cd SodProblem
bash run.sh
python postProc.py
cd LaxProblem
bash run.sh
python postProc.py
cd ShuOsherProblem
bash run.sh
python postProc.py
cd GrossmanCinnellaProblem
bash run.sh
python postProc.py
cd Blasius
python MakeProfile.py
bash run.sh
python postProc.py
cd CompressibleBL
python MakeInput.py CBL.json
bash run.sh
python postProc.py
cd VortexAdvection2D
bash run.sh [Number of refinements]
python postProc.py -n [Number of refinements]
cd TaylorGreen2D
bash run.sh [Number of refinements]
python postProc.py -n [Number of refinements]
The number of nodes for the calculation is set using the Mapping/tiles
and Mapping/tilesPerRank
parameters in base.json.
It is strongly advised to run with one tile per GPU
cd Coleman
python MakeChannel.py base.json
$HTR_DIR/prometeo.sh -i ChannelFlow.json
$HTR_DIR/prometeo.sh -i ChannelFlowStats.json
python postProc.py -json ChannelFlowStats.json -in [Averages files produced by the code]
The number of nodes for the calculation is set using the Mapping/tiles
and Mapping/tilesPerRank
parameters in base.json.
It is strongly advised to run with one tile per GPU
cd Sciacovelli
python MakeChannel.py base.json
$HTR_DIR/prometeo.sh -i ChannelFlow.json
$HTR_DIR/prometeo.sh -i ChannelFlowStats.json
python postProc.py -json ChannelFlowStats.json -in [Averages files produced by the code]
The number of nodes for the calculation is set using the Mapping/tiles
and Mapping/tilesPerRank
parameters in base.json.
It is strongly advised to run with one tile per GPU
cd Franko
python MakeInput.py base.json
$HTR_DIR/prometeo.sh -i NoStats.json
$HTR_DIR/prometeo.sh -i Stats.json
python postProc.py -json Stats.json -in [Averages files produced by the code]
The number of nodes for the calculation is set using the Mapping/tiles
and Mapping/tilesPerRank
parameters in base.json.
It is strongly advised to run with one tile per GPU
cd MultispeciesTBL
python MakeInput.py base.json
$HTR_DIR/prometeo.sh -i NoStats.json
$HTR_DIR/prometeo.sh -i Stats.json
python postProc.py -json Stats.json -in [Averages files produced by the code]
cd Speelman
python Speelman.py (This is optional. It produces the reference solution using Cantera)
$HTR_DIR/prometeo.sh -i Speelman.json
python postProc.py
cd Speelman_DV250
python mkHTRrestart.py
$HTR_DIR/prometeo.sh -i Speelman.json
python postProc.py
This test case is similar to Speelman
but provides the premixed laminar flame
speed, which is readily compared with published values. Depending on the
system, the wallTime
parameter in base.json
may need to be adjusted in
order to reach a steady state. Four cases are run at varying equivalence
ratios and compared with a Cantera-generated solution.
cd PlanarFlame1D
source run.sh
python3 postProc.py
cd scalingTest/WeakScaling
python scale_up.py -n [Number of refinements] -out [output dir] base.json
python postProc.py -n [Number of refinements] -out [output dir]
cd scalingTest/StrongScaling
python scale_up.py -n [Number of refinements] -out [output dir] base.json
python postProc.py -n [Number of refinements] -out [output dir]
This test runs a low-energy case for the GeometricKernel
laser model, post-processes
the solution files, and checks global quantities.
cd LaserInBox
source run.sh
source run-postproc.sh # Run this after the simulation is done.
### High-Fidelity along with Low fidelity test
cd scalingTest/LFHF python scale_up.py -n [Number of refinements] --lp lf.json -out [output dir] base.json python postProc.py -n [Number of refinements] -out [output dir]