[petsc-users] questions about the multigrid framework

[Peter Wang]

Hello, I have some concerns about the multigrid framework in PETSc.
 
We are trying to solve a two dimensional problem with a large variety in length scales.  The length of computational domain is in order of 1e3 m, and the width is in 1 m, nevertheless, there is a tiny object with 1e-3 m in a corner of the domain. 
 
As a first thinking, we tried to solve the problem with a larger number of uniform or non-uniform grids.  However, the error of the numerical solution increases when the number of the grid is too large.  In order to test the effect of the grid size on the solution, a domain with regular scale of 1m by 1m was tried to solve.  It is found that the extreme small grid size might lead to large variation to the exact solution.  For example, the exact solution is a linear distribution in the domain. The numerical solution is linear as similar as the exact solution when the grid number is nx=1000 by ny=1000.  However, if the grid number is nx=10000 by ny=10000, the numerical solution varies to nonlinear distribution which boundary is the only same as the exact solution.  The solver I used is a KSP solver in PETSc, which is set by calling :
KSPSetOperators(ksp,A,A,DIFFERENT_NONZERO_PATTERN,ierr).  Whether this solver is not suitable to the system with small size grid? Or, whether the problem crossing 6 orders of length scale is solvable with only one level grid system when the memory is enough for large matrix? Since there is less coding work for one level grid size, it would be easy to implement the solver.
 
I did some research work on the website and found the slides by Barry on
 http://www.mcs.anl.gov/petsc/petsc-2/documentation/tutorials/Columbia04/DDandMultigrid.pdf
It seems that the multigrid framework in PETSc is a possible approach to our problem.  We are thinking to turn to the multigrid framework in PETSc to solve the problem.  However, before we dig into it, there are some issues confusing us.  It would be great if we can get any suggestion from you:
1  Whether the multigrid framework can handle the problem with a large variety in length scales (up to 6 orders)? Is DMMG is the best tool for our problem? 
 
2  The coefficient matrix A and the right hand side vector b were created for the finite difference scheme of the domain and solved by KSP solver (call KSPSetOperators(ksp,A,A,DIFFERENT_NONZERO_PATTERN,ierr)).  Is it easy to immigrate the created Matrix A and Vector b to the multigrid framework?
 
3  How many levels of the subgrid are needed to obtain a solution close enough to the exact solution for a problem with 6 orders in length scale?
  		 	   		  
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