Hi,
I am writing to seek some advice about use of iterative solvers with
h-refinement.
My application is a GLS solver for compressible Euler equations, and I am
running 2D and 3D simulations at transonic Mach numbers. The initial mesh in 3D
analysis has ~2millions tetrahedra, and the h-refinement process increases this
to ~8million tetrahedra. I am using the standard PETSc GMRES solver with block
Jacobi preconditioner (the default).
The challenge that I am facing is that with mesh refinement the iterative
linear solver takes a large number of iterations to converge, and even with
20,000 iterations the residual is, at times, of order 10^4. I increased the
Krylov subspace dimension form the default value of 30 to ~250, and this took
care of most of the my 2D simulations with refinement. However, this is
continuing to be a problem for 3D. I can certainly increase this subspace to
higher numbers (~1000), but I want to make sure that I am not going out of the
norm here. I am suspecting that this might be too high a number, but I don't
have enough experience to say for sure.
I also highly doubt that there is a problem with the code.
So, a few questions:
-- I am curious about the experience that others have with such
applications?
-- What is the typical Krylov subspace dimension size that people use for
such problems?
-- Are there other built-in preconditioners in PETSc that could do a better
job?
-- Are other custom preconditioners expected to perform better? If so, which
ones?
I would appreciate any comments on this.
Thanks,
Manav
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