In particular allow for failed wells to shut even though they haven't failed consitatantly in the last 3 attempts
if the timestep is reduce enhough already.
Implement adaptive time stepping for master and slave procesess
when using reservoir coupling. The original adaptive time stepping method
is refactored at the same time.
so the FlowProblemComp can output ecl style output.
it duplicates the OutputBlackoilModule and GenericOutputBlackoilModule
for compositional related output for now.
This commit switches the parallel implemenation of function
Opm::gatherConvergenceReport() into using the general serialisation
framework (classes Opm::Serializer<> and Opm::Mpi::Packer). In
particular, we add serializeOp() functions to each of the types
- ConvergenceReport
- ConvergenceReport::ReservoirFailure
- ConvergenceReport::ReservoirConvergenceMetric
- ConvergenceReport::WellFailure
and defer the job of converting the objects between in-memory and
byte stream representations to Opm::Serializer<>. The new special
purpose class CollectConvReports inherits from the latter and uses
its pack() and unpack() member functions, along with its internal
m_buffer data member, to distribute each rank's convergence report
object to all ranks. We add this feature here, in a very narrowly
scoped use case, to enable testing and experimentation before we
consider adding this distribution mechanism as a general feature in
Opm::MpiSerializer.
This commit tracks the number of cells and their associate fraction
of the model's "eligible" pore volume (total pore volume in
numerical aquifers subtracted from the model's total pore volume) in
three distinct categories as a function of the non-linear iteration
number:
- 0: MAX_p { CNV_p } <= strict CNV tolerance
- 1: MAX_p { CNV_p } \in (strict, relaxed]
- 2: MAX_p { CNV_p } > relaxed CNV tolerance
We then output these cell counts and pore volume fractions as new
items in the INFOITER file to enable more targeted analysis of the
non-linear convergence behaviour.
To this end, introduce a type alias CnvPvSplit in the
ConvergenceReport and aggregate these across the MPI ranks before we
collect them in the ConvergenceReport objects.
While here, also reduce the amount of repeated logic in
gatherConvergenceReport.cpp through a few local lambdas.
