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Physics based simulation of material flow in balling plant

Physics based simulation of material flow in balling plant. Dr. Martin Servin Department of Physics / UMIT Research Lab 2009-10-21. Outline. Project overview The balling process – material flow Purpose of physics based simulation of material flow

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Physics based simulation of material flow in balling plant

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  1. Physics based simulation of material flow in balling plant Dr. Martin Servin Department of Physics / UMIT Research Lab 2009-10-21

  2. Outline • Project overview • The balling process – material flow • Purpose of physics based simulation of material flow • The approach: model and computational techniques • Status of the project

  3. Project overview • Current phase: in preparation, on-going pre-study • Project objective - Physics based simulation of material flow in balling plant • New models and computational techniques • Analysis of material flow, forces and dynamics • Tool for understanding and improving the balling process • Project participants • PhD student: recruiting • At Umeå University Kenneth Bodin – PhLic, HPC2N Claude Lacoursiere – PhD, Dep. Computer Science/HPC2N Mats G Larson – prof, Dep. Mathematics & Math Statistics Martin Servin – PhD, Dep. Physics • At LKAB: Kjell-Ove Mickelsson – Research engineer Kent Tano – Manager, Process Technology

  4. The balling process In: - fines (ore + binding) - undersized pellets 100 ton/h Out: - green pellets Return: - undersized pellets - oversized pellets (crush)

  5. Material flow Fines: iron ore, 150 μm, binding, 8-10% water Pellets: 12.5 mm, 3 g

  6. Material flow Fines: iron ore, 150 μm, binding, 8-10% water Pellets: 12.5 mm, 3 g

  7. Material flow Fines: iron ore, 150 μm, binding, 8-10% water Pellets: 12.5 mm, 3 g

  8. Material flow Fines: iron ore, 150 μm, binding, 8-10% water Pellets: 12.5 mm, 3 g

  9. Simulation of material flow • Purpose of the simulation • Understanding of the process- velocity fields, shear flow, contact and pressure forces - influence of control parameters (fine material, drum speed, water) • Improving the design of the balling plant, e.g. outlet- even flow, size distribution, spherical symmetry • Long term goal – agglomeration process and improving the process control • Requirements • 100 K – 10 M pellets • 10 m3 fines • Time-scales: 1 ms (d/v) – 5 min (oscillations in return mass flow) • simulation time/real time = 10/1 • Measurements from simulation: velocity fields, motion patterns, force distributions

  10. The approach • Models • Rigid multibodies for plant mechanics • Rigid bodies for pellets • SPH for fine • SPH for coarse model for pellets • SPH + rigid hybrid for mixture • Solver • Variational integrator (SPOOK) – stability, invariance • Constraint regularization • Direct and iterative linear solver, topological, SuperLU • Accelaration • Large time-steps • Adaptive level of detail, SPH + rigid hybrid • Parallelization • Hardware (GPGPU, multicore, super computer)

  11. Rigid multibodies and DEM • Newton-Euler equations, constraints and impulses -> DAE • Constraint regularization based on viscoelastic models, e.g. Hertz, linear elasticity • Mixed linear complementarity problem (MLCP) of size (6Np + Nc) x (6Np + Nc)

  12. Rigid multibodies and DEM • How to handle the large-scale complex systems? • Direct, iterative topological block-sparse solver • Parallelization – based on the system topology, force-chains • Wake/sleep, agglomerate/split • Hardware: GPGPU/CUDA vs supercomputer (HPC2N)

  13. SPH & constraint fluids • Smoothed Particle Hydrodynamics (SPH) • Constraint fluid = SPH with incompressibility constraint • Constraint regularization (SPOOK) – stiff and stable at large time-steps • Parallelization on GPGPU, precond conjugate-gradient • -> 1.000.000 particles at interactive rate • Research and development • Constraint-based viscoelasticity / viscoplasticity • Hybrid model: SPH + rigid • Adaptive resolution - computational time, quality of solution, stability

  14. 900.000 particles, water, ~3m, 10/1 sim ratio

  15. Status of the project • We are recruiting PhD student – candidates? • I first prototype is under development – 2.000 pellets at interactive rate on conventional PC- ”constraint fluid” to be integrated • We are interested in collaboration with users and developers of simulation tools

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