def test_options(self):
"""Verify that the SciPy solver specific options are declared."""
group = Group()
group.linear_solver = ScipyIterativeSolver()
assert (group.linear_solver.options['solver'] == gmres)
def test_const_jacobian(self):
model = Group()
comp = IndepVarComp()
for name, val in (('x', 1.), ('y1', np.ones(2)), ('y2', np.ones(2)),
('y3', np.ones(2)), ('z', np.ones((2, 2)))):
comp.add_output(name, val)
model.add_subsystem('input_comp', comp, promotes=['x', 'y1', 'y2', 'y3', 'z'])
problem = Problem(model=model)
problem.set_solver_print(level=0)
model.linear_solver = ScipyKrylov()
model.jacobian = COOJacobian()
model.add_subsystem('simple', SimpleCompConst(),
promotes=['x', 'y1', 'y2', 'y3', 'z', 'f', 'g'])
problem.setup(check=False)
problem.run_model()
totals = problem.compute_totals(['f', 'g'],
['x', 'y1', 'y2', 'y3', 'z'])
jacobian = {}
jacobian['f', 'x'] = [[1.]]
jacobian['f', 'z'] = np.ones((1, 4))
jacobian['f', 'y1'] = np.zeros((1, 2))
jacobian['f', 'y2'] = np.zeros((1, 2))
jacobian['f', 'y3'] = np.zeros((1, 2))
jacobian['g', 'y1'] = [[1, 0], [1, 0], [0, 1], [0, 1]]
jacobian['g', 'y2'] = [[1, 0], [0, 1], [1, 0], [0, 1]]
jacobian['g', 'y3'] = [[1, 0], [1, 0], [0, 1], [0, 1]]
jacobian['g', 'x'] = [[1], [0], [0], [1]]
jacobian['g', 'z'] = np.zeros((4, 4))
assert_rel_error(self, totals, jacobian)
def test_options(self):
"""Verify that the PetscKSP specific options are declared."""
group = Group()
group.linear_solver = PetscKSP()
assert (group.linear_solver.options['ksp_type'] == 'fgmres')
def test_shape(self):
n = 100
bal = BalanceComp()
bal.add_balance('x', shape=(n,))
tgt = IndepVarComp(name='y_tgt', val=4*np.ones(n))
exe = ExecComp('y=x**2', x=np.zeros(n), y=np.zeros(n))
model = Group()
model.add_subsystem('tgt', tgt, promotes_outputs=['y_tgt'])
model.add_subsystem('exe', exe)
model.add_subsystem('bal', bal)
model.connect('y_tgt', 'bal.rhs:x')
model.connect('bal.x', 'exe.x')
model.connect('exe.y', 'bal.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
prob['bal.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['bal.x'], 2.0*np.ones(n), decimal=7)
def test_const_jacobian(self):
model = Group()
comp = IndepVarComp()
for name, val in (('x', 1.), ('y1', np.ones(2)), ('y2', np.ones(2)),
('y3', np.ones(2)), ('z', np.ones((2, 2)))):
comp.add_output(name, val)
model.add_subsystem('input_comp', comp, promotes=['x', 'y1', 'y2', 'y3', 'z'])
problem = Problem(model=model)
problem.set_solver_print(level=0)
model.linear_solver = ScipyKrylov()
model.jacobian = COOJacobian()
model.add_subsystem('simple', SimpleCompConst(),
promotes=['x', 'y1', 'y2', 'y3', 'z', 'f', 'g'])
problem.setup(check=False)
problem.run_model()
totals = problem.compute_totals(['f', 'g'],
['x', 'y1', 'y2', 'y3', 'z'])
jacobian = {}
jacobian['f', 'x'] = [[1.]]
jacobian['f', 'z'] = np.ones((1, 4))
jacobian['f', 'y1'] = np.zeros((1, 2))
jacobian['f', 'y2'] = np.zeros((1, 2))
jacobian['f', 'y3'] = np.zeros((1, 2))
jacobian['g', 'y1'] = [[1, 0], [1, 0], [0, 1], [0, 1]]
jacobian['g', 'y2'] = [[1, 0], [0, 1], [1, 0], [0, 1]]
jacobian['g', 'y3'] = [[1, 0], [1, 0], [0, 1], [0, 1]]
jacobian['g', 'x'] = [[1], [0], [0], [1]]
jacobian['g', 'z'] = np.zeros((4, 4))
assert_rel_error(self, totals, jacobian)
def test_const_jacobian(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, DirectSolver, DenseJacobian
from openmdao.jacobians.tests.test_jacobian_features import SimpleCompConst
model = Group()
comp = IndepVarComp()
for name, val in (('x', 1.), ('y1', np.ones(2)), ('y2', np.ones(2)),
('y3', np.ones(2)), ('z', np.ones((2, 2)))):
comp.add_output(name, val)
model.add_subsystem('input_comp', comp, promotes=['x', 'y1', 'y2', 'y3', 'z'])
problem = Problem(model=model)
model.suppress_solver_output = True
model.linear_solver = DirectSolver()
model.jacobian = DenseJacobian()
model.add_subsystem('simple', SimpleCompConst(),
promotes=['x', 'y1', 'y2', 'y3', 'z', 'f', 'g'])
problem.setup(check=False)
problem.run_model()
totals = problem.compute_totals(['f', 'g'],
['x', 'y1', 'y2', 'y3', 'z'])
assert_rel_error(self, totals['f', 'x'], [[1.]])
assert_rel_error(self, totals['f', 'z'], np.ones((1, 4)))
assert_rel_error(self, totals['f', 'y1'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y2'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y3'], np.zeros((1, 2)))
assert_rel_error(self, totals['g', 'x'], [[1], [0], [0], [1]])
assert_rel_error(self, totals['g', 'z'], np.zeros((4, 4)))
assert_rel_error(self, totals['g', 'y1'], [[1, 0], [1, 0], [0, 1], [0, 1]])
assert_rel_error(self, totals['g', 'y2'], [[1, 0], [0, 1], [1, 0], [0, 1]])
assert_rel_error(self, totals['g', 'y3'], [[1, 0], [1, 0], [0, 1], [0, 1]])
def test_shape(self):
n = 100
bal = BalanceComp()
bal.add_balance('x', shape=(n, ))
tgt = IndepVarComp(name='y_tgt', val=4 * np.ones(n))
exe = ExecComp('y=x**2', x=np.zeros(n), y=np.zeros(n))
model = Group()
model.add_subsystem('tgt', tgt, promotes_outputs=['y_tgt'])
model.add_subsystem('exe', exe)
model.add_subsystem('bal', bal)
model.connect('y_tgt', 'bal.rhs:x')
model.connect('bal.x', 'exe.x')
model.connect('exe.y', 'bal.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
prob['bal.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['bal.x'], 2.0 * np.ones(n), decimal=7)
def test_options(self):
"""Verify that the PETScKrylov specific options are declared."""
group = Group()
group.linear_solver = PETScKrylov()
assert(group.linear_solver.options['ksp_type'] == 'fgmres')
def test_const_jacobian(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, DirectSolver
from openmdao.jacobians.tests.test_jacobian_features import SimpleCompConst
model = Group(assembled_jac_type='dense')
comp = IndepVarComp()
for name, val in (('x', 1.), ('y1', np.ones(2)), ('y2', np.ones(2)),
('y3', np.ones(2)), ('z', np.ones((2, 2)))):
comp.add_output(name, val)
model.add_subsystem('input_comp', comp, promotes=['x', 'y1', 'y2', 'y3', 'z'])
problem = Problem(model=model)
problem.set_solver_print(0)
model.linear_solver = DirectSolver(assemble_jac=True)
model.add_subsystem('simple', SimpleCompConst(),
promotes=['x', 'y1', 'y2', 'y3', 'z', 'f', 'g'])
problem.setup(check=False)
problem.run_model()
totals = problem.compute_totals(['f', 'g'],
['x', 'y1', 'y2', 'y3', 'z'])
assert_rel_error(self, totals['f', 'x'], [[1.]])
assert_rel_error(self, totals['f', 'z'], np.ones((1, 4)))
assert_rel_error(self, totals['f', 'y1'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y2'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y3'], np.zeros((1, 2)))
assert_rel_error(self, totals['g', 'z'], np.zeros((4, 4)))
assert_rel_error(self, totals['g', 'y1'], [[1, 0], [1, 0], [0, 1], [0, 1]])
assert_rel_error(self, totals['g', 'y2'], [[1, 0], [0, 1], [1, 0], [0, 1]])
assert_rel_error(self, totals['g', 'y3'], [[1, 0], [1, 0], [0, 1], [0, 1]])
assert_rel_error(self, totals['g', 'x'], [[1], [0], [0], [1]])
def test_const_jacobian(self):
model = Group()
comp = IndepVarComp()
for name, val in (('x', 1.), ('y1', np.ones(2)), ('y2', np.ones(2)),
('y3', np.ones(2)), ('z', np.ones((2, 2)))):
comp.add_output(name, val)
model.add_subsystem('input_comp',
comp,
promotes=['x', 'y1', 'y2', 'y3', 'z'])
problem = Problem(model=model)
model.suppress_solver_output = True
model.linear_solver = DirectSolver()
model.jacobian = DenseJacobian()
model.add_subsystem('simple',
SimpleCompConst(),
promotes=['x', 'y1', 'y2', 'y3', 'z', 'f', 'g'])
problem.setup(check=False)
problem.run_model()
totals = problem.compute_totals(['f', 'g'],
['x', 'y1', 'y2', 'y3', 'z'])
assert_rel_error(self, totals['f', 'x'], [[1.]])
assert_rel_error(self, totals['f', 'z'], np.ones((1, 4)))
assert_rel_error(self, totals['f', 'y1'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y2'], np.zeros((1, 2)))
assert_rel_error(self, totals['f', 'y3'], np.zeros((1, 2)))
assert_rel_error(self, totals['g', 'x'], [[1], [0], [0], [1]])
assert_rel_error(self, totals['g', 'z'], np.zeros((4, 4)))
assert_rel_error(self, totals['g', 'y1'],
[[1, 0], [1, 0], [0, 1], [0, 1]])
assert_rel_error(self, totals['g', 'y2'],
[[1, 0], [0, 1], [1, 0], [0, 1]])
assert_rel_error(self, totals['g', 'y3'],
[[1, 0], [1, 0], [0, 1], [0, 1]])
def test_options(self):
"""Verify that the SciPy solver specific options are declared."""
