def test_tracing(self):
# Check tracing of iteration coordinates.
top = Assembly()
comp = top.add('comp1', Dummy())
top.add('driverA', Driver())
comp = top.add('comp2', Dummy())
top.add('driverB', Driver())
sub = top.add('subassy', Assembly())
comp = sub.add('comp3', Dummy())
sub.driver.workflow.add('comp3')
top.driver.workflow = SequentialWorkflow()
top.driver.workflow.add(('comp1', 'driverA', 'driverB'))
top.driverA.workflow.add(('comp1', 'comp2'))
top.driverB.workflow.add(('comp2', 'subassy'))
trace_out = cStringIO.StringIO()
enable_trace(trace_out)
top.run()
expected = """\
1-comp1
1-driverA.1-comp1
1-driverA.1-comp2
1-driverB.1-comp2
1-driverB.1-subassy.1-comp3
"""
self.assertEqual(trace_out.getvalue(), expected)
disable_trace()
top.run()
self.assertEqual(trace_out.getvalue(), expected)
def configure(self):
self.add('d1', Discipline1_WithDerivatives())
self.d1.x1 = 1.0
self.d1.y1 = 1.0
self.d1.y2 = 1.0
self.d1.z1 = 5.0
self.d1.z2 = 2.0
self.add('d2', Discipline2_WithDerivatives())
self.d2.y1 = 1.0
self.d2.y2 = 1.0
self.d2.z1 = 5.0
self.d2.z2 = 2.0
self.add('P1', Paraboloid())
self.add('P2', Paraboloid())
self.connect('d1.y1', 'd2.y1')
self.connect('P1.f_xy', 'd1.x1')
self.connect('d1.y1', 'P2.x')
self.connect('d2.y2', 'P2.y')
self.add('driver', SimpleDriver())
self.add('driver2', Driver())
self.add('subdriver', NewtonSolver())
self.driver.workflow.add(['P1', 'subdriver', 'P2'])
self.subdriver.workflow.add(['d1', 'd2'])
self.subdriver.add_parameter('d1.y2', low=-1e99, high=1e99)
self.subdriver.add_constraint('d1.y2 = d2.y2')
self.driver.add_parameter('P1.x', low=-1e99, high=1e99)
self.driver.add_constraint('P2.f_xy < 0')
def configure_AEPWindRose(self):
"""Generic configure method for AEPSingleWindRose and AEPMultipleWindRoses.
After this configure method is called, some connections are hanging loose:
- if wind_rose_type == 'single':
- case_gen.wind_rose
- if wind_rose_type == 'multiple':
- case_gen.wt_layout
Examples
--------
### In a single wind rose case:
def configure(self):
self.add('case_gen', SingleWindRoseCaseGenerator())
self.add('postprocess_wind_rose', PostProcessSingleWindRose())
configure_AEPWindRose(self)
self.connect('wind_rose', 'case_gen.wind_rose')
### In a multiple wind rose case:
def configure(self):
self.add('case_gen', MultipleWindRosesCaseGenerator())
self.add('postprocess_wind_rose', PostProcessMultipleWindRoses())
configure_AEPWindRose(self)
self.connect('wt_layout', 'case_gen.wt_layout')
"""
# Adding
self.add('driver', Driver())
self.add('wind_rose_driver', CaseIteratorDriver())
self.add_default('wf', GenericWindFarm())
self.add_default('case_gen', GenericWindRoseCaseGenerator())
self.add_default('postprocess_wind_rose', GenericPostProcessWindRose())
# Drivers configuration
self.wind_rose_driver.workflow.add('wf')
self.wind_rose_driver.add_parameter('wf.wind_speed')
self.wind_rose_driver.add_parameter('wf.wind_direction')
self.wind_rose_driver.add_responses(
['wf.power', 'wf.wt_power', 'wf.wt_thrust'])
self.driver.workflow.add(
['case_gen', 'wind_rose_driver', 'postprocess_wind_rose'])
# wiring
self.connect('wind_speeds',
['case_gen.wind_speeds', 'postprocess_wind_rose.wind_speeds'])
self.connect(
'wind_directions',
['case_gen.wind_directions', 'postprocess_wind_rose.wind_directions'])
self.connect('case_gen.all_wind_speeds',
'wind_rose_driver.case_inputs.wf.wind_speed')
self.connect('case_gen.all_wind_directions',
'wind_rose_driver.case_inputs.wf.wind_direction')
self.connect('case_gen.all_frequencies',
'postprocess_wind_rose.frequencies')
self.connect('wind_rose_driver.case_outputs.wf.power',
'postprocess_wind_rose.powers')
self.connect('postprocess_wind_rose.net_aep', 'net_aep')
self.connect('postprocess_wind_rose.array_aep', 'array_aep')
def test_driver(self):
