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Nonsmooth DEM Simulation Fast and stable simulation of granular matter and machines

Nonsmooth DEM Simulation Fast and stable simulation of granular matter and machines. Dr C. Lacoursière 1 , Dr Martin Servin 1 , A. Backman 2 1 Umeå University, 2 Algoryx Simulation 2010 - 08-24. Fast and stable simulation of granular matter and machines. Targeting

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Nonsmooth DEM Simulation Fast and stable simulation of granular matter and machines

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  1. Nonsmooth DEM Simulation Fast and stable simulation of granular matter and machines Dr C. Lacoursière1, Dr Martin Servin1, A. Backman2 1 Umeå University, 2 Algoryx Simulation 2010-08-24

  2. Fast and stable simulation of granular matter and machines • Targeting • Simulation of complex mechanical systems • - short computing time e.g. realtime • Main results • Unified method – constraint based multibodies • Direct-iterative hybrid solver fornonsmooth DEM • Simplification & parallelization •  fast and stable simulation

  3. Fast and stable simulation of granular matter and machines • Realtime simulators for training, marketing and R&D, operator-in-the-loop • 100-1000 constrained multibodies running in 60 Hz • time-budget per update < 0.02 s

  4. Two alternative strategies • Smooth DEM (Cundall) • Δt < τelast • explicit force models (Hertz) • small time-step, explicit integration • Nonsmooth DEM (Moreau/Jean/Renouf/Anitescu) • Δt > τelast • implicit contact laws (unilateral constraints & impulses) • large time-step, implicit integration Radjai

  5. Summary of the method • Constraint based modeling • Constraint regularization • Variational time-integration of DAE system • Linearization into MLCP • Block-sparse direct-iterative hybrid solver

  6. Summary of the method • Constraint based modeling • system of particles and rigid bodies • constraint types: nonpenetration, dry friction, joints, motors, incompressibility • Lagrange multiplier - auxiliary variable – inequality, complementarity conditions • Constraint regularization • Variational time-integration of DAE system • Linearization into MLCP • Block-sparse direct-iterative hybrid solver

  7. Summary of the method • Constraint based modeling • Constraint regularization • stabilization • model elasticity and viscosity • Variational time-integration of DAE system • Linearization into MLCP • Block-sparse direct-iterative hybrid solver

  8. Summary of the method • Constraint based modeling • Constraint regularization • Variational time-integration of DAE system (Marsden) • SPOOK integrator (Lacoursière) • similar to Rattle and Shake … plus inequality and complementarity conditions • Linearization into MLCP • Block-sparse direct-iterative hybrid solver

  9. Summary of the method • Linearization into MLCP • Mixed linear complementarity problem • Fluid: 150K x 150K • Rocks:2K x 2K • Loader: 150 x 150

  10. Summary of the method • Constraint based modeling • Constraint regularization • Variational time-integration of DAE system • Linearization into MLCP • Block-sparse direct-iterative hybrid solver • SuperLU • linear scaling • Fluid: 150K x 150K • Rocks:2K x 2K • Loader: 150 x 150

  11. Accelerating the computations • Splitting the system – direct-iterative hybrid solver • Parallelization • Merge/split simplification • Contact reduction

  12. Accelerating the computations • Splitting the system – direct-iterative hybrid solver • joints and non-penetration (direct solver) • joint limits and friction (iterative solver) • Parallelization • Merge/split simplification • Contact reduction

  13. Accelerating the computations • Splitting the system – direct-iterative hybrid solver • Parallelization • constraint based SPH fluid on GPGPU using precondition conjugate gradient • inequality constraints (dry friction) difficult • Merge/split simplification • Contact reduction

  14. Accelerating the computations • Splitting the system – direct-iterative hybrid solver • Parallelization • Merge/split simplification • Contact reduction

  15. Accelerating the computations • Splitting the system – direct-iterative hybrid solver • Parallelization • Merge/split simplification • Contact reduction • eliminate redundant contacts

  16. Results and examples • Wheel loader system • Constraint fluid (SPH) on GPGPU • Rotating drum with rigid and fluid elements • Pellet balling plant

  17. Results and examples • 12 rigid bodies, 15 joints • 500 rigid rocks • 5000 contacts • 0.02 s time-step • 1 s simulation = • 0.1—3.5 s computation • 75% solver • 20% collision detection • 5% merge/split • 2–10 speed-up • Laptop: 64 bit, 3.03 GHz. 4GB RAM • Wheel loader system

  18. Results and examples • Wheel loader system • Shmulevich (2007) • Validation system – measuring blade force and particle displacement

