def brachistochrone_min_time(transcription='gauss-lobatto', num_segments=8, transcription_order=3,
compressed=True, optimizer='SLSQP', simul_derivs=True):
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
OPT, OPTIMIZER = set_pyoptsparse_opt(optimizer, fallback=True)
p.driver.options['optimizer'] = OPTIMIZER
if simul_derivs:
p.driver.declare_coloring()
if transcription == 'gauss-lobatto':
t = dm.GaussLobatto(num_segments=num_segments,
order=transcription_order,
compressed=compressed)
elif transcription == 'radau-ps':
t = dm.Radau(num_segments=num_segments,
order=transcription_order,
compressed=compressed)
phase = dm.Phase(ode_class=BrachistochroneODE, transcription=t)
p.model.add_subsystem('phase0', phase)
phase.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='s')
phase.add_state('x', fix_initial=True, fix_final=False, solve_segments=False,
units='m', rate_source='xdot')
phase.add_state('y', fix_initial=True, fix_final=False, solve_segments=False,
units='m', rate_source='ydot')
phase.add_state('v', fix_initial=True, fix_final=False, solve_segments=False,
units='m/s', rate_source='vdot')
phase.add_control('theta', continuity=True, rate_continuity=True,
units='deg', lower=0.01, upper=179.9)
phase.add_parameter('g', units='m/s**2', val=9.80665, targets=['g'])
phase.add_boundary_constraint('x', loc='final', equals=10)
phase.add_boundary_constraint('y', loc='final', equals=5)
# Minimize time at the end of the phase
phase.add_objective('time_phase', loc='final', scaler=10)
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 2.0
p['phase0.states:x'] = phase.interpolate(ys=[0, 10], nodes='state_input')
p['phase0.states:y'] = phase.interpolate(ys=[10, 5], nodes='state_input')
p['phase0.states:v'] = phase.interpolate(ys=[0, 9.9], nodes='state_input')
p['phase0.controls:theta'] = phase.interpolate(ys=[5, 100], nodes='control_input')
p['phase0.parameters:g'] = 9.80665
p.run_driver()
return p
def test_run_HS_problem_gl(self):
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
p.driver.declare_coloring()
_, optimizer = set_pyoptsparse_opt('SNOPT', fallback=True)
p.driver.options['optimizer'] = optimizer
traj = p.model.add_subsystem('traj', dm.Trajectory())
phase0 = traj.add_phase('phase0', dm.Phase(ode_class=HyperSensitiveODE,
transcription=dm.GaussLobatto(num_segments=30, order=3)))
phase0.set_time_options(fix_initial=True, fix_duration=True)
phase0.add_state('x', fix_initial=True, fix_final=False, rate_source='x_dot', targets=['x'])
phase0.add_state('xL', fix_initial=True, fix_final=False, rate_source='L', targets=['xL'])
phase0.add_control('u', opt=True, targets=['u'], rate_continuity=False)
phase0.add_boundary_constraint('x', loc='final', equals=1)
phase0.add_objective('xL', loc='final')
phase0.set_refine_options(refine=True)
p.setup(check=True)
tf = 100
p.set_val('traj.phase0.states:x', phase0.interpolate(ys=[1.5, 1], nodes='state_input'))
p.set_val('traj.phase0.states:xL', phase0.interpolate(ys=[0, 1], nodes='state_input'))
p.set_val('traj.phase0.t_initial', 0)
p.set_val('traj.phase0.t_duration', tf)
p.set_val('traj.phase0.controls:u', phase0.interpolate(ys=[-0.6, 2.4],
nodes='control_input'))
dm.run_problem(p, refine=True)
sqrt_two = np.sqrt(2)
val = sqrt_two * tf
c1 = (1.5 * np.exp(-val) - 1) / (np.exp(-val) - np.exp(val))
c2 = (1 - 1.5 * np.exp(val)) / (np.exp(-val) - np.exp(val))
ui = c1 * (1 + sqrt_two) + c2 * (1 - sqrt_two)
uf = c1 * (1 + sqrt_two) * np.exp(val) + c2 * (1 - sqrt_two) * np.exp(-val)
J = 0.5 * (c1 ** 2 * (1 + sqrt_two) * np.exp(2 * val) + c2 ** 2 * (1 - sqrt_two) * np.exp(-2 * val) -
(1 + sqrt_two) * c1 ** 2 - (1 - sqrt_two) * c2 ** 2)
assert_rel_error(self,
p.get_val('traj.phase0.timeseries.controls:u')[0],
ui,
tolerance=1e-2)
assert_rel_error(self,
p.get_val('traj.phase0.timeseries.controls:u')[-1],
uf,
tolerance=1e-2)
assert_rel_error(self,
p.get_val('traj.phase0.timeseries.states:xL')[-1],
J,
tolerance=5e-4)
def test_ex_two_burn_orbit_raise_connected(self):
_, optimizer = set_pyoptsparse_opt('SNOPT', fallback=False)
p = two_burn_orbit_raise_problem(transcription='gauss-lobatto', transcription_order=3,
compressed=False, optimizer=optimizer,
show_output=False, connected=True)
if p.model.traj.phases.burn2 in p.model.traj.phases._subsystems_myproc:
assert_rel_error(self, p.get_val('traj.burn2.states:deltav')[0], 0.3995,
tolerance=4.0E-3)
def make_problem(self, transcription=Radau, optimizer='SLSQP', numseg=30):
p = Problem(model=Group())