group = Group()
group.linear_solver = self.linear_solver_class()
self.assertEqual(group.linear_solver.options['solver'], self.linear_solver_name)
def test_scalar_with_guess_func_additional_input(self):
model = Group(assembled_jac_type='dense')
bal = BalanceComp()
bal.add_balance('x')
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['y_tgt', 'guess_x'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('guess_x', 'balance.guess_x')
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
# run problem without a guess function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_no_guess = model.nonlinear_solver._iter_count
# run problem with same initial value and a guess function
def guess_function(inputs, outputs, resids):
outputs['x'] = inputs['guess_x']
bal.options['guess_func'] = guess_function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_with_guess = model.nonlinear_solver._iter_count
# verify it converges faster with the guess function
self.assertTrue(iters_with_guess < iters_no_guess)
def setup(self):
#Design variables
self.add_subsystem('z_ini',
IndepVarComp(
'z', np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])),
promotes=['*'])
self.add_subsystem('x_aer_ini',
IndepVarComp('x_aer', 1.0),
promotes=['*'])
self.add_subsystem('x_str_ini',
IndepVarComp('x_str', np.array([1.0, 1.0])),
promotes=['*'])
self.add_subsystem('x_pro_ini',
IndepVarComp('x_pro', 1.0),
promotes=['*'])
#Disciplines
sap_group = Group()
sap_group.add_subsystem('Structure',
Structure(self.scalers),
promotes=['*'])
sap_group.add_subsystem('Aerodynamics',
Aerodynamics(self.scalers),
promotes=['*'])
sap_group.add_subsystem('Propulsion',
Propulsion(self.scalers),
promotes=['*'])
sap_group.nonlinear_solver = NonlinearBlockGS()
sap_group.nonlinear_solver.options['atol'] = 1.0e-3
sap_group.linear_solver = ScipyKrylov()
self.add_subsystem('Mda', sap_group, promotes=['*'])
self.add_subsystem('Performance',
Performance(self.scalers),
promotes=['*'])
#Constraints
cstrs = [
'con_theta_up = Theta*' + str(self.scalers['Theta']) + '-1.04',
'con_theta_low = 0.96-Theta*' + str(self.scalers['Theta']),
'con_dpdx = dpdx*' + str(self.scalers['dpdx']) + '-1.04',
'con1_esf = ESF*' + str(self.scalers['ESF']) + '-1.5',
'con2_esf = 0.5-ESF*' + str(self.scalers['ESF']),
'con_temp = Temp*' + str(self.scalers['Temp']) + '-1.02',
'con_dt=DT'
]
for i in range(5):
cstrs.append('con_sigma' + str(i + 1) + ' = sigma[' + str(i) +
']*' + str(self.scalers['sigma'][i]) + '-1.09')
self.add_subsystem('Constraints',
ExecComp(cstrs, sigma=np.zeros(5)),
promotes=['*'])
def test_scalar_with_guess_func_additional_input(self):
model = Group(assembled_jac_type='dense')
bal = BalanceComp()
bal.add_balance('x')
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['y_tgt', 'guess_x'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('guess_x', 'balance.guess_x')
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
# run problem without a guess function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_no_guess = model.nonlinear_solver._iter_count
# run problem with same initial value and a guess function
def guess_function(inputs, outputs, resids):
outputs['x'] = inputs['guess_x']
bal.options['guess_func'] = guess_function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_with_guess = model.nonlinear_solver._iter_count
# verify it converges faster with the guess function
self.assertTrue(iters_with_guess < iters_no_guess)
def coupled_group(self):
# type: () -> Optional[Group]
""":obj:`Group`, optional: Group wrapping the coupled blocks with a converger specified in the CMDOWS file.
If no coupled blocks are specified in the CMDOWS file this property is `None`.
"""
if self.coupled_blocks:
coupled_group = Group()
for uid in self.coupled_blocks:
# Get the correct DisciplineComponent
discipline_component = self.discipline_components[uid]
# Change input variable names if they are provided as copies of coupling variables
promotes = ['*'] # type: List[Union[str, Tuple[str, str]]]
if not self.has_converger:
for i in discipline_component.inputs_from_xml.keys():
if i in self.coupling_vars:
promotes.append((i, self.coupling_vars[i]['copy']))
# Add the DisciplineComponent to the group
coupled_group.add_subsystem(uid, self.discipline_components[uid], promotes)
# Find the convergence type of the coupled group
if self.has_converger:
conv_type = self.elem_problem_def.find('problemFormulation/convergerType').text
if conv_type == 'Gauss-Seidel':
coupled_group.linear_solver = LinearBlockGS()
coupled_group.nonlinear_solver = NonlinearBlockGS()
elif conv_type == 'Jacobi':
coupled_group.linear_solver = LinearBlockJac()
coupled_group.nonlinear_solver = NonlinearBlockJac()
else:
raise RuntimeError('Specified convergerType "%s" is not supported.' % conv_type)
else:
coupled_group.linear_solver = LinearRunOnce()
coupled_group.nonlinear_solver = NonLinearRunOnce()
return coupled_group
return None
def test_scalar_guess_func_using_outputs(self):
# Implicitly solve -(ax^2 + bx) = c using a BalanceComp.
# For a=1, b=-4 and c=3, there are solutions at x=1 and x=3.
# Verify that we can set the guess value (and target a solution) based on outputs.
ind = IndepVarComp()
ind.add_output('a', 1)
ind.add_output('b', -4)
ind.add_output('c', 3)
lhs = ExecComp('lhs=-(a*x**2+b*x)')
bal = BalanceComp()
def guess_function(inputs, outputs, residuals):
if outputs['x'] < 0:
return 5.
else:
return 0.
bal.add_balance(name='x', rhs_name='c', guess_func=guess_function)
model = Group()
model.add_subsystem('ind_comp', ind, promotes_outputs=['a', 'b', 'c'])
model.add_subsystem('lhs_comp', lhs, promotes_inputs=['a', 'b', 'x'])
model.add_subsystem('bal_comp', bal, promotes_inputs=['c'], promotes_outputs=['x'])
model.connect('lhs_comp.lhs', 'bal_comp.lhs:x')
model.linear_solver = DirectSolver()
model.nonlinear_solver = NewtonSolver(maxiter=1000, iprint=0)
prob = Problem(model)
prob.setup()
# initial value of 'x' less than zero, guess should steer us to solution of 3.
prob['bal_comp.x'] = -1
prob.run_model()
assert_almost_equal(prob['bal_comp.x'], 3.0, decimal=7)
# initial value of 'x' greater than zero, guess should steer us to solution of 1.
prob['bal_comp.x'] = 99
prob.run_model()
assert_almost_equal(prob['bal_comp.x'], 1.0, decimal=7)
def test_scalar_with_guess_func(self):
n = 1
model = Group(assembled_jac_type='dense')
def guess_function(inputs, outputs, residuals):
outputs['x'] = np.sqrt(inputs['rhs:x'])
bal = BalanceComp(
'x', guess_func=guess_function) # test guess_func as kwarg
tgt = IndepVarComp(name='y_tgt', val=4)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='target',
subsys=tgt,
promotes_outputs=['y_tgt'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
prob['balance.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
# should converge with no iteration due to the guess function
self.assertEqual(model.nonlinear_solver._iter_count, 1)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['balance']:
assert_almost_equal(cpd['balance'][of, wrt]['abs error'],
0.0,
decimal=5)
def test_guess_nonlinear(self):
class ImpWithInitial(QuadraticLinearize):
def solve_nonlinear(self, inputs, outputs):
""" Do nothing. """
pass
def guess_nonlinear(self, inputs, outputs, resids):
# Solution at x=1 and x=3. Default value takes us to the x=1 solution. Here
# we set it to a value that will take us to the x=3 solution.
outputs['x'] = 5.0
group = Group()
group.add_subsystem('pa', IndepVarComp('a', 1.0))
group.add_subsystem('pb', IndepVarComp('b', 1.0))
group.add_subsystem('pc', IndepVarComp('c', 1.0))
group.add_subsystem('comp2', ImpWithInitial())
group.connect('pa.a', 'comp2.a')
group.connect('pb.b', 'comp2.b')
group.connect('pc.c', 'comp2.c')
prob = Problem(model=group)
group.nonlinear_solver = NewtonSolver()
group.nonlinear_solver.options['solve_subsystems'] = True
group.nonlinear_solver.options['max_sub_solves'] = 1
group.linear_solver = ScipyIterativeSolver()
prob.setup(check=False)
prob['pa.a'] = 1.
prob['pb.b'] = -4.
prob['pc.c'] = 3.
# Making sure that guess_nonlinear is called early enough to eradicate this.
prob['comp2.x'] = np.NaN
prob.run_model()
assert_rel_error(self, prob['comp2.x'], 3.)
def test_guess_nonlinear(self):
class ImpWithInitial(QuadraticLinearize):
def solve_nonlinear(self, inputs, outputs):
""" Do nothing. """
pass
def guess_nonlinear(self, inputs, outputs, resids):
# Solution at x=1 and x=3. Default value takes us to the x=1 solution. Here
# we set it to a value that will take us to the x=3 solution.
outputs['x'] = 5.0
group = Group()
group.add_subsystem('pa', IndepVarComp('a', 1.0))
group.add_subsystem('pb', IndepVarComp('b', 1.0))
group.add_subsystem('pc', IndepVarComp('c', 1.0))
group.add_subsystem('comp2', ImpWithInitial())
group.connect('pa.a', 'comp2.a')
group.connect('pb.b', 'comp2.b')
group.connect('pc.c', 'comp2.c')
prob = Problem(model=group)
group.nonlinear_solver = NewtonSolver()
group.nonlinear_solver.options['solve_subsystems'] = True
group.nonlinear_solver.options['max_sub_solves'] = 1
group.linear_solver = ScipyKrylov()
prob.setup(check=False)
prob['pa.a'] = 1.
prob['pb.b'] = -4.
prob['pc.c'] = 3.
# Making sure that guess_nonlinear is called early enough to eradicate this.
prob['comp2.x'] = np.NaN
prob.run_model()
assert_rel_error(self, prob['comp2.x'], 3.)
def test_scalar_with_guess_func(self):
n = 1
model=Group(assembled_jac_type='dense')
def guess_function(inputs, outputs, residuals):
outputs['x'] = np.sqrt(inputs['rhs:x'])
bal = BalanceComp('x', guess_func=guess_function) # test guess_func as kwarg
tgt = IndepVarComp(name='y_tgt', val=4)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='target', subsys=tgt, promotes_outputs=['y_tgt'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
prob['balance.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
# should converge with no iteration due to the guess function
self.assertEqual(model.nonlinear_solver._iter_count, 1)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['balance']:
assert_almost_equal(cpd['balance'][of, wrt]['abs error'], 0.0, decimal=5)
def test_no_promotion_errors(self):
"""
Tests for error-handling for invalid variable names and keys.
