foaming_ini = json.load(fl)
## Surrogate model for foam conductivity.
#
# Backward mapping model is used. Gas and polymer conductivity are taken
# from substitute models.
m_foamConductivity = BackwardMappingModel(
_id='foamConductivity',
surrogateFunction=f_foamConductivity,
exactTask=FoamConductivityExactTask(),
substituteModels=[
gasMixtureConductivity.m_gasMixtureConductivity,\
polymerConductivity.m_polymer_thermal_conductivity\
],
initialisationStrategy=Strategy.InitialPoints(
initialPoints=foaming_ini,
),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(
nNewPoints=4
),
parameterFittingStrategy=Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=0.02,
improveErrorStrategy=Strategy.StochasticSampling(
nNewPoints=2
),
maxIterations=5 # Currently not used
),
)
## Foam conductivity simulation.
'max': 2,
'argPos': 2
},
},
)
# --------------------------------------------------------------------------- #
m = BackwardMappingModel(
_id='Rheology',
surrogateFunction=f_dummy,
exactTask=RheologyExactTask_dummy(),
# surrogateFunction= f_tFEM,
# exactTask= RheologyExactTask_tFEM(),
substituteModels=[polymerViscosity.m_polymerViscosity, SurfaceTension.m],
initialisationStrategy=Strategy.InitialPoints(initialPoints={
'T': [270.0, 330.0],
'shear': [0, 10000],
'X': [0, 0.3],
'm0': [2.5e10, 1.1e10],
'm1': [0.1, 0.1],
}, ),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(nNewPoints=4),
parameterFittingStrategy=Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=0.5,
improveErrorStrategy=Strategy.StochasticSampling(nNewPoints=2),
maxIterations=5 # Currently not used
),
)
parameters={
'param0': { 'min': 0.0, 'max': 10.0, 'argPos': 0 },
'param1': { 'min': 0.0, 'max': 10.0, 'argPos': 1 },
},
)
m = BackwardMappingModel(
_id= 'flowRate',
surrogateFunction= f,
exactTask= FlowRateExactSim(),
substituteModels= [ idealGas.m ],
initialisationStrategy= Strategy.InitialPoints(
initialPoints=
{
'D': [0.01, 0.01, 0.01, 0.01],
'T0': [300, 300, 300, 300],
'p0': [2.8e5, 3.2e5, 2.8e5, 3.2e5],
'p1Byp0': [0.03, 0.03, 0.04, 0.04],
},
),
outOfBoundsStrategy= Strategy.ExtendSpaceStochasticSampling(
nNewPoints= 4
),
parameterFittingStrategy= Strategy.NonLinFitWithErrorContol(
testDataPercentage= 0.2,
maxError= 0.05,
improveErrorStrategy= Strategy.StochasticSampling(
nNewPoints= 2
),
maxIterations= 5 # Currently not used
),
'max': 1E10,
'argPos': 1
},
'param2': {
'min': -1E10,
'max': 1E10,
'argPos': 2
},
},
species={
'A': blowing_agents,
'B': monomers
})
m = BackwardMappingModel(
_id='SurfaceTension[A=AIR,B=THF]',
surrogateFunction=f,
exactTask=SurfaceTensionExactSim(),
substituteModels=[],
initialisationStrategy=Strategy.InitialPoints(initialPoints={
'T': [270.0, 290.0, 300.0],
}, ),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(nNewPoints=4),
parameterFittingStrategy=Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=30.0,
improveErrorStrategy=Strategy.StochasticSampling(nNewPoints=2),
maxIterations=5 # Currently not used
),
)
'lamdba': { 'min': 1.35, 'max': 21.35, 'argPos': 0 },
'alpha': { 'min': 0, 'max': 2, 'argPos': 1 },
'n_rh': { 'min': 0, 'max': 2, 'argPos': 2 },
},
)