# Ensure we can't add a Driver to a component that is not an Assembly.
comp = Component()
try:
comp.add('driver', Driver())
except Exception as err:
self.assertEqual(str(err),
"A Driver may only be added to an Assembly")
pass
def test_gradient_options(self):
options = GradientOptions()
assert (options.get_metadata("fd_form")["framework_var"])
assert (options.get_metadata("fd_step")["framework_var"])
assert (options.get_metadata("fd_step_type")["framework_var"])
assert (options.get_metadata("force_fd")["framework_var"])
assert (options.get_metadata("gmres_tolerance")["framework_var"])
assert (options.get_metadata("gmres_maxiter")["framework_var"])
assert (Driver().get_metadata("gradient_options")["framework_var"])
def test_gradient_options(self):
options = GradientOptions()
assert (options.get_metadata("directional_fd")["framework_var"])
assert (options.get_metadata("derivative_direction")["framework_var"])
assert (options.get_metadata("fd_form")["framework_var"])
assert (options.get_metadata("fd_step")["framework_var"])
assert (options.get_metadata("fd_step_type")["framework_var"])
assert (options.get_metadata("force_fd")["framework_var"])
assert (options.get_metadata("lin_solver")["framework_var"])
assert (options.get_metadata("atol")["framework_var"])
assert (options.get_metadata("rtol")["framework_var"])
assert (options.get_metadata("maxiter")["framework_var"])
assert (Driver().get_metadata("gradient_options")["framework_var"])
def setup(self, compname, kwargs):
self.model.add('comp', eval('%s(**kwargs)' % compname))
self.model.add('sub', Driver())
self.model.driver.workflow.add('sub')
self.model.sub.workflow.add('comp')
self.inputs, self.outputs = self.model.comp.list_deriv_vars()
for item in self.inputs:
val = self.model.comp.get(item)
if hasattr(val, 'shape'):
shape1 = val.shape
self.model.comp.set(item, np.random.random(shape1))
else:
self.model.comp.set(item, random.random())
def test_mimic(self):