"""
g = Group(assembled_jac_type='dense')
g.linear_solver = DirectSolver(assemble_jac=True)
g.add_subsystem('c', ExecComp('y=2*x'))
p = Problem()
model = p.model
model.add_subsystem('g', g)
p.setup()
# -------------------------------------------------------------------
msg = 'Variable name "{}" not found.'
# inputs
with assertRaisesRegex(self, KeyError, msg.format('x')):
p['x'] = 5.0
p.final_setup()
p._initial_condition_cache = {}
with assertRaisesRegex(self, KeyError, msg.format('x')):
p['x']
# outputs
with assertRaisesRegex(self, KeyError, msg.format('y')):
p['y'] = 5.0
p.final_setup()
p._initial_condition_cache = {}
with assertRaisesRegex(self, KeyError, msg.format('y')):
p['y']
msg = 'Variable name "{}" not found.'
inputs, outputs, residuals = g.get_nonlinear_vectors()
# inputs
with assertRaisesRegex(self, KeyError, msg.format('x')):
inputs['x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('x')):
inputs['x']
with assertRaisesRegex(self, KeyError, msg.format('g.c.x')):
inputs['g.c.x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('g.c.x')):
inputs['g.c.x']
# outputs
with assertRaisesRegex(self, KeyError, msg.format('y')):
outputs['y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y')):
outputs['y']
with assertRaisesRegex(self, KeyError, msg.format('g.c.y')):
outputs['g.c.y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('g.c.y')):
outputs['g.c.y']
msg = r'Variable name pair \("{}", "{}"\) not found.'
jac = g.linear_solver._assembled_jac
# d(output)/d(input)
with assertRaisesRegex(self, KeyError, msg.format('y', 'x')):
jac['y', 'x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y', 'x')):
jac['y', 'x']
# allow absolute keys now
# with assertRaisesRegex(self, KeyError, msg.format('g.c.y', 'g.c.x')):
# jac['g.c.y', 'g.c.x'] = 5.0
# with assertRaisesRegex(self, KeyError, msg.format('g.c.y', 'g.c.x')):
# deriv = jac['g.c.y', 'g.c.x']
# d(output)/d(output)
with assertRaisesRegex(self, KeyError, msg.format('y', 'y')):
jac['y', 'y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y', 'y')):
jac['y', 'y']
def test_nested_promotion_errors(self):
"""
Tests for error-handling for promoted input variable names.
"""
c1 = IndepVarComp('x')
c2 = ExecComp('y=2*x')
c3 = ExecComp('z=3*x')
g = Group(assembled_jac_type='dense')
g.add_subsystem('c2', c2, promotes=['*'])
g.add_subsystem('c3', c3, promotes=['*'])
g.linear_solver = DirectSolver(assemble_jac=True)
model = Group()
model.add_subsystem('c1', c1, promotes=['*'])
model.add_subsystem('g', g)
p = Problem(model)
p.setup()
# -------------------------------------------------------------------
msg1 = "The promoted name g.x is invalid because it refers to multiple inputs: " \
"[g.c2.x, g.c3.x] that are not connected to an output variable."
# inputs (g.x is not connected)
# with assertRaisesRegex(self, RuntimeError, msg1.format('g.x')):
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup()
p.final_setup()
# -------------------------------------------------------------------
# inputs (g.x is not connected)
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Start from a clean state again
p = Problem(model)
p.setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
msg2 = "The promoted name x is invalid because it refers to multiple inputs: " \
"[g.c2.x, g.c3.x] that are not connected to an output variable."
jac = g.linear_solver._assembled_jac
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
# -------------------------------------------------------------------
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup()
p.final_setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
# -------------------------------------------------------------------
msg1 = "The promoted name g.x is invalid because it refers to multiple inputs: " \
"[g.c2.x ,g.c3.x]. Access the value from the connected output variable x instead."
# From here, 'g.x' has a valid source.
model.connect('x', 'g.x')
p = Problem(model)
p.setup()
# inputs (g.x is connected to x)
p['g.x'] = 5.0
with self.assertRaises(Exception) as context:
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup()
p.final_setup()
# inputs (g.x is connected to x)
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
self.assertEqual(str(context.exception), msg1)
# Final test, the getitem
p = Problem(model)
p.setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'],
5.0) # Start from a clean state again
self.assertEqual(str(context.exception), msg2)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup()
p.final_setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
def test_with_promotion_errors(self):
"""
Tests for error-handling for invalid variable names and keys.
"""
c1 = IndepVarComp('x')
c2 = ExecComp('y=2*x')
c3 = ExecComp('z=3*x')
g = Group(assembled_jac_type='dense')
g.add_subsystem('c1', c1, promotes=['*'])
g.add_subsystem('c2', c2, promotes=['*'])
g.add_subsystem('c3', c3, promotes=['*'])
g.linear_solver = DirectSolver(assemble_jac=True)
model = Group()
model.add_subsystem('g', g, promotes=['*'])
p = Problem(model)
p.setup()
# Conclude setup but don't run model.
p.final_setup()
# -------------------------------------------------------------------
msg1 = 'Variable name "{}" not found.'
msg2 = "The promoted name x is invalid because it refers to multiple inputs: " \
"[g.c2.x ,g.c3.x]. Access the value from the connected output variable x instead."
inputs, outputs, residuals = g.get_nonlinear_vectors()
# inputs
with self.assertRaises(Exception) as context:
inputs['x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(inputs['x'], 5.0)
self.assertEqual(str(context.exception), msg2)
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.x')):
inputs['g.c2.x'] = 5.0
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.x')):
self.assertEqual(inputs['g.c2.x'], 5.0)
# outputs
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.y')):
outputs['g.c2.y'] = 5.0
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.y')):
self.assertEqual(outputs['g.c2.y'], 5.0)
msg1 = r'Variable name pair \("{}", "{}"\) not found.'
jac = g.linear_solver._assembled_jac
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
def test_no_promotion_errors(self):
"""
Tests for error-handling for invalid variable names and keys.
"""
g = Group(assembled_jac_type='dense')
g.linear_solver = DirectSolver(assemble_jac=True)
g.add_subsystem('c', ExecComp('y=2*x'))
p = Problem()
model = p.model
model.add_subsystem('g', g)
p.setup(check=False)
# -------------------------------------------------------------------
msg = 'Variable name "{}" not found.'
# inputs
with assertRaisesRegex(self, KeyError, msg.format('x')):
p['x'] = 5.0
p.final_setup()
p._initial_condition_cache = {}
with assertRaisesRegex(self, KeyError, msg.format('x')):
p['x']
# outputs
with assertRaisesRegex(self, KeyError, msg.format('y')):
p['y'] = 5.0
p.final_setup()
p._initial_condition_cache = {}
with assertRaisesRegex(self, KeyError, msg.format('y')):
p['y']
msg = 'Variable name "{}" not found.'
inputs, outputs, residuals = g.get_nonlinear_vectors()
# inputs
with assertRaisesRegex(self, KeyError, msg.format('x')):
inputs['x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('x')):
inputs['x']
with assertRaisesRegex(self, KeyError, msg.format('g.c.x')):
inputs['g.c.x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('g.c.x')):
inputs['g.c.x']
# outputs
with assertRaisesRegex(self, KeyError, msg.format('y')):
outputs['y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y')):
outputs['y']
with assertRaisesRegex(self, KeyError, msg.format('g.c.y')):
outputs['g.c.y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('g.c.y')):
outputs['g.c.y']
msg = r'Variable name pair \("{}", "{}"\) not found.'
jac = g.linear_solver._assembled_jac
# d(output)/d(input)
with assertRaisesRegex(self, KeyError, msg.format('y', 'x')):
jac['y', 'x'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y', 'x')):
jac['y', 'x']
# allow absolute keys now
# with assertRaisesRegex(self, KeyError, msg.format('g.c.y', 'g.c.x')):
# jac['g.c.y', 'g.c.x'] = 5.0
# with assertRaisesRegex(self, KeyError, msg.format('g.c.y', 'g.c.x')):
# deriv = jac['g.c.y', 'g.c.x']
# d(output)/d(output)
with assertRaisesRegex(self, KeyError, msg.format('y', 'y')):
jac['y', 'y'] = 5.0
with assertRaisesRegex(self, KeyError, msg.format('y', 'y')):
jac['y', 'y']
def test_with_promotion_errors(self):
"""
Tests for error-handling for invalid variable names and keys.
"""
c1 = IndepVarComp('x')
c2 = ExecComp('y=2*x')
c3 = ExecComp('z=3*x')
g = Group(assembled_jac_type='dense')
g.add_subsystem('c1', c1, promotes=['*'])
g.add_subsystem('c2', c2, promotes=['*'])
g.add_subsystem('c3', c3, promotes=['*'])
g.linear_solver = DirectSolver(assemble_jac=True)
model = Group()
model.add_subsystem('g', g, promotes=['*'])
p = Problem(model)
p.setup(check=False)
# Conclude setup but don't run model.
p.final_setup()
# -------------------------------------------------------------------
msg1 = 'Variable name "{}" not found.'
msg2 = "The promoted name x is invalid because it refers to multiple inputs: " \
"[g.c2.x ,g.c3.x]. Access the value from the connected output variable x instead."
inputs, outputs, residuals = g.get_nonlinear_vectors()
# inputs
with self.assertRaises(Exception) as context:
inputs['x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(inputs['x'], 5.0)
self.assertEqual(str(context.exception), msg2)
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.x')):
inputs['g.c2.x'] = 5.0
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.x')):
self.assertEqual(inputs['g.c2.x'], 5.0)
# outputs
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.y')):
outputs['g.c2.y'] = 5.0
with assertRaisesRegex(self, KeyError, msg1.format('g.c2.y')):
self.assertEqual(outputs['g.c2.y'], 5.0)
msg1 = r'Variable name pair \("{}", "{}"\) not found.'