m = BackwardMappingModel(
_id= 'Rheology',
surrogateFunction= f,
exactTask= RheologyExactTask(),
substituteModels= [ polymerViscosity.m_polymerViscosity, SurfaceTension.m],
# substituteModels= [ ],
initialisationStrategy= Strategy.InitialPoints(
initialPoints=
{
'T': [300.0, 310.0], # 310 is the maximum that is supported by Surface Tension Model
'shear': [0.01, 0.1],
'X': [0.1, 0.3],
},
),
outOfBoundsStrategy= Strategy.ExtendSpaceStochasticSampling(
nNewPoints= 4
),
parameterFittingStrategy= Strategy.NonLinFitWithErrorContol(
testDataPercentage= 0.2,
maxError= 0.5,
improveErrorStrategy= Strategy.StochasticSampling(
nNewPoints= 2
),
maxIterations= 5 # Currently not used
),
)
_id='FoamElasticModulus_TEST',
surrogateFunction=f_backward,
exactTask=MechanicalPropertiesExactSim(),
substituteModels=[],
initialisationStrategy=Strategy.InitialData(
initialData={
'rho': [
30.2, 60, 120, 240, 30.2, 60, 120, 240, 30.2, 30.2, 30.2, 30.2,
30.2, 30.2, 30.2
],
'strut_content': [
0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.85, 0.85, 0.85, 0.85,
0.85, 0.85, 0.85
],
'Mu': [
0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39,
0.39, 0.39, 0.39, 0.39, 0.39
],
'Sigma': [
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.01, 0.05,
0.1, 0.15, 0.2, 0.25, 0.30
],
'E': [
8.16, 22.85, 52.89, 124.73, 6.53, 13.06, 30.36, 65.63, 4.97,
5.48, 5.42, 4.52, 4.61, 4.42, 4.79
],
}),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(nNewPoints=4),
parameterFittingStrategy=Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=9e99,
'min': 0.0,
'max': 1.0
},
}
f2 = CFunction(Ccode=Ccode2,
inputs=inputs2,
outputs=outputs,
parameters=parameters,
indices=indices)
f3 = CFunction(Ccode=Ccode3,
inputs=inputs3,
outputs=outputs,
parameters=parameters4,
indices=indices)
outOfBoundsStrategy = Strategy.ExtendSpaceStochasticSampling(nNewPoints=4)
parameterFittingStrategy = Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=0.1,
improveErrorStrategy=Strategy.StochasticSampling(nNewPoints=2),
maxIterations=5 # Currently not used
)
m_solubilityCO2PU = BackwardMappingModel(
_id='Solubility[A=CO2,B=2]',
surrogateFunction=f2,
exactTask=SolubilityExactSim(),
substituteModels=[],
initialisationStrategy=Strategy.InitialPoints(initialPoints={
'T': [270, 320, 370, 420],
'xl1': [1.1e-3, 1.0e-3, 1.0e-3, 1.0e-4],
'xl2': [0.9989, 0.999, 0.999, 0.9999],
@namespace wallDrainage.wallDrainage
@ingroup mod_wallDrainage
@brief Backward mapping Firetask for Wall drainage model.
@author Pavel Ferkl
@copyright 2014-2016, MoDeNa Project. GNU Public License.
@details
# Wall drainage python module
Contains a FireTask, which runs the detailed model and copies the results into
the results folder. The relative path and name of the detailed model executable
are hard coded. The FireTask is BackwardMapping, which means that if one of the
lower scale backward mapping models will get out of validity range that model
will be re-fitted to larger range and the detailed model will be re-run. This is
repeated until the detailed model succesfully finishes (or possibly crashes for
other reason, in which case an error is printed out).