# Ensure we can mimic a driver.
top = Assembly()
top.add('c1', Component())
top.add('c2', Component())
top.driver.workflow.add(('c1', 'c2'))
top.driver.printvars = ['c1.force_execute', 'c2.force_execute']
recorder1 = FakeRecorder()
recorder2 = FakeRecorder()
top.driver.recorders = [recorder1, recorder2]
workflow_id = id(top.driver.workflow)
new_driver = Driver()
new_id = id(new_driver)
self.assertNotEqual(new_id, id(top.driver))
top.replace('driver', new_driver)
self.assertEqual(new_id, id(top.driver))
self.assertEqual(workflow_id, id(top.driver.workflow))
self.assertEqual(top.driver.printvars,
['c1.force_execute', 'c2.force_execute'])
self.assertEqual(top.driver.recorders, [recorder1, recorder2])
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
#objective = '(dis1.x1)**2 + dis1.z2 + dis1.y1 + exp(-dis2.y2)'
#constraint1 = 'dis1.y1 > 3.16'
#constraint2 = 'dis2.y2 < 24.0'
# Metamodel for sellar discipline 1
self.add("meta_model_dis1", MetaModel())
self.meta_model_dis1.surrogates = {"y1": ResponseSurface()}
self.meta_model_dis1.model = SellarDiscipline1()
self.meta_model_dis1.recorder = DBCaseRecorder()
self.meta_model_dis1.force_execute = True
# Metamodel for sellar discipline 2
self.add("meta_model_dis2", MetaModel())
self.meta_model_dis2.surrogates = {"y2": ResponseSurface()}
self.meta_model_dis2.model = SellarDiscipline2()
self.meta_model_dis2.recorder = DBCaseRecorder()
self.meta_model_dis2.force_execute = True
#training metalmodel for disc1
# self.add("DOE_Trainer_dis2",NeighborhoodDOEdriver())
# self.DOE_Trainer_dis2.DOEgenerator = CentralComposite()
# self.DOE_Trainer_dis2.alpha = .1
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.z1",low=-10,high=10,start=5.0)
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.z2",low=0,high=10,start=2.0)
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.y1",low=0,high=20)
# self.DOE_Trainer_dis2.add_event("meta_model_dis2.train_next")
# self.DOE_Trainer_dis2.force_execute = True
#optimization of global objective function
self.add('sysopt', SLSQPdriver())
self.sysopt.add_objective(
'(meta_model_dis1.x1)**2 + meta_model_dis1.z2 + meta_model_dis1.y1 + math.exp(-meta_model_dis2.y2)'
)
self.sysopt.add_parameter(['meta_model_dis1.z1', 'meta_model_dis2.z1'],
low=-10,
high=10.0,
start=5.0)
self.sysopt.add_parameter(['meta_model_dis1.z2', 'meta_model_dis2.z2'],
low=0,
high=10.0,
start=2.0)
self.sysopt.add_parameter('meta_model_dis1.y2', low=-1e99, high=1e99)
self.sysopt.add_parameter('meta_model_dis2.y1', low=-1e99, high=1e99)
#feasibility constraints
self.sysopt.add_constraint('meta_model_dis1.y2 <= meta_model_dis2.y2')
self.sysopt.add_constraint('meta_model_dis1.y2 >= meta_model_dis2.y2')
self.sysopt.add_constraint('meta_model_dis2.y1 <= meta_model_dis1.y1')
self.sysopt.add_constraint('meta_model_dis2.y1 >= meta_model_dis1.y1')
self.sysopt.add_constraint('3.16 < meta_model_dis1.y1')
self.sysopt.add_constraint('meta_model_dis2.y2 < 24.0')
self.sysopt.force_execute = True
#optimization of discipline 1 (discipline 2 of the sellar problem has no local variables)
self.add('local_opt_dis1', SLSQPdriver())
self.local_opt_dis1.add_objective('meta_model_dis1.y1')
self.local_opt_dis1.add_parameter('meta_model_dis1.x1',
low=0,
high=10.0)
self.local_opt_dis1.add_constraint('3.16 < meta_model_dis1.y1')
self.local_opt_dis1.add_event('meta_model_dis1.train_next')
self.local_opt_dis1.force_execute = True
self.local_opt_dis1.workflow.add(['meta_model_dis1'])
#training metalmodel for disc1
self.add("DOE_Trainer_dis1", NeighborhoodDOEdriver())
self.DOE_Trainer_dis1.DOEgenerator = CentralComposite()
self.DOE_Trainer_dis1.alpha = .1
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.z1",
low=-10,
high=10,
start=5.0)
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.z2",
low=0,
high=10,
start=2.0)
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.y2",
low=-100,
high=100)
self.DOE_Trainer_dis1.add_event("meta_model_dis1.train_next")
self.DOE_Trainer_dis1.force_execute = True
self.DOE_Trainer_dis1.workflow.add("local_opt_dis1")
self.add('reset_train', Driver())
self.reset_train.add_event('meta_model_dis1.reset_training_data')
self.reset_train.add_event('meta_model_dis2.reset_training_data')
self.reset_train.workflow.add(['meta_model_dis1', 'meta_model_dis2'])
self.reset_train.force_execute = True
#build workflow for bliss2000
self.add('driver', FixedPointIterator())
#self.add('main_driver', IterateUntil())
#self.main_driver.max_iterations = 1
self.driver.tolerance = .0001
# self.driver.workflow.add(['local_opt_dis1','reset_train','DOE_Trainer_dis1','DOE_Trainer_dis2','sysopt'])
self.driver.workflow.add(['sysopt'])
self.driver.add_parameter('x1_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.x1 = x1_store')
self.driver.add_parameter('z1_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.z1 = z1_store')
self.driver.add_parameter('z2_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.z2 = z2_store')
def _setup(self):
""" Setup to begin new run. """
# if params have changed we need to setup systems again
if self.workflow._system is None:
obj = self
while obj.parent is not None:
obj = obj.parent
obj._setup()
if not self.sequential:
# Save model to egg.
# Must do this before creating any locks or queues.
self._replicants += 1
version = 'replicant.%d' % (self._replicants)
# If only local host will be used, we can skip determining
# distributions required by the egg.
allocators = RAM.list_allocators()
need_reqs = False
if not self.ignore_egg_requirements:
for allocator in allocators:
if not isinstance(allocator, LocalAllocator):
need_reqs = True
break
# Replicate and mutate model to run our workflow once.
# Originally this was done in-place, but that 'invalidated'
# various workflow quantities.
replicant = self.parent.copy()
workflow = replicant.get(self.name + '.workflow')
driver = replicant.add('driver', Driver())
workflow.parent = driver
workflow.scope = None
replicant.driver.workflow = workflow
egg_info = replicant.save_to_egg(self.name,
version,
need_requirements=need_reqs)
replicant = workflow = driver = None # Release objects.
gc.collect() # Collect/compact before possible fork.
self._egg_file = egg_info[0]
self._egg_required_distributions = egg_info[1]
self._egg_orphan_modules = [name for name, path in egg_info[2]]
inp_paths = []
inp_values = []
for path, param in self.get_parameters().items():
if isinstance(path, tuple):
path = path[0] # Use first target of ParameterGroup.
path = make_legal_path(path)
value = self.get('case_inputs.' + path)
for target in param.targets:
inp_paths.append(target)
inp_values.append(value)
outputs = self.get_responses().keys()
extra_outputs = self.workflow._rec_outputs
length = len(inp_values[0]) if inp_values else 0
cases = []
for i in range(length):
inputs = []
for j in range(len(inp_paths)):
inputs.append((inp_paths[j], inp_values[j][i]))
cases.append(
_Case(i,
inputs,
outputs,
extra_outputs,
parent_uuid=self._case_uuid))
self.init_responses(length)
self._iter = iter(cases)
self._abort_exc = None
def configure(self):
self._tdir = mkdtemp()
self.comp_name = None
#check to make sure no more than one component is being referenced
compnames = set()
for param in self.parent.get_parameters().values():
compnames.update(param.get_referenced_compnames())
if len(compnames) > 1:
self.parent.raise_exception('The EGO architecture can only be used on one'
'component at a time, but parameters from %s '
'were added to the problem formulation.' %compnames,
ValueError)
self.comp_name = compnames.pop()
#change name of component to add '_model' to it.