jac = g.linear_solver._assembled_jac
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
def test_simple_external_code_implicit_comp(self):
from openmdao.api import Group, NewtonSolver, Problem, IndepVarComp, DirectSolver, \
ExternalCodeImplicitComp
class MachExternalCodeComp(ExternalCodeImplicitComp):
def initialize(self):
self.options.declare('super_sonic', types=bool)
def setup(self):
self.add_input('area_ratio', val=1.0, units=None)
self.add_output('mach', val=1., units=None)
self.declare_partials(of='mach', wrt='area_ratio', method='fd')
self.input_file = 'mach_input.dat'
self.output_file = 'mach_output.dat'
# providing these are optional; the component will verify that any input
# files exist before execution and that the output files exist after.
self.options['external_input_files'] = [self.input_file]
self.options['external_output_files'] = [self.output_file]
self.options['command_apply'] = [
'python', 'extcode_mach.py', self.input_file, self.output_file,
]
self.options['command_solve'] = [
'python', 'extcode_mach.py', self.input_file, self.output_file,
]
def apply_nonlinear(self, inputs, outputs, residuals):
with open(self.input_file, 'w') as input_file:
input_file.write('residuals\n')
input_file.write('{}\n'.format(inputs['area_ratio'][0]))
input_file.write('{}\n'.format(outputs['mach'][0]))
# the parent apply_nonlinear function actually runs the external code
super(MachExternalCodeComp, self).apply_nonlinear(inputs, outputs, residuals)
# parse the output file from the external code and set the value of mach
with open(self.output_file, 'r') as output_file:
mach = float(output_file.read())
residuals['mach'] = mach
def solve_nonlinear(self, inputs, outputs):
with open(self.input_file, 'w') as input_file:
input_file.write('outputs\n')
input_file.write('{}\n'.format(inputs['area_ratio'][0]))
input_file.write('{}\n'.format(self.options['super_sonic']))
# the parent apply_nonlinear function actually runs the external code
super(MachExternalCodeComp, self).solve_nonlinear(inputs, outputs)
# parse the output file from the external code and set the value of mach
with open(self.output_file, 'r') as output_file:
mach = float(output_file.read())
outputs['mach'] = mach
group = Group()
group.add_subsystem('ar', IndepVarComp('area_ratio', 0.5))
mach_comp = group.add_subsystem('comp', MachExternalCodeComp(), promotes=['*'])
prob = Problem(model=group)
group.nonlinear_solver = NewtonSolver()
group.nonlinear_solver.options['solve_subsystems'] = True
group.nonlinear_solver.options['iprint'] = 0
group.nonlinear_solver.options['maxiter'] = 20
group.linear_solver = DirectSolver()
prob.setup(check=False)
area_ratio = 1.3
super_sonic = False
prob['area_ratio'] = area_ratio
mach_comp.options['super_sonic'] = super_sonic
prob.run_model()
assert_rel_error(self, prob['mach'], mach_solve(area_ratio, super_sonic=super_sonic), 1e-8)
area_ratio = 1.3
super_sonic = True
prob['area_ratio'] = area_ratio
mach_comp.options['super_sonic'] = super_sonic
prob.run_model()
assert_rel_error(self, prob['mach'], mach_solve(area_ratio, super_sonic=super_sonic), 1e-8)
def setup(self):
num_times = self.options['num_times']
num_cp = self.options['num_cp']
step_size = self.options['step_size']
cubesat = self.options['cubesat']
mtx = self.options['mtx']
shape = (3, num_times)
drag_unit_vec = np.outer(
np.array([0., 0., 1.]),
np.ones(num_times),
)
comp = IndepVarComp()
comp.add_output('drag_unit_vec_t_3xn', val=drag_unit_vec)
comp.add_output('dry_mass', val=cubesat['dry_mass'], shape=num_times)
comp.add_output('radius_earth_km',
val=cubesat['radius_earth_km'],
shape=num_times)
for var_name in ['initial_orbit_state']:
comp.add_output(var_name, val=cubesat[var_name])
self.add_subsystem('input_comp', comp, promotes=['*'])
# comp = InitialOrbitComp()
# self.add_subsystem('initial_orbit_comp', comp, promotes=['*'])
comp = LinearCombinationComp(
shape=(num_times, ),
out_name='mass',
coeffs_dict=dict(dry_mass=1., propellant_mass=1.),
)
self.add_subsystem('mass_comp', comp, promotes=['*'])
if 1:
coupled_group = Group()
comp = LinearCombinationComp(
shape=shape,
out_name='force_3xn',
coeffs_dict=dict(thrust_3xn=1., drag_3xn=1.),
)
coupled_group.add_subsystem('force_3xn_comp', comp, promotes=['*'])
comp = RelativeOrbitRK4Comp(
num_times=num_times,
step_size=step_size,
)
coupled_group.add_subsystem('relative_orbit_rk4_comp',
comp,
promotes=['*'])
comp = LinearCombinationComp(
shape=(6, num_times),
out_name='orbit_state',
coeffs_dict=dict(
relative_orbit_state=1.,
reference_orbit_state=1.,
),
)
coupled_group.add_subsystem('orbit_state_comp',
comp,
promotes=['*'])
comp = LinearCombinationComp(
shape=(6, num_times),
out_name='orbit_state_km',
coeffs_dict=dict(orbit_state=1.e-3),
)
coupled_group.add_subsystem('orbit_state_km_comp',
comp,
promotes=['*'])
comp = RotMtxTIComp(num_times=num_times)
coupled_group.add_subsystem('rot_mtx_t_i_3x3xn_comp',
comp,
promotes=['*'])
comp = ArrayReorderComp(
in_shape=(3, 3, num_times),
out_shape=(3, 3, num_times),
in_subscripts='ijn',
out_subscripts='jin',
in_name='rot_mtx_t_i_3x3xn',
out_name='rot_mtx_i_t_3x3xn',
)
coupled_group.add_subsystem('rot_mtx_i_t_3x3xn_comp',
comp,
promotes=['*'])
comp = MtxVecComp(
num_times=num_times,
mtx_name='rot_mtx_i_t_3x3xn',
vec_name='drag_unit_vec_t_3xn',
out_name='drag_unit_vec_3xn',
)
coupled_group.add_subsystem('drag_unit_vec_3xn_comp',
comp,
promotes=['*'])
comp = PowerCombinationComp(shape=shape,
out_name='drag_3xn',
powers_dict=dict(
drag_unit_vec_3xn=1.,
drag_scalar_3xn=1.,
))
coupled_group.add_subsystem('drag_3xn_comp', comp, promotes=['*'])
coupled_group.nonlinear_solver = NonlinearBlockGS(iprint=0,
maxiter=40,
atol=1e-14,
rtol=1e-12)
coupled_group.linear_solver = LinearBlockGS(iprint=0,
maxiter=40,
atol=1e-14,
rtol=1e-12)
self.add_subsystem('coupled_group', coupled_group, promotes=['*'])
comp = OrbitStateDecompositionComp(
num_times=num_times,
position_name='position_km',
velocity_name='velocity_km_s',
orbit_state_name='orbit_state_km',
)
self.add_subsystem('orbit_state_decomposition_comp',
comp,
promotes=['*'])
comp = LinearCombinationComp(
shape=shape,
out_name='position',
coeffs_dict=dict(position_km=1.e3),
)
self.add_subsystem('position_comp', comp, promotes=['*'])
comp = LinearCombinationComp(
shape=shape,
out_name='velocity',
coeffs_dict=dict(velocity_km_s=1.e3),
)
self.add_subsystem('velocity_comp', comp, promotes=['*'])
#
group = DecomposeVectorGroup(
num_times=num_times,
vec_name='position_km',
norm_name='radius_km',
unit_vec_name='position_unit_vec',
)
self.add_subsystem('position_decomposition_group',
group,
promotes=['*'])
group = DecomposeVectorGroup(
num_times=num_times,
vec_name='velocity_km_s',
norm_name='speed_km_s',
unit_vec_name='velocity_unit_vec',
)
self.add_subsystem('velocity_decomposition_group',
group,
promotes=['*'])
#
comp = LinearCombinationComp(
shape=(num_times, ),
out_name='altitude_km',
coeffs_dict=dict(radius_km=1., radius_earth_km=-1.),
)
self.add_subsystem('altitude_km_comp', comp, promotes=['*'])
comp = KSComp(
in_name='altitude_km',
out_name='ks_altitude_km',
shape=(1, ),
constraint_size=num_times,
rho=100.,
lower_flag=True,
)
comp.add_constraint('ks_altitude_km', lower=450.)
self.add_subsystem('ks_altitude_km_comp', comp, promotes=['*'])
comp = PowerCombinationComp(shape=(
6,
num_times,
),
out_name='relative_orbit_state_sq',
powers_dict={
'relative_orbit_state': 2.,
})
self.add_subsystem('relative_orbit_state_sq_comp',
comp,
promotes=['*'])
comp = ScalarContractionComp(
shape=(
6,
num_times,
),
out_name='relative_orbit_state_sq_sum',
in_name='relative_orbit_state_sq',
)
self.add_subsystem('relative_orbit_state_sq_sum_comp',
comp,
promotes=['*'])
group.connect('s.S', 'rc.S')
# # Drag model
# comp = DragGroup()
# group.add_subsystem('drag_g', comp)
# group.connect('ivc.sweep','drag_g.sweep')
# group.connect('s.S','drag_g.S')
# group.connect('ws.b','drag_g.b') # connects wing span output b to drag group's b
# # group.connect('ivc.V_max','drag_g.V')
# group.connect('mac.C_bar', 'drag_g.C_bar')
group.nonlinear_solver = NonlinearBlockGS(iprint=2, maxiter=20)
group.linear_solver = LinearBlockGS(iprint=2, maxiter=100)
prob.model = group
prob.model.add_subsystem('objective',
ExecComp('obj = U_P', obj=0),
promotes=['*'])
prob.model.connect('cer_g.U_P', 'U_P')
prob.model.add_objective('obj')
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.setup()
prob.run_model()
# prob.run_driver()
def test_scalar_guess_func_using_outputs(self):
model = Group()
ind = IndepVarComp()
ind.add_output('a', 1)
ind.add_output('b', -4)
ind.add_output('c', 3)
lhs = ExecComp('lhs=-(a*x**2+b*x)')
bal = BalanceComp(name='x', rhs_name='c')
model.add_subsystem('ind_comp', ind, promotes_outputs=['a', 'b', 'c'])
model.add_subsystem('lhs_comp', lhs, promotes_inputs=['a', 'b', 'x'])
model.add_subsystem('bal_comp', bal, promotes_inputs=['c'], promotes_outputs=['x'])
model.connect('lhs_comp.lhs', 'bal_comp.lhs:x')
model.linear_solver = DirectSolver()
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
# first verify behavior of the balance comp without the guess function
# at initial conditions x=5, x=0 and x=-1
prob = Problem(model)
prob.setup()
# default solution with initial value of 5 is x=3.
prob['x'] = 5
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
# default solution with initial value of 0 is x=1.
prob['x'] = 0
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# default solution with initial value of -1 is x=1.
prob['x'] = -1
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# now use a guess function that steers us to the x=3 solution only
# if the initial value of x is less than zero
def guess_function(inputs, outputs, residuals):
if outputs['x'] < 0:
outputs['x'] = 3.
