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Barry,<br>
<br>
sorry for not replying to your other e-mail earlier.<br>
The equation I am solving is:<br>
<br>
<img style="vertical-align: middle"
src="cid:part1.02090407.09020206@uci.edu"
alt="$\nabla\cdot(\frac{1}{\rho}\nabla p)=\nabla\cdot u^*$"><br>
<br>
where <img style="vertical-align: middle"
src="cid:part2.09040601.00080502@uci.edu" alt="$p$"> is the
pressure field, <img style="vertical-align: middle"
src="cid:part3.00030104.06080306@uci.edu" alt="$\rho$"> the
density field and <img style="vertical-align: middle"
src="cid:part4.04090409.06000008@uci.edu" alt="$u^*$"> the
velocity field. <br>
Since I am using finite difference on a staggered grid, the pressure
is defined on "cell" centers, while the velocity components on
"cell" faces, even if<br>
no cell is actually defined.<br>
I am simulating a bi-phase flow, thus both density and pressure are
discontinuos, but not the velocity (no mass trasfer is included at
the moment).<br>
Therefore the right-hand-side (rhs) of the above equation does not
have jumps, while $p$ and $rho$ do.<br>
In order to deal with such jumps, I am using a Ghost Fluid approach.
Therefore the resulting linear system is slighly different from the
typical Poisson system, though<br>
simmetry and diagonal dominance of the matrix are mantained.<br>
The boundary conditions are periodic in all the three space
directions, therefore the system is singular. Thus I removed the
nullspace of the matrix by using:<br>
<br>
call MatNullSpaceCreate(
PETSC_COMM_WORLD,PETSC_TRUE,PETSC_NULL_INTEGER,&<br>
&
PETSC_NULL_INTEGER,nullspace,ierr)<br>
call KSPSetNullspace(ksp,nullspace,ierr)<br>
call MatNullSpaceDestroy(nullspace,ierr) <br>
<br>
Hope this helps. Please let me know if you need any other info.<br>
Also, I use the pressure at the previous time step as starting point
for the solve. Could this be a reason why the convergence is so
slow?<br>
Thanks a lot,<br>
<br>
Michele<br>
<br>
<br>
<br>
<br>
<br>
<br>
<div class="moz-cite-prefix">On 10/02/2013 11:39 AM, Barry Smith
wrote:<br>
</div>
<blockquote
cite="mid:7229EA34-CB99-44B3-B4CE-DE5F71CF141A@mcs.anl.gov"
type="cite">
<pre wrap="">
Something is wrong, you should be getting better convergence. Please answer my other email.
On Oct 2, 2013, at 1:10 PM, Michele Rosso <a class="moz-txt-link-rfc2396E" href="mailto:mrosso@uci.edu"><mrosso@uci.edu></a> wrote:
</pre>
<blockquote type="cite">
<pre wrap="">Thank you all for your contribution.
So far the fastest solution is still the initial one proposed by Jed in an earlier round:
-ksp_atol 1e-9 -ksp_monitor_true_residual -ksp_view -log_summary -mg_coarse_pc_factor_mat_solver_package superlu_dist
-mg_coarse_pc_type lu -mg_levels_ksp_max_it 3 -mg_levels_ksp_type richardson -options_left -pc_mg_galerkin
-pc_mg_levels 5 -pc_mg_log -pc_type mg
where I used -mg_levels_ksp_max_it 3 as Barry suggested instead of -mg_levels_ksp_max_it 1.
I attached the diagnostics for this case. Any further idea?
Thank you,
Michele
On 10/01/2013 11:44 PM, Barry Smith wrote:
</pre>
<blockquote type="cite">
<pre wrap="">On Oct 2, 2013, at 12:28 AM, Jed Brown <a class="moz-txt-link-rfc2396E" href="mailto:jedbrown@mcs.anl.gov"><jedbrown@mcs.anl.gov></a> wrote:
</pre>
<blockquote type="cite">
<pre wrap="">"Mark F. Adams" <a class="moz-txt-link-rfc2396E" href="mailto:mfadams@lbl.gov"><mfadams@lbl.gov></a> writes:
</pre>
<blockquote type="cite">
<pre wrap="">run3.txt uses:
-ksp_type richardson
This is bad and I doubt anyone recommended it intentionally.
</pre>
</blockquote>
</blockquote>
<pre wrap=""> Hell this is normal multigrid without a Krylov accelerator. Under normal circumstances with geometric multigrid this should be fine, often the best choice.
</pre>
<blockquote type="cite">
<pre wrap="">I would have expected FGMRES, but Barry likes Krylov smoothers and
Richardson is one of a few methods that can tolerate nonlinear
preconditioners.
</pre>
<blockquote type="cite">
<pre wrap="">You also have, in this file,
-mg_levels_ksp_type gmres
did you or the recommenders mean
-mg_levels_ksp_type richardson ???
you are using gmres here, which forces you to use fgmres in the outer solver. This is a safe thing to use you if you apply your BCa symmetrically with a low order discretization then
-ksp_type cg
-mg_levels_ksp_type richardson
-mg_levels_pc_type sor
is what I'd recommend.
</pre>
</blockquote>
<pre wrap="">I thought that was tried in an earlier round.
I don't understand why SOR preconditioning in the Krylov smoother is so
drastically more expensive than BJacobi/ILU and why SOR is called so
many more times even though the number of outer iterations
bjacobi: PCApply 322 1.0 4.1021e+01 1.0 6.44e+09 1.0 3.0e+07 1.6e+03 4.5e+04 74 86 98 88 92 28160064317351226 20106
bjacobi: KSPSolve 46 1.0 4.6268e+01 1.0 7.52e+09 1.0 3.0e+07 1.8e+03 4.8e+04 83100100 99 99 31670065158291309 20800
sor: PCApply 1132 1.0 1.5532e+02 1.0 2.30e+10 1.0 1.0e+08 1.6e+03 1.6e+05 69 88 99 88 93 21871774317301274 18987
sor: KSPSolve 201 1.0 1.7101e+02 1.0 2.63e+10 1.0 1.1e+08 1.8e+03 1.7e+05 75100100 99 98 24081775248221352 19652
</pre>
</blockquote>
<pre wrap="">
</pre>
</blockquote>
<pre wrap="">
<best.txt>
</pre>
</blockquote>
<pre wrap="">
</pre>
</blockquote>
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