Simulation of moving Particles in 3D with the Lattice Boltzmann Method

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Chair for System Simulation University Erlangen/Nuremberg Chair

1 Simulation of moving Particles in 3D with the Lattice Boltzmann Method, Nils Thürey, Christian Feichtinger, Hans-Joachim Schmid Chair for System Simulation University Erlangen/Nuremberg Chair for Particle Technology University Erlangen/Nuremberg 1 ICMMES 2006

2 Outline The LBM particle simulation The moving particle simulation The rigid body simulation The LBM simulation Memory reduction techniques Grid refinement Grid compression Conclusion 2 ICMMES 2006

3 The moving Particle Simulation The Rigid Body Physics Simulation The Lattice Boltzmann Simulation The moving particle simulation Rigid body physics simulation CFD simulation: Lattice Boltzmann Method Lattice Boltzmann simulation with moving particles 3 ICMMES 2006

4 The moving Particle Simulation The Rigid Body Physics Simulation The Lattice Boltzmann Simulation The rigid body physics simulation Velocity-Störmer-Verlet time discretization Arbitrarily complex particle agglomerates (Frictionless) collision handling Calculation of contact forces and torques Different simulation worlds High performance C++ implementation Extendable framework 4 ICMMES 2006

The moving Particle Simulation The Rigid Body Physics Simulation The Lattice Boltzmann Simulation The Lattice Boltzmann simulation D3Q19i model Focus on laminar flows Treatment of the curved particle

5 The moving Particle Simulation The Rigid Body Physics Simulation The Lattice Boltzmann Simulation The Lattice Boltzmann simulation D3Q19i model Focus on laminar flows Treatment of the curved particle surfaces Second order boundary treatment (Yu et al) Momentum exchange method (Ladd) High performance C++ implementation References: D. Yu, R.Mei, W. Shyy, A unified boundary treatment in lattice Boltzmann, 2003 A. Ladd, Numerical simulation of particular suspensions via a discretized Boltzmann equation, ICMMES 2006

6 Memory requirements Example: Drag force calculations on sphere in channel flow Combination of two conflicting aspects: good approximation of the particles large domains high aspect ratios high memory requirements 6 ICMMES 2006

7 Memory requirements Example: Drag force calculations on sphere in channel flow Combination of two conflicting aspects: good approximation of the particles large domains high aspect ratios high memory requirements 7 ICMMES 2006

8 Memory requirements Example: Drag force calculations on sphere in channel flow Aspect ratio between sphere and domain: 1 : 100 Combination of two conflicting aspects: good approximation of the particles large domains high aspect ratios high memory requirements 8 ICMMES 2006

Grid refinement Low memory requirement Adaptive refinement around the particles Hierarchical approach Arbitrary refinement ratios High Performance Minimum block size of 20³ cells

9 Grid refinement Low memory requirement Adaptive refinement around the particles Hierarchical approach Arbitrary refinement ratios High Performance Minimum block size of 20³ cells Fusion of stream and collide step (few conditionals) Negligible interpolation overhead Parallelization References: O. Filippova, D. Hänel, for Lattice-BGK Models, ICMMES 2006

10 Grid refinement 10 ICMMES 2006

11 Grid refinement 11 ICMMES 2006

12 Grid refinement 12 ICMMES 2006

13 Grid refinement Low memory requirement Adaptive refinement around the particles Hierarchical approach Arbitrary refinement ratios High Performance Minimum block size of 20³ cells Fusion of stream and collide step (few conditionals) Negligible interpolation overhead Parallelization References: O. Filippova, D. Hänel, for Lattice-BGK Models, ICMMES 2006

14 Grid refinement in combination with particles Each subgrid contains its own rigid body simulation world Decoupling of the different time step sizes 14 ICMMES 2006

15 Grid compression Memory reduction of nearly 50% (compared to two grids) Performance improvement (5-10%) Elegant C/C++ implementation possible Basic idea: data shifting within one grid 15 ICMMES 2006

16 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 16 ICMMES 2006

17 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 17 ICMMES 2006

18 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 18 ICMMES 2006

19 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 19 ICMMES 2006

20 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 20 ICMMES 2006

21 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 21 ICMMES 2006

22 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 22 ICMMES 2006

23 Memory reduction: grid compression Key: Unused Initial values (t=0) 1.Update (t=1) 2.Update (t=2) 23 ICMMES 2006

24 Conclusion Coupling between the LBM and a physics engine Simulation of arbitrarily complex agglomerates Calculation of the contact forces and torques Need for memory reduction techniques Current extension: Electrostatic forces Related work from Erlangen: N. Thürey, Adaptive Simulation of Open Water Free Surface Flows G. Wellein, Introducting a Cache-Oblivious Blocking Approach for the Lattice Boltzmann Method T. Zeiser, Microscale Flow Analysis in Trabecular Vertebral Bone 24 ICMMES 2006

25 Appendix 25 ICMMES 2006

26 The rigid body physics simulation 26 ICMMES 2006

27 Coupling between the two simulations for all time steps { //Mapping the particles into the LBM grid // and calculation of the hydrodynamic forces FlagForceStep //LBM stream- and collide step StreamCollideStep } //Moving the rigid bodies and treating //possible collisions RigidBodyStep 27 ICMMES 2006

Memory reduction: grid compression Memory reduction of nearly 50% (compared to two grids) Performance improvement Elegant C++ implementation possible Standard C++ approach: //Allocation of two full

28 Memory reduction: grid compression Memory reduction of nearly 50% (compared to two grids) Performance improvement Elegant C++ implementation possible Standard C++ approach: //Allocation of two full grids double* src = new double[cellsize*xsize*ysize*zsize]; double* dst = new double[cellsize*xsize*ysize*zsize]; //Data access to distribution function df of cell (i,j,k) src[((i*ysize+j)*xsize+k)*cellsize+df] =...; dst[((i*ysize+j)*xsize+k)*cellsize+df] =...; Basic idea: data shifting within one grid 28 ICMMES 2006

29 Memory reduction: grid compression C++ implementation: xsize += 1; //Increasing the original ysize += 1; // x-, y- and z-size by 1. zsize += 1; // //Allocation of only one (slighly larger) grid double* src = new double[cellsize*xsize*ysize*zsize]; double* dst = &src[((1*ysize+1)*xsize+1)*cellsize]; //Same data access as before src[((i*ysize+j)*xsize+k)*cellsize+df] =...; dst[((i*ysize+j)*xsize+k)*cellsize+df] =...; 29 ICMMES 2006

Simulation of Liquid-Gas-Solid Flows with the Lattice Boltzmann Method

Simulation of Liquid-Gas-Solid Flows with the Lattice Boltzmann Method June 21, 2011 Introduction Free Surface LBM Liquid-Gas-Solid Flows Parallel Computing Examples and More References Fig. Simulation