Multigrid Methods The Implementation

1. Multigrid MethodsThe Implementation Wei E CSE@Technische Universität München. Ferien Akademie 19th Sep. 2005

2. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

3. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

4. Introduction • Fluid Dynamics • Solving PDEs • Computational Fluid Dynamics • Numerical Solution

5. The Navier-Stokes Equations (1) • Non-stationary incompressible viscous fluids • 2D Cartesian coordinates • system of partial differential equations • two momentum equations + continuity equation

6. The Navier-Stokes Equations (2)

7. Poisson Equation • Where f(x,y) is the right-hand side calculated by the quantities in the previous time step; • the unknown u is to be solved in the current time step.

8. Discretization (1) • Finite Difference Scheme:

9. Discretization (2) • The corresponding matrix representation is: where

10. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

11. Multigrid Implementation • The two classical schemes: V-Cycle Full Multigrid (FMG)

12. V-Cycle: The Algorithm grid = { double Ddim_array f // the right hand side double Ddim_array v // the current approximation } Grid = array of structure grid. for j = 0 to coarsest - 1 Grid[j].v <- relax(Grid[j].v, Grid[j].f, num_sweeps_down); Grid[j+1].f <-restrict(Grid[j].f- calculate_rhs(Grid[j].v)); endfor Grid[coarsest].v = direct_solve(Grid[coarsest].v, Grid[coarsest].f); for j = coarsest – 1 to 0 Grid[j].v <- Grid[j].v + interpolate(Grid[j+1].v); Grid[j].v <-relax(Grid[j].v, Grid[j].f, num_sweeps_up); endfor

13. V-Cycle: Comments (1) • a) Non-recursive structure; • b) Gauss-Seidel is used as the relaxation method; • c) calculate_rhs() is a function to calculate the right-hand side based on current approximation; • d) Several methods can be used to solve the small-size problem, we choose SOR (successive over relaxation);

14. V-Cycle: Comments (2) • e) For restriction, we take the mean value of the four neighbors as the result • f) For interpolation, we use the similar method as restriction: spreading the value into it’s four neighbors. restriction interpolation

15. FMG: The Algorithm Once we have the V-cycle, FMG would be rather easy to implement: for j = 0 to coarsest - 1 Grid[coarsest-j+1].v <- Grid[coarsest-j+1].v + interpolate_fine(Grid[coarsest-j].v); Grid[coarsest-j+1].v <- V-cycle(Grid[coarsest-j+1].v, Grid[coarsest-j+1].f); endfor

16. FMG: Comments • a) Initialization for all the approximations and right-hand sides should be made before executing the FMG main loop; • b) interpolate_fine() stands for a higher order interpolator. In practice, we use the interpolation matrix:

17. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

18. Model Problem (1): Hidden Step • Fluid flows with a constant velocity through a channel with a hidden obstacle on one side. No-slip conditions are imposed at the upper and lower walls.

19. Simulation result (1) • The Hidden Step:

20. Model Problem (2): Karman Vortex • The flow in a channel can meet a tilted plate. At the left boundary, the fluid inflow has a constant velocity profile, while at the upper and lower boundaries no-slip conditions are imposed.

21. Simulation result (2) • Von Karman Vortex

22. Performance (1) Testing platform: P4 2.4GHz, 1GB Memory, SUSE Linux 9.3

23. Performance (2)

24. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

25. A Failing Example • Throttle :

26. Simulation Result

27. The Reason

28. Content • Introduction • Algorithm • Results & Performance • A Failing Example • Conclusion

29. Conclusion • An Optimal (i.e., O(N)) Solver. • Highly Modular Program Structure • Advanced Debugging Technique

30. Reference • [1] Practical Course – Scientific Computing and Visualization Worksheet, Lehrstuhl für Informatik mit Schwerpunkt Wissenschaftliches Rechnen, TU-Muenchen, 2005. • [2] Krzysztof J. Fidkowski, A High-Order Discontinuous Galerkin Multigrid Solver for Aerodynamic Applications, Master Thesis in Aerospace Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY • [3] S. McCormick, B. Briggs, and V. Henson, "A Multigrid Tutorial”, second edition, SIAM,Philadelphia, June 2000.

31. Thank You!