# lets me name the metamodel as the old name
self.comp= getattr(self.parent,self.comp_name)
self.comp.name = "%s_model"%self.comp_name
#add in the metamodel
meta_model = self.parent.add(self.comp_name,MetaModel()) #metamodel now replaces old component with same name
meta_model.default_surrogate = KrigingSurrogate()
meta_model.model = self.comp
meta_model_recorder = DBCaseRecorder(os.path.join(self._tdir,'trainer.db'))
meta_model.recorder = meta_model_recorder
EI = self.parent.add("EI",ExpectedImprovement())
self.objective = self.parent.get_objectives().keys()[0]
EI.criteria = self.objective
pfilter = self.parent.add("filter",ParetoFilter())
pfilter.criteria = [self.objective]
pfilter.case_sets = [meta_model_recorder.get_iterator(),]
#Driver Configuration
DOE_trainer = self.parent.add("DOE_trainer",DOEdriver())
DOE_trainer.sequential = True
DOE_trainer.DOEgenerator = OptLatinHypercube(num_samples=self.initial_DOE_size)
for name,param in self.parent.get_parameters().iteritems():
DOE_trainer.add_parameter(param)
DOE_trainer.add_event("%s.train_next"%self.comp_name)
DOE_trainer.case_outputs = [self.objective]
DOE_trainer.recorders = [DBCaseRecorder(':memory:')]
EI_opt = self.parent.add("EI_opt",Genetic())
EI_opt.opt_type = "maximize"
EI_opt.population_size = 100
EI_opt.generations = 10
#EI_opt.selection_method = "tournament"
for name,param in self.parent.get_parameters().iteritems():
EI_opt.add_parameter(param)
EI_opt.add_objective("EI.%s"%self.EI_PI)
retrain = self.parent.add("retrain",Driver())
retrain.recorders = self.data_recorders
retrain.add_event("%s.train_next"%self.comp_name)
iter = self.parent.add("iter",IterateUntil())
iter.max_iterations = self.sample_iterations
iter.add_stop_condition('EI.PI <= %s'%self.min_ei_pi)
#Data Connections
self.parent.connect("filter.pareto_set","EI.best_case")
self.parent.connect(self.objective,"EI.predicted_value")
#Iteration Heirarchy
self.parent.driver.workflow.add(['DOE_trainer', 'iter'])
#DOE_trainer.workflow.add(self.comp_name)
iter.workflow = SequentialWorkflow()
iter.workflow.add(['filter', 'EI_opt', 'retrain'])
#EI_opt.workflow.add([self.comp_name,'EI'])
retrain.workflow.add(self.comp_name)
def execute(self):
Driver.execute(self)
def configure(self):
""" Set it all up. """
# Place all design variables on the Assembly boundary.
self.add('S', Float(0.0, iotype='in', desc='Wing Area'))
self.add('ac_w',
Float(0.0, iotype='in', desc='Weight of aircraft + payload'))
self.add('SFCSL', Float(0.0, iotype='in', desc='sea-level SFC value'))
self.add('thrust_sl',
Float(0.0, iotype='in', desc='Maximum sea-level thrust'))
self.add('AR', Float(0.0, iotype='in', desc='Aspect Ratio'))
self.add('oswald', Float(0.0, iotype='in', desc="Oswald's efficiency"))
# Splines
self.add(
'SysXBspline',
SysXBspline(num_elem=self.num_elem,
num_pt=self.num_pt,
x_init=self.x_pts,
jac_h=self.jac_h))
self.SysXBspline.x_pt = self.x_pts
self.add(
'SysHBspline',
SysHBspline(num_elem=self.num_elem,
num_pt=self.num_pt,
x_init=self.x_pts,
jac_h=self.jac_h))
self.add(
'SysMVBspline',
SysMVBspline(num_elem=self.num_elem,
num_pt=self.num_pt,
x_init=self.x_pts,
jac_h=self.jac_h))
self.add(
'SysGammaBspline',
SysGammaBspline(num_elem=self.num_elem,