bal.options['guess_func'] = guess_function
# solution with initial value of 5 is still x=3.
prob['x'] = 5
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
# solution with initial value of 0 is still x=1.
prob['x'] = 0
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# solution with initial value of -1 is now x=3.
prob['x'] = -1
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
def test_scalar_guess_func_using_outputs(self):
model = Group()
ind = IndepVarComp()
ind.add_output('a', 1)
ind.add_output('b', -4)
ind.add_output('c', 3)
lhs = ExecComp('lhs=-(a*x**2+b*x)')
bal = BalanceComp(name='x', rhs_name='c')
model.add_subsystem('ind_comp', ind, promotes_outputs=['a', 'b', 'c'])
model.add_subsystem('lhs_comp', lhs, promotes_inputs=['a', 'b', 'x'])
model.add_subsystem('bal_comp',
bal,
promotes_inputs=['c'],
promotes_outputs=['x'])
model.connect('lhs_comp.lhs', 'bal_comp.lhs:x')
model.linear_solver = DirectSolver()
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
# first verify behavior of the balance comp without the guess function
# at initial conditions x=5, x=0 and x=-1
prob = Problem(model)
prob.setup()
# default solution with initial value of 5 is x=3.
prob['x'] = 5
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
# default solution with initial value of 0 is x=1.
prob['x'] = 0
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# default solution with initial value of -1 is x=1.
prob['x'] = -1
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# now use a guess function that steers us to the x=3 solution only
# if the initial value of x is less than zero
def guess_function(inputs, outputs, residuals):
if outputs['x'] < 0:
outputs['x'] = 3.
bal.options['guess_func'] = guess_function
# solution with initial value of 5 is still x=3.
prob['x'] = 5
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
# solution with initial value of 0 is still x=1.
prob['x'] = 0
prob.run_model()
assert_almost_equal(prob['x'], 1.0, decimal=7)
# solution with initial value of -1 is now x=3.
prob['x'] = -1
prob.run_model()
assert_almost_equal(prob['x'], 3.0, decimal=7)
def setup(self):
super(ImplicitTMIntegrator, self).setup()
ode_function = self.options['ode_function']
method = self.options['method']
starting_coeffs = self.options['starting_coeffs']
has_starting_method = method.starting_method is not None
is_starting_method = starting_coeffs is not None
states = ode_function._states
static_parameters = ode_function._static_parameters
dynamic_parameters = ode_function._dynamic_parameters
time_units = ode_function._time_options['units']
starting_norm_times, my_norm_times = self._get_meta()
glm_A, glm_B, glm_U, glm_V, num_stages, num_step_vars = self._get_method()
num_times = len(my_norm_times)
num_stages = method.num_stages
num_step_vars = method.num_values
glm_A = method.A
glm_B = method.B
glm_U = method.U
glm_V = method.V
# ------------------------------------------------------------------------------------
integration_group = Group()
self.add_subsystem('integration_group', integration_group)
for i_step in range(len(my_norm_times) - 1):
group = Group(assembled_jac_type='dense')
group_old_name = 'integration_group.step_%i' % (i_step - 1)
group_new_name = 'integration_group.step_%i' % i_step
integration_group.add_subsystem(group_new_name.split('.')[1], group)
comp = self._create_ode(num_stages)
group.add_subsystem('ode_comp', comp)
if ode_function._time_options['targets']:
self.connect('time_comp.stage_times',
['.'.join((group_new_name + '.ode_comp', t)) for t in
ode_function._time_options['targets']],
src_indices=i_step * (num_stages) + np.arange(num_stages))
if len(static_parameters) > 0:
self._connect_multiple(
self._get_static_parameter_names('static_parameter_comp', 'out'),
self._get_static_parameter_names(group_new_name + '.ode_comp', 'targets'),
src_indices_list=[[0] * num_stages for _ in range(len(static_parameters))]
)
if len(dynamic_parameters) > 0:
src_indices_list = []
for parameter_name, value in iteritems(dynamic_parameters):
size = np.prod(value['shape'])
shape = value['shape']
arange = np.arange(((len(my_norm_times) - 1) * num_stages * size)).reshape(
((len(my_norm_times) - 1, num_stages,) + shape))
src_indices = arange[i_step, :, :]
src_indices_list.append(src_indices.flat)
self._connect_multiple(
self._get_dynamic_parameter_names('dynamic_parameter_comp', 'out'),
self._get_dynamic_parameter_names(group_new_name + '.ode_comp', 'targets'),
src_indices_list,
)
comp = ImplicitTMStageComp(
states=states, time_units=time_units,
num_stages=num_stages, num_step_vars=num_step_vars,
glm_A=glm_A, glm_U=glm_U, i_step=i_step,
)
group.add_subsystem('stage_comp', comp)
self.connect('time_comp.h_vec', group_new_name + '.stage_comp.h', src_indices=i_step)
comp = ImplicitTMStepComp(
states=states, time_units=time_units,
num_stages=num_stages, num_step_vars=num_step_vars,
glm_B=glm_B, glm_V=glm_V, i_step=i_step,
)
group.add_subsystem('step_comp', comp)
self.connect('time_comp.h_vec', group_new_name + '.step_comp.h', src_indices=i_step)
self._connect_multiple(
self._get_state_names(group_new_name + '.ode_comp', 'rate_source'),
self._get_state_names(group_new_name + '.step_comp', 'F', i_step=i_step),
)
self._connect_multiple(
self._get_state_names(group_new_name + '.ode_comp', 'rate_source'),
self._get_state_names(group_new_name + '.stage_comp', 'F', i_step=i_step),
)
self._connect_multiple(
self._get_state_names(group_new_name + '.stage_comp', 'Y', i_step=i_step),
self._get_state_names(group_new_name + '.ode_comp', 'targets'),
)
if i_step == 0:
self._connect_multiple(
self._get_state_names('starting_system', 'starting'),
self._get_state_names(group_new_name + '.step_comp', 'y_old', i_step=i_step),
)
self._connect_multiple(
self._get_state_names('starting_system', 'starting'),
self._get_state_names(group_new_name + '.stage_comp', 'y_old', i_step=i_step),
)
else:
self._connect_multiple(
self._get_state_names(group_old_name + '.step_comp', 'y_new', i_step=i_step - 1),
self._get_state_names(group_new_name + '.step_comp', 'y_old', i_step=i_step),
)
self._connect_multiple(
self._get_state_names(group_old_name + '.step_comp', 'y_new', i_step=i_step - 1),
self._get_state_names(group_new_name + '.stage_comp', 'y_old', i_step=i_step),
)
group.nonlinear_solver = NewtonSolver(iprint=2, maxiter=100)
group.linear_solver = DirectSolver(assemble_jac=True)
promotes = []
promotes.extend([get_name('state', state_name) for state_name in states])
if is_starting_method:
promotes.extend([get_name('starting', state_name) for state_name in states])
comp = TMOutputComp(
states=states, num_starting_times=len(starting_norm_times),
num_my_times=len(my_norm_times), num_step_vars=num_step_vars,
starting_coeffs=starting_coeffs)
self.add_subsystem('output_comp', comp, promotes_outputs=promotes)
if has_starting_method:
self._connect_multiple(
self._get_state_names('starting_system', 'state'),
self._get_state_names('output_comp', 'starting_state'),
)
for i_step in range(len(my_norm_times)):
if i_step == 0:
self._connect_multiple(
self._get_state_names('starting_system', 'starting'),
self._get_state_names('output_comp', 'y', i_step=i_step),
)
else:
self._connect_multiple(
self._get_state_names('integration_group.step_%i' % (i_step - 1) + '.step_comp', 'y_new', i_step=i_step - 1),
self._get_state_names('output_comp', 'y', i_step=i_step),
)
def test_no_promotion_errors(self):
"""
Tests for error-handling for invalid variable names and keys.
"""
g = Group(assembled_jac_type='dense')
g.linear_solver = DirectSolver(assemble_jac=True)
g.add_subsystem('c', ExecComp('y=2*x'))
p = Problem()
model = p.model
model.add_subsystem('g', g)
p.setup()
# -------------------------------------------------------------------
msg = '\'Group (<model>): Variable "{}" not found.\''
# inputs
with self.assertRaises(KeyError) as ctx:
p['x'] = 5.0
self.assertEqual(str(ctx.exception), msg.format('x'))
p._initial_condition_cache = {}
with self.assertRaises(KeyError) as ctx:
p['x']
self.assertEqual(str(ctx.exception), msg.format('x'))
# outputs
with self.assertRaises(KeyError) as ctx:
p['y'] = 5.0
self.assertEqual(str(ctx.exception), msg.format('y'))
p._initial_condition_cache = {}
with self.assertRaises(KeyError) as ctx:
p['y']
self.assertEqual(str(ctx.exception), msg.format('y'))
p.final_setup()
msg = "Group (g): Variable name '{}' not found."
inputs, outputs, residuals = g.get_nonlinear_vectors()
# inputs
for vname in ['x', 'g.c.x']:
with self.assertRaises(KeyError) as cm:
inputs[vname] = 5.0
self.assertEqual(cm.exception.args[0],
f"Group (g): Variable name '{vname}' not found.")
with self.assertRaises(KeyError) as cm:
inputs[vname]
self.assertEqual(cm.exception.args[0],
f"Group (g): Variable name '{vname}' not found.")
