particles) • Apply FRS to study interactions of sedimenting particles with turbulent flow and quantify drag and lift correlations in “inhomogeneous” clusters Large-eddy Simulation (LES) with under-resolved particle dynamics • Develop an efficient approach for LES of turbulent flows with dense particle-laden flows with Discrete Element Modeling (DEM) • Apply LES-DEM to investigate particle-turbulent interactions in realistic oxycoal reactors. • Further advance LES-DEM for turbulent reacting flows Particulate Flow Modeling

  10. Background Fully resolved Resolved Simulations of Particle-Laden Flows • Arbitrary Lagrangian Eulerian Schemes (ALE) (Hirt, Hu et al.) • Fictitious Domain Method (Glowinski, Hu, Patankar, Minev) • Overset Grids (Burton) • Lattice-Boltzmann (Ladd, ten Cate etal.) • Immersed Boundary Methods (Peskin,Ulhmann, Mittal) • Immersed Boundary with Spectral Model (PHYSALIS: Prosperetti) • Immersed Boundary + Lattice Boltzmann (Proteus: Michaelides) • …. None show simulations with large density ratios (particle-air~ 2000)

  11. Fictitious-Domain Based Approach • Fixed background grid (structured or unstructured) • Particle sizes are assumed larger than grid resolutions • Assume the entire domain (even the particle regions) filled with a fluid • Solve Navier-Stokes over the entire domain (finite volume) • Impose additional constraints obtained from restricting the particle domain to undergo rigid body motion (translation and rotation)

  12. Algorithm • Define material points/volumes within the particle domain • Use color functions to identify particle domain (volume fraction) • Use conservative kernels (second order) for interpolation of all quantities between material volumes and grid CVs (Roma et al.) • Compute density using the color function

  13. Fractional Time-Stepping for Rigidity Constraint Momentum equation over entire domain Solve variable coefficient Poisson equation to enforce divergence-free constraint Reconstruct pressure gradient and update velocity fields

  14. Fractional Time-Stepping for Rigidity Constraint Rigid body motion and rigidity constraint Compute rigidity constraint force Requires interpolations from grid to particles Enforce rigidity constraint Patankar (2001) Apte et al. (JCP, 2008 under review) Advance particle positions and repeat

  15. Verification Studies for Fully Resolved Simulation (FRS)

  16. Taylor Problem Error in pressure Error in velocity • Stationary, decaying vortices • A rotating rigid body (cube) • Initial condition (velocity & pressure) and velocity at material points specified

  17. Flow Over a Fixed Sphere

  18. Flow Over a Fixed Sphere

  19. Flow Over an Oscillating Sphere

  20. Freely Falling Sphere Velocity Magnitude t=0.15 s t=0.6 s t=0.96 s Experiments by Ten Cate et al. (PoF 2005) Grid: 100x100x160 Time Step:0.75 ms

  21. Freely Falling Sphere Experiments by Ten Cate et al. (PoF 2005)

  22. Wake Interactions (Drafting-Kissing-Tumbling) Same density particles

  23. Wake Interactions Heavy particle Density ratio ~1.5 Rep~100

  24. Can We Simulate Large Number of Particles? • Overhead ~ 20% • Simulations of 10,000 particles may require around 10 million grid points

  25. Subgrid Particles

  26. Subgrid Particles (LES-DEM) Mixture theory based formulation [Joseph and Lundgren, 1990] Continuum phase: Eulerian; Dispersed Phase: Lagrangian Continuity Locally non-zero divergence field Momentum Interphase interaction force

  27. Subgrid Particles (LES-DEM) Time scales Mixture theory based formulation [Joseph and Lundgren, 1990] Continuum phase: Eulerian; Dispersed Phase: Lagrangian Based on a drag model Flow around particle not resolved

  28. Searching and Locating Particles n • Criterion for Locating • Compare face-normal vectors • Brute Force • Compute Minimum Distance of Droplet from CV Centroids • Search CV and Neighbors to Locate Droplet • Known Vicinity Algorithm: Neighbor to Neighbor Search • Lohner, R. (JCP, Vol. 118, 1995) • Requires Good Guess of Initial Location of Droplet • Search in the Direction of Particle Motion • Most Efficient if Particle Located in < 10-15 attempts • Scalar in Nature Initial Final Search Path Droplet CV Centroid

  29. Performance of Search Algorithm

  30. Particle-laden Swirling Flow • Experiments by Sommerfeld et al. (1991) Dilute Loading (particle-particle interactions negligible)

  31. Particle-laden Swirling Flow • 1.6 million total hexahedral cells; nearly 1.2 million cells in region of interest Convective Boundary condition Convective Boundary Condition

  32. Particle-laden Swirling Flow Coaxial combustor: Re=26,200 Apte et al, IJMF 2003

  33. Particle-laden Swirling Flow • Gas Phase Statistics Apte et al, IJMF 2003 Mean Axial Velocity RMS of Axial Velocity RMS of Radial Velocity Mean Radial Velocity Mean Swirl Velocity RMS of Swirl Velocity

  34. Particle-laden Swirling Flow • Particle Statistics Apte et al, IJMF 2003 Mean Axial Velocity RMS of Axial Velocity RMS of Radial Velocity Mean Radial Velocity RMS of Swirl Velocity Mean Swirl Velocity RMS of Particle Diameter Mean Particle Diameter

  35. Densely Loaded Regions Ongoing Developments • Issues: • Need to model inter-particle interactions • Models for collision • Load imbalance (only few processors have particles) leading to loss of computing efficiency • Sparse block grid • Partition particles on a simple Cartesian mesh (boxes) • Redistribute boxes among processors to “balance load” • Solve particle equations and advance particle locations (searching and locating simple as Cartesian boxes) • Transfer particles to appropriate processors partitioned based on the unstructured grid (Octree searches) • Compute particle-fluid interactions forces • Solve fluid equations.

  36. Gravitational Settling Particle Evolution Apte et al, IJMF 2008

  37. Rayleigh-Taylor Instability (preliminary study) Particle Evolution Particle void fraction

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