num_pt=self.num_pt,
x_init=self.x_pts,
jac_gamma=self.jac_gamma))
# Atmospherics
self.add('SysSFC', SysSFC(num_elem=self.num_elem))
self.add('SysTemp', SysTemp(num_elem=self.num_elem))
self.add('SysRho', SysRho(num_elem=self.num_elem))
self.add('SysSpeed', SysSpeed(num_elem=self.num_elem))
self.connect('SFCSL', 'SysSFC.SFCSL')
self.connect('SysHBspline.h', 'SysSFC.h')
self.connect('SysHBspline.h', 'SysTemp.h')
self.connect('SysHBspline.h', 'SysRho.h')
self.connect('SysTemp.temp', 'SysRho.temp')
self.connect('SysTemp.temp', 'SysSpeed.temp')
self.connect('SysMVBspline.M', 'SysSpeed.M')
self.connect('SysMVBspline.v_spline', 'SysSpeed.v_spline')
# -----------------------------------
# Comps for Coupled System begin here
# -----------------------------------
# Vertical Equilibrium
self.add('SysCLTar', SysCLTar(num_elem=self.num_elem))
self.connect('S', 'SysCLTar.S')
self.connect('ac_w', 'SysCLTar.ac_w')
self.connect('SysRho.rho', 'SysCLTar.rho')
self.connect('SysGammaBspline.Gamma', 'SysCLTar.Gamma')
self.connect('SysSpeed.v', 'SysCLTar.v')
# Tripan Alpha
self.add(
'SysTripanCLSurrogate',
SysTripanCLSurrogate(num_elem=self.num_elem,
num=self.num,
CL=self.CL_arr))
self.connect('SysMVBspline.M', 'SysTripanCLSurrogate.M')
self.connect('SysHBspline.h', 'SysTripanCLSurrogate.h')
self.connect('SysCLTar.CL', 'SysTripanCLSurrogate.CL_tar')
# Tripan Eta
self.add(
'SysTripanCMSurrogate',
SysTripanCMSurrogate(num_elem=self.num_elem,
num=self.num,
CM=self.CM_arr))
self.connect('SysMVBspline.M', 'SysTripanCMSurrogate.M')
self.connect('SysHBspline.h', 'SysTripanCMSurrogate.h')
self.connect('SysTripanCLSurrogate.alpha',
'SysTripanCMSurrogate.alpha')
# Tripan Drag
self.add(
'SysTripanCDSurrogate',
SysTripanCDSurrogate(num_elem=self.num_elem,
num=self.num,
CD=self.CD_arr))
self.connect('SysMVBspline.M', 'SysTripanCDSurrogate.M')
self.connect('SysHBspline.h', 'SysTripanCDSurrogate.h')
self.connect('SysTripanCMSurrogate.eta', 'SysTripanCDSurrogate.eta')
self.connect('SysTripanCLSurrogate.alpha',
'SysTripanCDSurrogate.alpha')
# Horizontal Equilibrium
self.add('SysCTTar', SysCTTar(num_elem=self.num_elem))
self.connect('SysGammaBspline.Gamma', 'SysCTTar.Gamma')
self.connect('SysTripanCDSurrogate.CD', 'SysCTTar.CD')
self.connect('SysTripanCLSurrogate.alpha', 'SysCTTar.alpha')
self.connect('SysRho.rho', 'SysCTTar.rho')
self.connect('SysSpeed.v', 'SysCTTar.v')
self.connect('S', 'SysCTTar.S')
self.connect('ac_w', 'SysCTTar.ac_w')
# Weight
self.add('SysFuelWeight', SysFuelWeight(num_elem=self.num_elem))
self.SysFuelWeight.fuel_w = np.linspace(1.0, 0.0, self.num_elem + 1)
self.connect('SysSpeed.v', 'SysFuelWeight.v')
self.connect('SysGammaBspline.Gamma', 'SysFuelWeight.Gamma')
self.connect('SysCTTar.CT_tar', 'SysFuelWeight.CT_tar')
self.connect('SysXBspline.x', 'SysFuelWeight.x')
self.connect('SysSFC.SFC', 'SysFuelWeight.SFC')
self.connect('SysRho.rho', 'SysFuelWeight.rho')
self.connect('S', 'SysFuelWeight.S')
# ------------------------------------------------
# Coupled Analysis - Newton for outer loop
# TODO: replace with GS/Newton cascaded solvers when working
# -----------------------------------------------
self.add('coupled_solver', NewtonSolver())
