Technische Universität München Benefits of Structured Cartesian Grids for the Simulation of Fluid-...
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Technische Universität München
Benefits of Structured Cartesian Grids for the Simulation of Fluid-
Structure Interactions
Miriam Mehl
Department of Computer Science
TU München
Technische Universität München
Outline
• Our Cartesian Grids
• Requirements Fluid-Structure Interactions
• Cartesian Grids – CFD
• Cartesian Grids – Coupling
• Application Examples
• Conclusion
Technische Universität München
Our Cartesian Grids
• Cartesian grid cells
squares/cubes
• recursive refinement
tree structure
Technische Universität München
Our Cartesian Grids
• Cartesian grid cells
squares/cubes
• recursive refinement
tree structure
Technische Universität München
Fluid-Structure Interactions – Requirements
• complex and changing geometries
flow solver
Technische Universität München
Fluid-Structure Interactions – Requirements
• complex and changing geometries
flow solver
• partitioned approaches
coupling of codes
modularity
Structure SolverFlow Solver
Coupling
Technische Universität München
Cartesian Grids – CFD
fast and flexible geometry treatment
Eulerian Approach + Marker-and-Cell
Technische Universität München
Cartesian Grids – CFD
# grid cells runtime (sec)
52,662,337 48.188
210,666,753 168.641
842,687.105 662.797Pentium 4, 2.4 GHz, 512 MB cache
fast and flexible geometry treatment
Technische Universität München
Cartesian Grids – CFD
• recursive cell-tree local grid changes
fast and flexible geometry treatment
Technische Universität München
Cartesian Grids – CFD
cell-oriented operator evaluation
constant difference stencils
no neighbour relations
Technische Universität München
Cartesian Grids – CFD
cell-oriented operator evaluation
constant difference stencils
no neighbour relations
i,ji-1,j ½ -1
½
Technische Universität München
Cartesian Grids – CFD
cell-oriented operator evaluation
constant difference stencils
no neighbour relations
i,ji-1,j
i,ji-1,j
-1 ½½
Technische Universität München
Cartesian Grids – CFD
cell-oriented operator evaluation
constant difference stencils
no neighbour relations
i-1,j
i-1,j
½ -1 ½
Technische Universität München
Cartesian Grids – CFD
cell-oriented operator evaluation
constant difference stencils
no neighbour relations
½½ -1
Technische Universität München
Cartesian Grids – CFD
Peano curve
linearisation of the cell-tree
processing order
Technische Universität München
Cartesian Grids – CFD
Peano curve
linearisation of the cell-tree
processing order
Technische Universität München
Cartesian Grids – CFD
Peano curve + stacks = data access with
locality in space
locality in time
Technische Universität München
Cartesian Grids – CFD
Peano curve + stacks = data access with
locality in space
locality in time
Technische Universität München
Cartesian Grids – CFD
low memory requirements
bytes/cell bytes/vertex
2D6 2 only grid
14 20 flow solver
3D10 2 only grid
18 28 flow solver
hardware + numerical efficiency
Technische Universität München
==19243== D refs: 7,249,842,728 (4,026,485,237 rd + 3,223,357,491 wr)==19243== D1 misses: 1,249,032 ( 621,413 rd + 627,619 wr)==19243== L2d misses: 632,162 ( 301,283 rd + 330,879 wr)==19243== D1 miss rate: 0.0% ( 0.0% + 0.0% )==19243== L2d miss rate: 0.0% ( 0.0% + 0.0% )==19243== ==19243== L2 refs: 19,559,185 ( 18,931,566 rd + 627,619 wr)==19243== L2 misses: 646,343 ( 315,464 rd + 330,879 wr)==19243== L2 miss rate: 0.0% ( 0.0% + 0.0% )
Cartesian Grids – CFD
2D Poisson equation, 1,000,000 degrees of freedom, Pentium 4, 1MB L2 Cache, Cachegrind simulation
hardware + numerical efficiency
high cache-efficiency
Technische Universität München
Cartesian Grids – CFD
multigrid
• dehierarchisation• compute residual• smooth• restrict residual
hardware + numerical efficiency
Technische Universität München
Cartesian Grids – CFD
# dyn. refinem. k=0 k=1 k=2 k=3
# iterations 9 10 9 9
accuracy 5.972e-2 4.613e-3 4.521e-4 6.771e-5
Poisson equation on a cube, F-cycle
hardware + numerical efficiency
multigrid
Technische Universität München
Cartesian Grids – CFD
0
)sin(3 2
u
xu i
tol. 1.17e-3 reg. grid adapt. grid
# dofs 509.656 61.267
hardware + numerical efficiency
dynamical adaptivity
Technische Universität München
Cartesian Grids – CFD
dynamically balanced parallelisation
0
1 2 3 4
5 6 7 8 17 18 19 20
13 14 15 169 10 11 12
Technische Universität München
Cartesian Grids – CFD
connected partitions
quasi-minimal partition surface
dynamically balanced parallelisation
Technische Universität München
Advantages of Cartesian Grids – CFD
dynamically balanced parallelisation
Technische Universität München
Cartesian Grids – CFD
1 10 20 30 40 50 60 70 80 90 100 Row
6705
101520253035404550556065707580
Speedup
dynamically balanced parallelisation
Technische Universität München
Cartesian Grids – Coupling
efficient data mapping for non-matching grids
fluid solver+ interpolation
struct. solver+ interpolation
FSI*cesurfacecoupling
Grid administration
Data mapping
Technische Universität München
Cartesian Grids – Coupling
grid resolution # boundary nodes runtime [s]
64 18,482 0.200
128 75,514 0.671
256 305,394 2.594
512 1,227,987 10.114
sphere (8,000 triangles), Pentium M 1.6 GHz, 2048 kB cache
efficient data mapping for non-matching grids
Technische Universität München
Cartesian Grids – Coupling
triangles runtime [s]
16,000 12.7
32,000 14.3
64,000 16.2
128,000 17.4
grid resolution 512, Pentium M 1.6 GHz, 2048 kB cache
efficient data mapping for non-matching grids
Technische Universität München
Application Examples – Drift Ratchet
• silicon wafer pierced with pores
• oscillating pressure conditions
• suspended particles (0.1 – 1.2 m)
• observation: particle drift
Technische Universität München
Conclusion
• applicability of Cartesian Grids
• fast grid generation / updates
• memory efficiency
• numerical efficiency