  19. Results and examples • Wheel loader system • Constraint fluid (SPH) on GPGPU • Rotating drum with rigid and fluid elements • Pellet balling plant • SPH incompressibility • constraint • 60 l constraint fluid • 0.01 s time-step • 60 l standard SPH • 0.01 s time-step

  20. Results and examples • 0.9 M particles • 0.001s time-step • density of water • 4 wide • conjugate gradient • 1 s simulation = • 10 s computation • Speed-up • 10x - incomprconstr • 100x - GPGPU • Constraint fluid (SPH) on GPGPU • NVIDIA GTX 280 graphics card • Hosted by Intel Core i7 Processor

  21. Results and examples • Wheel loader system • Constraint fluid (SPH) on GPGPU • Rotating drum with rigid and fluid elements • Pellet balling plant Extension of cosstraint SPH toviscoplastic fluids for complex materials, e.g., size mixtures, wet material • Bui, Int. J. Numer. Anal. Meth. Geomech. 2008; 32:1537–1570 • Jop, Nature, Vol 441|8 June 2006

  22. Results and examples • Wheel loader system • Constraint fluid (SPH) on GPGPU • Rotating drum with rigid and fluid elements • Pellet balling plant • rigid drum driven with • motor constraint • 222 rigid bodies • 1235 SPH fluid particles • 860 contacts • 0.02 s time-step • 1 s simulation = • 10 s computation • non-penetration constraint • produce buoyancy

  23. Results and examples • Wheel loader system • Rotating drum with rigid and fluid elements • Constraint fluid (SPH) on GPGPU • Pellet balling plant

  24. Balling plant In: - fines (ore + binding) - undersized pellets 100 ton/h • Mono-sizedspherical pellets • Understand flow patterns and forces • Design parameters • Control parameters • Agglomeration process • 100K – 100M particles • Time scales 1ms – 5min • 1/10 fraction real vs simulated time Out: - green pellets Return: - undersized pellets - oversized pellets (crush)

  25. Balling plant Next step • Study of outlet design … – 2011-03 • PhD student project 2010 – 2014

  26. Validation and analysis

  27. Validation and analysis

  28. Validation and analysis

  29. Summary and conclusions • A unified method for combining nonsmooth DEM, SPH fluid and machines based on rigid multibodies • Constraint based approach  large time-steps, fast and stable  suitable for real-time simulators & efficient off-line simulation • A number of opportunities to further accelerate the computations • It is not well established what is lost in the nonsmooth approximation (Δt > τelast) – analysis is on-going • Suggestions for critical validation tests are much appreciated!

  30. Project proposal - demonstrator Demonstrator for simulation based design optimization and control of systems with granular material – nonsmooth DEM Suitable system for demonstrator: • Crushing, loading, sorting, conveying • Rigid body model for large elements (0.01 – 1 m) • Fluid model for finer material (100 um – 10mm) • Rock, mineral ore, biomaterials, dry/wet Gathering interested participants: • Plant owners – LKAB, Boliden, energy companies • System manufacturer –conveors, chrusher, control systems • Consulting – control and optimization • Simulation software companies – Algoryx, DEM Solution?

  31. Videos Realtimeconeyor with loader and rocks http://www.algoryx.se/~servin/vids/conveyor_06.avi Realtime simple balling drum http://www.algoryx.se/~servin/vids/rulltrumma_2.avi Robot conveyor demo http://www.algoryx.se/~servin/vids/AGX_robotics1.avi Constraint fluid http://www.algoryx.se/~servin/vids/constraintFluidsXVID.avi GPGPU fluid 1M particles at 10 Hz http://www.algoryx.se/~servin/vids/gpufluid1.avi GPGPU fluid + boxes http://www.algoryx.se/~servin/vids/constraint%20fluid%20with%20boxes.avi Viskoelastic fluid http://www.algoryx.se/~servin/vids/visko-elasto-plastic.avi Merge+split for realtime loader demo http://www.algoryx.se/~servin/vids/loader.wmv

  32. Links Research group Modeling and simulation of complex mechanical systems http://www.physics.umu.se/english/research/statistical-physics-and-networks/complex-mechanical-systems/ Algoryx Simulation - Spin-off company for prof simulation software development, 14 employees https://www.algoryx.se/, training simulaors: http://www.oryx.se/ UMIT Research Lab at Umeå University – Multidisciplinary research environment with 25+ researchers on computational science and engineering http://www.org.umu.se/umit/umit/?languageId=1 ProcessITInnpovaions – Innovation system connecting processing industry in northern Sweden with research and ITC and software companies http://www.processitinnovations.se/default.aspx?id=1765

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