# Direct connections (cycles) are faster.
self.connect('SysFuelWeight.fuel_w', 'SysCLTar.fuel_w')
self.connect('SysCTTar.CT_tar', 'SysCLTar.CT_tar')
self.connect('SysTripanCLSurrogate.alpha', 'SysCLTar.alpha')
self.connect('SysTripanCMSurrogate.eta', 'SysTripanCLSurrogate.eta')
self.connect('SysFuelWeight.fuel_w', 'SysCTTar.fuel_w')
#self.coupled_solver.add_parameter('SysCLTar.fuel_w')
#self.coupled_solver.add_constraint('SysFuelWeight.fuel_w = SysCLTar.fuel_w')
#self.coupled_solver.add_parameter('SysCLTar.CT_tar')
#self.coupled_solver.add_constraint('SysCTTar.CT_tar = SysCLTar.CT_tar')
#self.coupled_solver.add_parameter('SysCLTar.alpha')
#self.coupled_solver.add_constraint('SysTripanCLSurrogate.alpha = SysCLTar.alpha')
#self.coupled_solver.add_parameter('SysTripanCLSurrogate.eta')
#self.coupled_solver.add_constraint('SysTripanCMSurrogate.eta = SysTripanCLSurrogate.eta')
#self.coupled_solver.add_parameter('SysCTTar.fuel_w')
#self.coupled_solver.add_constraint('SysFuelWeight.fuel_w = SysCTTar.fuel_w')
# (Implicit comps)
self.coupled_solver.add_parameter('SysTripanCLSurrogate.alpha')
self.coupled_solver.add_constraint(
'SysTripanCLSurrogate.alpha_res = 0')
self.coupled_solver.add_parameter('SysTripanCMSurrogate.eta')
self.coupled_solver.add_constraint('SysTripanCMSurrogate.CM = 0')
# --------------------
# Downstream of solver
# --------------------
# Functionals (i.e., components downstream of the coupled system.)
self.add('SysTau', SysTau(num_elem=self.num_elem))
self.add('SysTmin', SysTmin(num_elem=self.num_elem))
self.add('SysTmax', SysTmax(num_elem=self.num_elem))
#self.add('SysSlopeMin', SysSlopeMin(num_elem=self.num_elem))
#self.add('SysSlopeMax', SysSlopeMax(num_elem=self.num_elem))
self.add('SysFuelObj', SysFuelObj(num_elem=self.num_elem))
self.add('SysHi', SysHi(num_elem=self.num_elem))
self.add('SysHf', SysHf(num_elem=self.num_elem))
self.add('SysMi', SysMi(num_elem=self.num_elem))
self.add('SysMf', SysMf(num_elem=self.num_elem))
self.add('SysBlockTime', SysBlockTime(num_elem=self.num_elem))
self.connect('S', 'SysTau.S')
self.connect('thrust_sl', 'SysTau.thrust_sl')
self.connect('SysRho.rho', 'SysTau.rho')
self.connect('SysCTTar.CT_tar', 'SysTau.CT_tar')
self.connect('SysHBspline.h', 'SysTau.h')
self.connect('SysSpeed.v', 'SysTau.v')
self.connect('SysTau.tau', 'SysTmin.tau')
self.connect('SysTau.tau', 'SysTmax.tau')
#self.connect('SysGammaBspline.Gamma', 'SysSlopeMin.Gamma')
#self.connect('SysGammaBspline.Gamma', 'SysSlopeMax.Gamma')
self.connect('SysFuelWeight.fuel_w', 'SysFuelObj.fuel_w')
self.connect('SysHBspline.h', 'SysHi.h')
self.connect('SysHBspline.h', 'SysHf.h')
self.connect('SysMVBspline.M', 'SysMi.M')
self.connect('SysMVBspline.M', 'SysMf.M')
self.connect('SysXBspline.x', 'SysBlockTime.x')
self.connect('SysSpeed.v', 'SysBlockTime.v')
self.connect('SysGammaBspline.Gamma', 'SysBlockTime.Gamma')