# outputs
for vname in ['y', 'g.c.y']:
with self.assertRaises(KeyError) as cm:
outputs[vname] = 5.0
self.assertEqual(cm.exception.args[0],
f"Group (g): Variable name '{vname}' not found.")
with self.assertRaises(KeyError) as cm:
outputs[vname]
self.assertEqual(cm.exception.args[0],
f"Group (g): Variable name '{vname}' not found.")
msg = r'Variable name pair \("{}", "{}"\) not found.'
jac = g.linear_solver._assembled_jac
# d(output)/d(input)
with self.assertRaisesRegex(KeyError, msg.format('y', 'x')):
jac['y', 'x'] = 5.0
with self.assertRaisesRegex(KeyError, msg.format('y', 'x')):
jac['y', 'x']
# allow absolute keys now
# with self.assertRaisesRegex(KeyError, msg.format('g.c.y', 'g.c.x')):
# jac['g.c.y', 'g.c.x'] = 5.0
# with self.assertRaisesRegex(KeyError, msg.format('g.c.y', 'g.c.x')):
# deriv = jac['g.c.y', 'g.c.x']
# d(output)/d(output)
with self.assertRaisesRegex(KeyError, msg.format('y', 'y')):
jac['y', 'y'] = 5.0
with self.assertRaisesRegex(KeyError, msg.format('y', 'y')):
jac['y', 'y']
def setup(self):
surfaces = self.options['surfaces']
coupled = Group()
for surface in surfaces:
name = surface['name']
# Connect the output of the loads component with the FEM
# displacement parameter. This links the coupling within the coupled
# group that necessitates the subgroup solver.
coupled.connect(name + '_loads.loads', name + '.loads')
# Perform the connections with the modified names within the
# 'aero_states' group.
coupled.connect(name + '.normals', 'aero_states.' + name + '_normals')
coupled.connect(name + '.def_mesh', 'aero_states.' + name + '_def_mesh')
# Connect the results from 'coupled' to the performance groups
coupled.connect(name + '.def_mesh', name + '_loads.def_mesh')
coupled.connect('aero_states.' + name + '_sec_forces', name + '_loads.sec_forces')
# Connect the results from 'aero_states' to the performance groups
self.connect('coupled.aero_states.' + name + '_sec_forces', name + '_perf' + '.sec_forces')
# Connection performance functional variables
self.connect(name + '_perf.CL', 'total_perf.' + name + '_CL')
self.connect(name + '_perf.CD', 'total_perf.' + name + '_CD')
self.connect('coupled.aero_states.' + name + '_sec_forces', 'total_perf.' + name + '_sec_forces')
self.connect('coupled.' + name + '.chords', name + '_perf.aero_funcs.chords')
# Connect parameters from the 'coupled' group to the performance
# groups for the individual surfaces.
self.connect('coupled.' + name + '.disp', name + '_perf.disp')
self.connect('coupled.' + name + '.S_ref', name + '_perf.S_ref')
self.connect('coupled.' + name + '.widths', name + '_perf.widths')
# self.connect('coupled.' + name + '.chords', name + '_perf.chords')
self.connect('coupled.' + name + '.lengths', name + '_perf.lengths')
self.connect('coupled.' + name + '.cos_sweep', name + '_perf.cos_sweep')
# Connect parameters from the 'coupled' group to the total performance group.
self.connect('coupled.' + name + '.S_ref', 'total_perf.' + name + '_S_ref')
self.connect('coupled.' + name + '.widths', 'total_perf.' + name + '_widths')
self.connect('coupled.' + name + '.chords', 'total_perf.' + name + '_chords')
self.connect('coupled.' + name + '.b_pts', 'total_perf.' + name + '_b_pts')
# Add components to the 'coupled' group for each surface.
# The 'coupled' group must contain all components and parameters
# needed to converge the aerostructural system.
coupled_AS_group = CoupledAS(surface=surface)
if surface['distributed_fuel_weight']:
promotes = ['load_factor']
else:
promotes = []
coupled.add_subsystem(name, coupled_AS_group, promotes_inputs=promotes)
# Add a single 'aero_states' component for the whole system within the
# coupled group.
coupled.add_subsystem('aero_states',
VLMStates(surfaces=surfaces),
promotes_inputs=['v', 'alpha', 'rho'])
# Explicitly connect parameters from each surface's group and the common
# 'aero_states' group.
for surface in surfaces:
name = surface['name']
# Add a loads component to the coupled group
coupled.add_subsystem(name + '_loads', LoadTransfer(surface=surface))
"""
### Change the solver settings here ###
"""
# Set solver properties for the coupled group
# coupled.linear_solver = ScipyIterativeSolver()
# coupled.linear_solver.precon = LinearRunOnce()
coupled.nonlinear_solver = NonlinearBlockGS(use_aitken=True)
coupled.nonlinear_solver.options['maxiter'] = 100
coupled.nonlinear_solver.options['atol'] = 1e-7
coupled.nonlinear_solver.options['rtol'] = 1e-30
# coupled.linear_solver = DirectSolver()
coupled.linear_solver = DirectSolver(assemble_jac=True)
coupled.options['assembled_jac_type'] = 'csc'
# coupled.nonlinear_solver = NewtonSolver(solve_subsystems=True)
# coupled.nonlinear_solver.options['maxiter'] = 50
coupled.nonlinear_solver.options['iprint'] = 2
"""
### End change of solver settings ###
"""
# Add the coupled group to the model problem
self.add_subsystem('coupled', coupled, promotes_inputs=['v', 'alpha', 'rho'])
for surface in surfaces:
name = surface['name']
# Add a performance group which evaluates the data after solving
# the coupled system
perf_group = CoupledPerformance(surface=surface)
self.add_subsystem(name + '_perf', perf_group, promotes_inputs=['rho', 'v', 'alpha', 're', 'Mach_number'])
# Add functionals to evaluate performance of the system.
# Note that only the interesting results are promoted here; not all
# of the parameters.
self.add_subsystem('total_perf',
TotalPerformance(surfaces=surfaces,
user_specified_Sref=self.options['user_specified_Sref'],
internally_connect_fuelburn=self.options['internally_connect_fuelburn']),
promotes_inputs=['v', 'rho', 'empty_cg', 'total_weight', 'CT', 'speed_of_sound', 'R', 'Mach_number', 'W0', 'load_factor', 'S_ref_total'],
promotes_outputs=['L_equals_W', 'fuelburn', 'CL', 'CD', 'CM', 'cg'])
def test_nested_promotion_errors(self):
"""
Tests for error-handling for promoted input variable names.
"""
c1 = IndepVarComp('x')
c2 = ExecComp('y=2*x')
c3 = ExecComp('z=3*x')
g = Group(assembled_jac_type='dense')
g.add_subsystem('c2', c2, promotes=['*'])
g.add_subsystem('c3', c3, promotes=['*'])
g.linear_solver = DirectSolver(assemble_jac=True)
model = Group()
model.add_subsystem('c1', c1, promotes=['*'])
model.add_subsystem('g', g)
p = Problem(model)
p.setup(check=False)
# -------------------------------------------------------------------
msg1 = "The promoted name g.x is invalid because it refers to multiple inputs: " \
"[g.c2.x, g.c3.x] that are not connected to an output variable."
# inputs (g.x is not connected)
# with assertRaisesRegex(self, RuntimeError, msg1.format('g.x')):
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup(check=False)
p.final_setup()
# -------------------------------------------------------------------
# inputs (g.x is not connected)
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Start from a clean state again
p = Problem(model)
p.setup(check=False)
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
msg2 = "The promoted name x is invalid because it refers to multiple inputs: " \
"[g.c2.x, g.c3.x] that are not connected to an output variable."
jac = g.linear_solver._assembled_jac
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
# -------------------------------------------------------------------
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup(check=False)
p.final_setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
# -------------------------------------------------------------------
msg1 = "The promoted name g.x is invalid because it refers to multiple inputs: " \
"[g.c2.x ,g.c3.x]. Access the value from the connected output variable x instead."
# From here, 'g.x' has a valid source.
model.connect('x', 'g.x')
p = Problem(model)
p.setup(check=False)
# inputs (g.x is connected to x)
p['g.x'] = 5.0
with self.assertRaises(Exception) as context:
p.final_setup()
self.assertEqual(str(context.exception), msg1)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup(check=False)
p.final_setup()
# inputs (g.x is connected to x)
with self.assertRaises(Exception) as context:
p['g.x'] = 5.0
self.assertEqual(str(context.exception), msg1)
# Final test, the getitem
p = Problem(model)
p.setup(check=False)
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0) # Start from a clean state again
self.assertEqual(str(context.exception), msg2)
# Repeat test for post final_setup when vectors are allocated.
p = Problem(model)
p.setup(check=False)
p.final_setup()
with self.assertRaises(Exception) as context:
self.assertEqual(p['g.x'], 5.0)
self.assertEqual(str(context.exception), msg1)
# d(outputs)/d(inputs)
with self.assertRaises(Exception) as context:
jac['y', 'x'] = 5.0
self.assertEqual(str(context.exception), msg2)
with self.assertRaises(Exception) as context:
self.assertEqual(jac['y', 'x'], 5.0)
self.assertEqual(str(context.exception), msg2)
def main():
num_nodes = 1
num_blades = 10
num_radial = 15
num_cp = 6
af_filename = 'airfoils/mh117.dat'
chord = 20.
theta = 5.0*np.pi/180.0
pitch = 0.
# Numbers taken from the Aviary group's study of the RVLT tiltwing
# turboelectric concept vehicle.
n_props = 4
hub_diameter = 30. # cm
prop_diameter = 15*30.48 # 15 ft in cm
c0 = np.sqrt(1.4*287.058*300.) # meters/second
rho0 = 1.4*98600./(c0*c0) # kg/m^3
omega = 236. # rad/s
# Find the thrust per rotor from the vehicle's mass.
m_full = 6367 # kg
g = 9.81 # m/s**2
thrust_vtol = m_full*g/n_props
prob = Problem()
comp = IndepVarComp()
comp.add_output('rho', val=rho0, shape=num_nodes, units='kg/m**3')
comp.add_output('mu', val=1., shape=num_nodes, units='N/m**2*s')
comp.add_output('asound', val=c0, shape=num_nodes, units='m/s')
comp.add_output('v', val=2., shape=num_nodes, units='m/s')
comp.add_output('alpha', val=0., shape=num_nodes, units='rad')
comp.add_output('incidence', val=0., shape=num_nodes, units='rad')
comp.add_output('precone', val=0., units='deg')
comp.add_output('hub_diameter', val=hub_diameter, shape=num_nodes, units='cm')
comp.add_output('prop_diameter', val=prop_diameter, shape=num_nodes, units='cm')
comp.add_output('pitch', val=pitch, shape=num_nodes, units='rad')
comp.add_output('chord_dv', val=chord, shape=num_cp, units='cm')
comp.add_output('theta_dv', val=theta, shape=num_cp, units='rad')
comp.add_output('thrust_vtol', val=thrust_vtol, shape=num_nodes, units='N')
prob.model.add_subsystem('indep_var_comp', comp, promotes=['*'])
comp = GeometryGroup(num_nodes=num_nodes, num_cp=num_cp,
num_radial=num_radial)
prob.model.add_subsystem(
'geometry_group', comp,
promotes_inputs=['hub_diameter', 'prop_diameter', 'chord_dv',
'theta_dv', 'pitch'],
promotes_outputs=['radii', 'dradii', 'chord', 'theta'])
balance_group = Group()
comp = CCBladeGroup(num_nodes=num_nodes, num_radial=num_radial,
num_blades=num_blades, af_filename=af_filename,
turbine=False)
balance_group.add_subsystem(
'ccblade_group', comp,
promotes_inputs=['radii', 'dradii', 'chord', 'theta', 'rho', 'mu',
'asound', 'v', 'precone', 'omega', 'hub_diameter',
'prop_diameter'],
promotes_outputs=['thrust', 'torque', 'efficiency'])
comp = BalanceComp()
comp.add_balance(
name='omega',
eq_units='N', lhs_name='thrust', rhs_name='thrust_vtol',
val=omega, units='rad/s',
lower=0.)