"""
import os
from modena import Strategy
from modena.Strategy import BackwardMappingScriptTask
## Wall drainage application recipe
#
# Runs the detailed model and saves the results.
m_simulation = Strategy.BackwardMappingScriptTask(
script=os.path.dirname(os.path.abspath(__file__)) + '/wallDrainage' +
' && cp filmthickness.csv ../results/wallDrainage/' +
' && cp results_1d.csv ../results/wallDrainage/')
return {
key: [
sr[key]['min'] +
(sr[key]['min'] - sr[key]['min']) * points[j][i]
for j in xrange(nPoints)
]
for (i, key) in enumerate(sr)
}
m = BackwardMappingModel(
_id='flowRate',
surrogateFunction=f,
exactTask=FlowRateExactSim(),
substituteModels=[],
initialisationStrategy=Strategy.InitialPoints(initialPoints={
'D': [0.01, 0.01, 0.01, 0.01],
'rho0': [3.4, 3.5, 3.4, 3.5],
'p0': [2.8e5, 3.2e5, 2.8e5, 3.2e5],
'p1Byp0': [0.03, 0.03, 0.04, 0.04],
}, ),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(nNewPoints=4),
parameterFittingStrategy=Strategy.Test(
testDataPercentage=0.2,
maxError=0.5,
improveErrorStrategy=TemporaryClass(nNewPoints=2,
constraints="p0 / p1 > 0"),
maxIterations=5 # Currently not used
),
)
import os
import modena
from modena import ForwardMappingModel, BackwardMappingModel, SurrogateModel, \
CFunction
import modena.Strategy as Strategy
from fireworks.user_objects.firetasks.script_task import FireTaskBase, ScriptTask
from fireworks import Firework, Workflow, FWAction
from fireworks.utilities.fw_utilities import explicit_serialize
from blessings import Terminal
from jinja2 import Template
import Rheology
## Create terminal for colour output
term = Terminal()
# For the case, when only foam conductivity and no aging is needed.
m = Strategy.BackwardMappingScriptTask(
script=os.path.dirname(os.path.abspath(__file__)) +
'/src_dummy/workflowdummy')
_id= 'idealGas',
#exactTask= Strategy.InitialDataPoints(),
surrogateFunction= f2,
substituteModels= [ ],
parameters= [ 287.0 ],
inputs={
'p0': { 'min': 0, 'max': 9e99 },
'T0': { 'min': 0, 'max': 9e99 },
},
outputs={
'rho0': {'min': 0, 'max': 9e99 },
},
initialisationStrategy= Strategy.InitialData(
initialData={
'p0' : [1,2,3,4,5],
'T0' : [296,297,298,299,300],
'rho0' : [0.000011771353234, 0.00002346343809758, 0.00003507705259219, 0.00004661298404, 0.0000580720092915],
},
),
)
m2 = BackwardMappingModel(
_id= 'idealGasBackward',
exactTask= Strategy.InitialDataPoints(),
surrogateFunction= f1,
substituteModels= [ ],
parameters= [ 287.0 ],
inputs={
'p0': { 'min': 0, 'max': 9e99 },
'T0': { 'min': 0, 'max': 9e99 },
},
'x[CyP]': setIP(xCyP0),
}
## Surrogate model for foam conductivity
#
# Backward mapping model is used.
m_foamConductivity = BackwardMappingModel(
_id='foamConductivity',
surrogateFunction=f_foamConductivity,
exactTask=FoamConductivityExactTask(),
substituteModels=[
gasMixtureConductivity.m_gasMixtureConductivity,\
polymerConductivity.m_polymer_thermal_conductivity\
],
initialisationStrategy=Strategy.InitialPoints(
initialPoints=initialPoints_foamConductivity_auto,
),
outOfBoundsStrategy=Strategy.ExtendSpaceStochasticSampling(
nNewPoints=4
),
parameterFittingStrategy=Strategy.NonLinFitWithErrorContol(
testDataPercentage=0.2,
maxError=0.01,
improveErrorStrategy=Strategy.StochasticSampling(
nNewPoints=2
),
maxIterations=5 # Currently not used
),
)
## Foam conductivity simulation