# Promote useful variables to the boundary.
self.create_passthrough('SysHBspline.h_pt')
self.connect('h_pt', 'SysGammaBspline.h_pt')
self.create_passthrough('SysMVBspline.v_pt')
self.create_passthrough('SysMVBspline.M_pt')
self.create_passthrough('SysTmin.Tmin')
self.create_passthrough('SysTmax.Tmax')
self.create_passthrough('SysFuelObj.fuelburn')
self.create_passthrough('SysHi.h_i')
self.create_passthrough('SysHf.h_f')
#-------------------------
# Iteration Hierarchy
#-------------------------
#self.driver.workflow.add(['SysXBspline', 'SysHBspline',
#'SysMVBspline', 'SysGammaBspline',
#'SysSFC', 'SysTemp', 'SysRho', 'SysSpeed',
#'coupled_solver',
#'SysTau', 'SysTmin', 'SysTmax',
#'SysFuelObj', 'SysHi', 'SysHf', 'SysMi', 'SysMf', 'SysBlockTime'])
self.add('bsplines', Driver())
self.add('atmosphere', Driver())
self.add('SysCLTar_Sub', Driver())
self.add('SysTripanCLSurrogate_Sub', Driver())
self.add('SysTripanCMSurrogate_Sub', Driver())
self.add('SysTripanCDSurrogate_Sub', Driver())
self.add('SysCTTar_Sub', Driver())
self.add('SysFuelWeight_Sub', Driver())
self.driver.workflow.add([
'bsplines', 'atmosphere', 'coupled_solver', 'SysTau', 'SysTmin',
'SysTmax', 'SysFuelObj', 'SysHi', 'SysHf', 'SysMi', 'SysMf',
'SysBlockTime'
])
self.bsplines.workflow.add(
['SysXBspline', 'SysHBspline', 'SysMVBspline', 'SysGammaBspline'])
self.atmosphere.workflow.add(
['SysSFC', 'SysTemp', 'SysRho', 'SysSpeed'])
self.coupled_solver.workflow.add([
'SysCLTar_Sub', 'SysTripanCLSurrogate_Sub',
'SysTripanCMSurrogate_Sub', 'SysTripanCDSurrogate_Sub',
'SysCTTar_Sub', 'SysFuelWeight_Sub'
])
self.SysCLTar_Sub.workflow.add(['SysCLTar'])
self.SysTripanCLSurrogate_Sub.workflow.add(['SysTripanCLSurrogate'])
self.SysTripanCMSurrogate_Sub.workflow.add(['SysTripanCMSurrogate'])
self.SysTripanCDSurrogate_Sub.workflow.add(['SysTripanCDSurrogate'])
self.SysCTTar_Sub.workflow.add(['SysCTTar'])
self.SysFuelWeight_Sub.workflow.add(['SysFuelWeight'])
#-------------------------
# Driver Settings
#-------------------------
self.driver.gradient_options.lin_solver = "linear_gs"
self.driver.gradient_options.maxiter = 1
self.driver.gradient_options.derivative_direction = 'adjoint'
self.coupled_solver.atol = 1e-9
self.coupled_solver.rtol = 1e-9
self.coupled_solver.max_iteration = 15
self.coupled_solver.gradient_options.atol = 1e-14
self.coupled_solver.gradient_options.rtol = 1e-20
self.coupled_solver.gradient_options.maxiter = 50
self.coupled_solver.iprint = 1
def execute(self):
Driver.execute(self)
msg = "Component 'sub.comp' is not in the scope of the top assembly."
self.assertEqual(str(err), msg)
else:
self.fail('Expected AttributeError')
# Test 3, add a comp that does not exist
try:
self.model.driver.workflow.add('stuff', check=True)
except AttributeError, err:
msg = "Component 'stuff' does not exist in the top assembly."
self.assertEqual(str(err), msg)
else:
self.fail('Expected AttributeError')
# Test 4, create a driver recursion loop
self.model.add('driver2', Driver())
self.model.driver.workflow.add('driver2', check=True)
try:
self.model.driver2.workflow.add('driver', check=True)
except AttributeError, err:
msg = "Driver recursion loop detected"
self.assertEqual(str(err), msg)
else:
self.fail('Expected AttributeError')
if __name__ == '__main__':
import nose
import sys
sys.argv.append('--cover-package=openmdao')
sys.argv.append('--cover-erase')
def test_get_entry_group(self):
self.assertEqual(_get_entry_group(Driver()), 'openmdao.driver')