balance_group.add_subsystem('thrust_balance_comp', comp, promotes=['*'])
balance_group.linear_solver = DirectSolver(assemble_jac=True)
# balance_group.nonlinear_solver = NewtonSolver(maxiter=20, iprint=2)
# balance_group.nonlinear_solver.options['solve_subsystems'] = True
# balance_group.nonlinear_solver.options['atol'] = 1e-9
prob.model.add_subsystem('thrust_balance_group', balance_group,
promotes=['*'])
prob.model.nonlinear_solver = NewtonSolver(maxiter=20, iprint=2)
prob.model.nonlinear_solver.options['solve_subsystems'] = True
prob.model.nonlinear_solver.options['atol'] = 1e-9
prob.model.nonlinear_solver.linesearch = BoundsEnforceLS()
prob.model.nonlinear_solver.linesearch.options['iprint'] = 2
prob.setup()
prob.final_setup()
# Calculate the induced axial velocity at the rotor for hover, used for
# non-diminsionalation.
rho = prob.get_val('rho', units='kg/m**3')[0]
hub_diameter = prob.get_val('hub_diameter', units='m')[0]
prop_diameter = prob.get_val('prop_diameter', units='m')[0]
thrust_vtol = prob.get_val('thrust_vtol', units='N')[0]
A_rotor = 0.25*np.pi*(prop_diameter**2 - hub_diameter**2)
v_h = np.sqrt(thrust_vtol/(2*rho*A_rotor))
# Climb:
climb_velocity_nondim = np.linspace(0.1, 2., 10)
induced_velocity_nondim = np.zeros_like(climb_velocity_nondim)
for vc, vi in np.nditer(
[climb_velocity_nondim, induced_velocity_nondim],
op_flags=[['readonly'], ['writeonly']]):
# Run the model with the requested climb velocity.
prob.set_val('v', vc*v_h, units='m/s')
print(f"v = {prob.get_val('v', units='m/s')}")
prob.run_model()
# Calculate the area-weighted average induced velocity at the rotor.
# Need the area of each blade section.
radii = prob.get_val('radii',
units='m')
dradii = prob.get_val('dradii',
units='m')
dArea = 2*np.pi*radii*dradii
# Get the induced velocity at the rotor plane for each blade section.
Vx = prob.get_val('ccblade_group.Vx', units='m/s')
a = prob.get_val('ccblade_group.ccblade_comp.a')
# Get the area-weighted average of the induced velocity.
vi[...] = np.sum(a*Vx*dArea/A_rotor)/v_h
# Induced velocity from plain old momentum theory (for climb).
induced_velocity_mt = (
-0.5*climb_velocity_nondim + np.sqrt((0.5*climb_velocity_nondim)**2 + 1.))
fig, ax = plt.subplots()
ax.plot(climb_velocity_nondim, -induced_velocity_nondim,
label='CCBlade.jl (climb)')
ax.plot(climb_velocity_nondim, induced_velocity_mt,
label='Momentum Theory (climb)')
# Descent:
climb_velocity_nondim = np.linspace(-4., -2.5, 10)
induced_velocity_nondim = np.zeros_like(climb_velocity_nondim)
for vc, vi in np.nditer(
[climb_velocity_nondim, induced_velocity_nondim],
op_flags=[['readonly'], ['writeonly']]):
# Run the model with the requested climb velocity.
prob.set_val('v', vc*v_h, units='m/s')
print(f"vc = {vc}, v = {prob.get_val('v', units='m/s')}")
prob.run_model()
# Calculate the area-weighted average induced velocity at the rotor.
# Need the area of each blade section.
radii = prob.get_val('radii',
units='m')
dradii = prob.get_val('dradii',
units='m')
dArea = 2*np.pi*radii*dradii
# Get the induced velocity at the rotor plane for each blade section.
Vx = prob.get_val('ccblade_group.Vx', units='m/s')
a = prob.get_val('ccblade_group.ccblade_comp.a')
# Get the area-weighted average of the induced velocity.
vi[...] = np.sum(a*Vx*dArea/A_rotor)/v_h
# Induced velocity from plain old momentum theory (for descent).
induced_velocity_mt = (
-0.5*climb_velocity_nondim - np.sqrt((0.5*climb_velocity_nondim)**2 - 1.))
# Plot the induced velocity for descent.
ax.plot(climb_velocity_nondim, -induced_velocity_nondim,
label='CCBlade.jl (descent)')
ax.plot(climb_velocity_nondim, induced_velocity_mt,
label='Momentum Theory (descent)')
# # Empirical region:
# climb_velocity_nondim = np.linspace(-1.9, -1.5, 5)
# induced_velocity_nondim = np.zeros_like(climb_velocity_nondim)
# for vc, vi in np.nditer(
# [climb_velocity_nondim, induced_velocity_nondim],
# op_flags=[['readonly'], ['writeonly']]):
# # Run the model with the requested climb velocity.
# prob.set_val('v', vc*v_h, units='m/s')
# print(f"vc = {vc}, v = {prob.get_val('v', units='m/s')}")
# prob.run_model()
# # Calculate the area-weighted average induced velocity at the rotor.
# # Need the area of each blade section.
# radii = prob.get_val('radii',
# units='m')
# dradii = prob.get_val('dradii',
# units='m')
# dArea = 2*np.pi*radii*dradii
# # Get the induced velocity at the rotor plane for each blade section.
# Vx = prob.get_val('ccblade_group.Vx', units='m/s')
# a = prob.get_val('ccblade_group.ccblade_comp.a')
# # Get the area-weighted average of the induced velocity.
# vi[...] = np.sum(a*Vx*dArea/A_rotor)/v_h
# # Plot the induced velocity for the empirical region.
# ax.plot(climb_velocity_nondim, -induced_velocity_nondim,
# label='CCBlade.jl (empirical region)')
ax.set_xlabel('Vc/vh')
ax.set_ylabel('Vi/vh')
ax.legend()
fig.savefig('induced_velocity.png')
def setup(self):
surfaces = self.options['surfaces']
coupled = Group()
for surface in surfaces:
name = surface['name']
# Connect the output of the loads component with the FEM
# displacement parameter. This links the coupling within the coupled
# group that necessitates the subgroup solver.
coupled.connect(name + '_loads.loads', name + '.loads')
# Perform the connections with the modified names within the
# 'aero_states' group.
coupled.connect(name + '.normals', 'aero_states.' + name + '_normals')
coupled.connect(name + '.def_mesh', 'aero_states.' + name + '_def_mesh')
# Connect the results from 'coupled' to the performance groups
coupled.connect(name + '.def_mesh', name + '_loads.def_mesh')
coupled.connect('aero_states.' + name + '_sec_forces', name + '_loads.sec_forces')
# Connect the results from 'aero_states' to the performance groups
self.connect('coupled.aero_states.' + name + '_sec_forces', name + '_perf' + '.sec_forces')
# Connection performance functional variables
self.connect(name + '_perf.CL', 'total_perf.' + name + '_CL')
self.connect(name + '_perf.CD', 'total_perf.' + name + '_CD')
self.connect('coupled.aero_states.' + name + '_sec_forces', 'total_perf.' + name + '_sec_forces')
self.connect('coupled.' + name + '.chords', name + '_perf.aero_funcs.chords')
# Connect parameters from the 'coupled' group to the performance
# groups for the individual surfaces.
self.connect('coupled.' + name + '.disp', name + '_perf.disp')
self.connect('coupled.' + name + '.S_ref', name + '_perf.S_ref')
self.connect('coupled.' + name + '.widths', name + '_perf.widths')
# self.connect('coupled.' + name + '.chords', name + '_perf.chords')
self.connect('coupled.' + name + '.lengths', name + '_perf.lengths')
self.connect('coupled.' + name + '.cos_sweep', name + '_perf.cos_sweep')
# Connect parameters from the 'coupled' group to the total performance group.
self.connect('coupled.' + name + '.S_ref', 'total_perf.' + name + '_S_ref')
self.connect('coupled.' + name + '.widths', 'total_perf.' + name + '_widths')
self.connect('coupled.' + name + '.chords', 'total_perf.' + name + '_chords')
self.connect('coupled.' + name + '.b_pts', 'total_perf.' + name + '_b_pts')
# Add components to the 'coupled' group for each surface.
# The 'coupled' group must contain all components and parameters
# needed to converge the aerostructural system.
coupled_AS_group = CoupledAS(surface=surface)
if surface['distributed_fuel_weight']:
promotes = ['load_factor']
else:
promotes = []
coupled.add_subsystem(name, coupled_AS_group, promotes_inputs=promotes)
# Add a single 'aero_states' component for the whole system within the
# coupled group.
coupled.add_subsystem('aero_states',
VLMStates(surfaces=surfaces),
promotes_inputs=['v', 'alpha', 'rho'])
# Explicitly connect parameters from each surface's group and the common
# 'aero_states' group.
for surface in surfaces:
name = surface['name']
# Add a loads component to the coupled group
coupled.add_subsystem(name + '_loads', LoadTransfer(surface=surface))
"""
### Change the solver settings here ###
"""
# Set solver properties for the coupled group
# coupled.linear_solver = ScipyIterativeSolver()
# coupled.linear_solver.precon = LinearRunOnce()
coupled.nonlinear_solver = NonlinearBlockGS(use_aitken=True)
coupled.nonlinear_solver.options['maxiter'] = 50
coupled.nonlinear_solver.options['atol'] = 5e-6
coupled.nonlinear_solver.options['rtol'] = 1e-12
# coupled.linear_solver = DirectSolver()
coupled.linear_solver = DirectSolver(assemble_jac=True)
coupled.options['assembled_jac_type'] = 'csc'
# coupled.nonlinear_solver = NewtonSolver(solve_subsystems=True)
# coupled.nonlinear_solver.options['maxiter'] = 50
coupled.nonlinear_solver.options['iprint'] = 0
"""
### End change of solver settings ###
"""
# Add the coupled group to the model problem
self.add_subsystem('coupled', coupled, promotes_inputs=['v', 'alpha', 'rho'])
for surface in surfaces:
name = surface['name']
# Add a performance group which evaluates the data after solving
# the coupled system
perf_group = CoupledPerformance(surface=surface)
self.add_subsystem(name + '_perf', perf_group, promotes_inputs=["rho", "v", "alpha", "re", "M"])
# Add functionals to evaluate performance of the system.
# Note that only the interesting results are promoted here; not all
# of the parameters.
self.add_subsystem('total_perf',
TotalPerformance(surfaces=surfaces,
user_specified_Sref=self.options['user_specified_Sref'],
internally_connect_fuelburn=self.options['internally_connect_fuelburn']),
promotes_inputs=['v', 'rho', 'empty_cg', 'total_weight', 'CT', 'a', 'R', 'M', 'W0', 'load_factor', 'S_ref_total'],
promotes_outputs=['L_equals_W', 'fuelburn', 'CL', 'CD', 'CM', 'cg'])
def test_simple_external_code_implicit_comp(self):
from openmdao.api import Group, NewtonSolver, Problem, IndepVarComp, DirectSolver, \
ExternalCodeImplicitComp
class MachExternalCodeComp(ExternalCodeImplicitComp):
def initialize(self):
self.options.declare('super_sonic', types=bool)
def setup(self):
self.add_input('area_ratio', val=1.0, units=None)
self.add_output('mach', val=1., units=None)
self.declare_partials(of='mach', wrt='area_ratio', method='fd')
self.input_file = 'mach_input.dat'
self.output_file = 'mach_output.dat'
# providing these are optional; the component will verify that any input
# files exist before execution and that the output files exist after.
self.options['external_input_files'] = [self.input_file]
self.options['external_output_files'] = [self.output_file]
self.options['command_apply'] = [
'python',
'extcode_mach.py',
self.input_file,
self.output_file,
]
self.options['command_solve'] = [
'python',
'extcode_mach.py',
self.input_file,
self.output_file,
]
def apply_nonlinear(self, inputs, outputs, residuals):
with open(self.input_file, 'w') as input_file:
input_file.write('residuals\n')
input_file.write('{}\n'.format(inputs['area_ratio'][0]))
input_file.write('{}\n'.format(outputs['mach'][0]))
# the parent apply_nonlinear function actually runs the external code
super(MachExternalCodeComp,
self).apply_nonlinear(inputs, outputs, residuals)
# parse the output file from the external code and set the value of mach
with open(self.output_file, 'r') as output_file:
mach = float(output_file.read())
residuals['mach'] = mach
def solve_nonlinear(self, inputs, outputs):
with open(self.input_file, 'w') as input_file:
input_file.write('outputs\n')
input_file.write('{}\n'.format(inputs['area_ratio'][0]))
input_file.write('{}\n'.format(
self.options['super_sonic']))
# the parent apply_nonlinear function actually runs the external code
super(MachExternalCodeComp,
self).solve_nonlinear(inputs, outputs)
# parse the output file from the external code and set the value of mach
with open(self.output_file, 'r') as output_file:
mach = float(output_file.read())
outputs['mach'] = mach
group = Group()
group.add_subsystem('ar', IndepVarComp('area_ratio', 0.5))
mach_comp = group.add_subsystem('comp',
MachExternalCodeComp(),
promotes=['*'])
prob = Problem(model=group)
group.nonlinear_solver = NewtonSolver()
group.nonlinear_solver.options['solve_subsystems'] = True
group.nonlinear_solver.options['iprint'] = 0
group.nonlinear_solver.options['maxiter'] = 20
group.linear_solver = DirectSolver()
prob.setup(check=False)
area_ratio = 1.3
super_sonic = False
prob['area_ratio'] = area_ratio
mach_comp.options['super_sonic'] = super_sonic
prob.run_model()
assert_rel_error(self, prob['mach'],
mach_solve(area_ratio, super_sonic=super_sonic), 1e-8)
area_ratio = 1.3
super_sonic = True
prob['area_ratio'] = area_ratio
mach_comp.options['super_sonic'] = super_sonic
prob.run_model()
assert_rel_error(self, prob['mach'],
mach_solve(area_ratio, super_sonic=super_sonic), 1e-8)
def setup(self):
super(VectorizedIntegrator, self).setup()
ode_function = self.options['ode_function']
method = self.options['method']
starting_coeffs = self.options['starting_coeffs']
formulation = self.options['formulation']
has_starting_method = method.starting_method is not None
is_starting_method = starting_coeffs is not None
states = ode_function._states
static_parameters = ode_function._static_parameters
dynamic_parameters = ode_function._dynamic_parameters
time_units = ode_function._time_options['units']
starting_norm_times, my_norm_times = self._get_meta()
glm_A, glm_B, glm_U, glm_V, num_stages, num_step_vars = self._get_method(
)
num_times = len(my_norm_times)
# ------------------------------------------------------------------------------------
integration_group = Group(assembled_jac_type='dense')
self.add_subsystem('integration_group', integration_group)
if formulation == 'optimizer-based':
comp = IndepVarComp()
for state_name, state in iteritems(states):
comp.add_output('Y:%s' % state_name,
shape=(
num_times - 1,
num_stages,
) + state['shape'],
units=state['units'])
comp.add_design_var('Y:%s' % state_name)
integration_group.add_subsystem('desvars_comp', comp)
elif formulation == 'solver-based':
comp = IndepVarComp()
for state_name, state in iteritems(states):
comp.add_output('Y:%s' % state_name,
val=0.,
shape=(
num_times - 1,
num_stages,
) + state['shape'],
units=state['units'])
integration_group.add_subsystem('dummy_comp', comp)
comp = self._create_ode((num_times - 1) * num_stages)
integration_group.add_subsystem('ode_comp', comp)
if ode_function._time_options['targets']:
self.connect(
'time_comp.stage_times',
[
'.'.join(('integration_group.ode_comp', t))
for t in ode_function._time_options['targets']
],
)
if len(static_parameters) > 0:
self._connect_multiple(
self._get_static_parameter_names('static_parameter_comp',
'out'),
self._get_static_parameter_names('integration_group.ode_comp',
'targets'),
[
np.array([0] * self._get_stage_norm_times(), np.int)
for _ in range(len(static_parameters))
])
if len(dynamic_parameters) > 0:
self._connect_multiple(
self._get_dynamic_parameter_names('dynamic_parameter_comp',
'out'),
self._get_dynamic_parameter_names('integration_group.ode_comp',
'targets'),
)
comp = VectorizedStageStepComp(
states=states,
time_units=time_units,
num_times=num_times,
num_stages=num_stages,
num_step_vars=num_step_vars,
glm_A=glm_A,
glm_U=glm_U,
glm_B=glm_B,
glm_V=glm_V,
)
integration_group.add_subsystem('vectorized_stagestep_comp', comp)
self.connect('time_comp.h_vec',
'integration_group.vectorized_stagestep_comp.h_vec')
comp = VectorizedStepComp(
states=states,
time_units=time_units,
num_times=num_times,
num_stages=num_stages,
num_step_vars=num_step_vars,
glm_B=glm_B,
glm_V=glm_V,
)
self.add_subsystem('vectorized_step_comp', comp)
self.connect('time_comp.h_vec', 'vectorized_step_comp.h_vec')
self._connect_multiple(
self._get_state_names('starting_system', 'starting'),
self._get_state_names(
'integration_group.vectorized_stagestep_comp', 'y0'),
)
self._connect_multiple(
self._get_state_names('starting_system', 'starting'),
self._get_state_names('vectorized_step_comp', 'y0'),
)
comp = VectorizedOutputComp(
states=states,
num_starting_times=len(starting_norm_times),
num_my_times=len(my_norm_times),
num_step_vars=num_step_vars,
starting_coeffs=starting_coeffs,
)
promotes = []
promotes.extend(
[get_name('state', state_name) for state_name in states])
if is_starting_method:
promotes.extend(
[get_name('starting', state_name) for state_name in states])
self.add_subsystem('output_comp', comp, promotes_outputs=promotes)
if has_starting_method:
self._connect_multiple(
self._get_state_names('starting_system', 'state'),
self._get_state_names('output_comp', 'starting_state'),
)
src_indices_to_ode = []
src_indices_from_ode = []
for state_name, state in iteritems(states):
size = np.prod(state['shape'])
shape = state['shape']
src_indices_to_ode.append(
np.arange(
(num_times - 1) * num_stages *
size).reshape(((num_times - 1) * num_stages, ) + shape))
src_indices_from_ode.append(
np.arange((num_times - 1) * num_stages * size).reshape((
num_times - 1,
num_stages,
) + shape))
src_indices_to_ode = [
np.array(idx).squeeze() for idx in src_indices_to_ode
]
self._connect_multiple(
self._get_state_names('vectorized_step_comp', 'y'),
self._get_state_names('output_comp', 'y'),
)
self._connect_multiple(
self._get_state_names('integration_group.ode_comp', 'rate_source'),
self._get_state_names('vectorized_step_comp', 'F'),
src_indices_from_ode,
)
self._connect_multiple(
self._get_state_names('integration_group.ode_comp', 'rate_source'),
self._get_state_names(
'integration_group.vectorized_stagestep_comp', 'F'),
src_indices_from_ode,
)
if formulation == 'solver-based':
self._connect_multiple(
self._get_state_names(
'integration_group.vectorized_stagestep_comp', 'Y_out'),
self._get_state_names('integration_group.ode_comp', 'targets'),
src_indices_to_ode,
)
self._connect_multiple(
self._get_state_names('integration_group.dummy_comp', 'Y'),
self._get_state_names(
'integration_group.vectorized_stagestep_comp', 'Y_in'),
)
elif formulation == 'optimizer-based':
self._connect_multiple(
self._get_state_names('integration_group.desvars_comp', 'Y'),
self._get_state_names('integration_group.ode_comp', 'targets'),
src_indices_to_ode,
)
self._connect_multiple(
self._get_state_names('integration_group.desvars_comp', 'Y'),
self._get_state_names(
'integration_group.vectorized_stagestep_comp', 'Y_in'),
)
for state_name, state in iteritems(states):
integration_group.add_constraint(
'vectorized_stagestep_comp.Y_out:%s' % state_name,
equals=0.,
vectorize_derivs=True,
)
if has_starting_method:
self.starting_system.options['formulation'] = self.options[
'formulation']
if formulation == 'solver-based':
if 1:
integration_group.nonlinear_solver = NonlinearBlockGS(
iprint=2, maxiter=40, atol=1e-14, rtol=1e-12)
else:
integration_group.nonlinear_solver = NewtonSolver(iprint=2,
maxiter=100)
if 1:
integration_group.linear_solver = LinearBlockGS(iprint=1,
maxiter=40,
atol=1e-14,
rtol=1e-12)
else:
integration_group.linear_solver = DirectSolver(
assemble_jac=True, iprint=1)