p.driver = pyOptSparseDriver()
p.driver.declare_coloring(tol=1.0E-12)
OPT, OPTIMIZER = set_pyoptsparse_opt(optimizer, fallback=False)
p.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
p.driver.opt_settings['iSumm'] = 6
p.driver.opt_settings['Verify level'] = 3
elif OPTIMIZER == 'IPOPT':
p.driver.opt_settings['max_iter'] = 500
p.driver.opt_settings['print_level'] = 4
p.driver.opt_settings['tol'] = 1.0E-6
p.driver.opt_settings['acceptable_tol'] = 1.0E-5
traj = p.model.add_subsystem('traj', Trajectory())
phase = traj.add_phase('phase', Phase(ode_class=RobotArmODE,
transcription=transcription(num_segments=numseg, order=3)))
phase.set_time_options(fix_initial=True, fix_duration=False)
phase.add_state('x0', fix_initial=True, fix_final=True, rate_source='x0_dot', units='m')
phase.add_state('x1', fix_initial=True, fix_final=True, rate_source='x1_dot', units='rad')
phase.add_state('x2', fix_initial=True, fix_final=True, rate_source='x2_dot', units='rad')
phase.add_state('x3', fix_initial=True, fix_final=True, rate_source='x3_dot', units='m/s')
phase.add_state('x4', fix_initial=True, fix_final=True, rate_source='x4_dot', units='rad/s')
phase.add_state('x5', fix_initial=True, fix_final=True, rate_source='x5_dot', units='rad/s')
phase.add_control('u0', opt=True, lower=-1, upper=1, scaler=0.1, units='m**2/s**2',
continuity=False, rate_continuity=False)
phase.add_control('u1', opt=True, lower=-1, upper=1, scaler=0.1, units='m**3*rad/s**2',
continuity=False, rate_continuity=False)
phase.add_control('u2', opt=True, lower=-1, upper=1, scaler=0.1, units='m**3*rad/s**2',
continuity=False, rate_continuity=False)
phase.add_path_constraint('u0', lower=-1, upper=1, scaler=0.1)
phase.add_path_constraint('u1', lower=-1, upper=1, scaler=0.1)
phase.add_path_constraint('u2', lower=-1, upper=1, scaler=0.1)
phase.add_objective('time', ref=0.1)
phase.set_refine_options(refine=True, tol=1e-5, smoothness_factor=1.2)
p.setup(check=True, force_alloc_complex=False, mode='auto')
p.set_val('traj.phase.t_initial', 0)
p.set_val('traj.phase.t_duration', 10)
p.set_val('traj.phase.states:x0', phase.interpolate(ys=[4.5, 4.5], nodes='state_input'))
p.set_val('traj.phase.states:x1', phase.interpolate(ys=[0.0, 2 * np.pi / 3], nodes='state_input'))
p.set_val('traj.phase.states:x2', phase.interpolate(ys=[np.pi / 4, np.pi / 4], nodes='state_input'))
p.set_val('traj.phase.states:x3', phase.interpolate(ys=[0.0, 0.0], nodes='state_input'))
p.set_val('traj.phase.states:x4', phase.interpolate(ys=[0.0, 0.0], nodes='state_input'))
p.set_val('traj.phase.states:x5', phase.interpolate(ys=[0.0, 0.0], nodes='state_input'))
return p
def test_ex_two_burn_orbit_raise(self):
_, optimizer = set_pyoptsparse_opt('SNOPT', fallback=False)
p = two_burn_orbit_raise_problem(transcription='gauss-lobatto',
transcription_order=3,
compressed=True,
show_plots=False,
optimizer=optimizer)
assert_rel_error(self,
p.get_val('burn2.states:deltav')[-1],
0.3995,
tolerance=2.0E-3)
def make_problem(self,
transcription=GaussLobatto,
optimizer='SLSQP',
numseg=30):
p = Problem(model=Group())
p.driver = pyOptSparseDriver()
p.driver.declare_coloring()
OPT, OPTIMIZER = set_pyoptsparse_opt(optimizer, fallback=False)
p.driver.options['optimizer'] = OPTIMIZER
traj = p.model.add_subsystem('traj', Trajectory())
phase0 = traj.add_phase(
'phase0',
Phase(ode_class=HyperSensitiveODE,
transcription=transcription(num_segments=numseg, order=3)))
phase0.set_time_options(fix_initial=True, fix_duration=True)
phase0.add_state('x',
fix_initial=True,
fix_final=False,
rate_source='x_dot',
targets=['x'])
phase0.add_state('xL',
fix_initial=True,
fix_final=False,
rate_source='L',
targets=['xL'])
phase0.add_control('u', opt=True, targets=['u'])
phase0.add_boundary_constraint('x', loc='final', equals=1)
phase0.add_objective('xL', loc='final')
p.setup(check=True)
p.set_val('traj.phase0.states:x',
phase0.interpolate(ys=[1.5, 1], nodes='state_input'))
p.set_val('traj.phase0.states:xL',
phase0.interpolate(ys=[0, 1], nodes='state_input'))
p.set_val('traj.phase0.t_initial', 0)
p.set_val('traj.phase0.t_duration', tf)
p.set_val('traj.phase0.controls:u',
phase0.interpolate(ys=[-0.6, 2.4], nodes='control_input'))
return p
def test_water_rocket_range_for_docs(self):
p = om.Problem(model=om.Group())
traj, phases = new_water_rocket_trajectory(objective='range')
traj = p.model.add_subsystem('traj', traj)
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=False)
p.driver = om.pyOptSparseDriver(optimizer='IPOPT')
p.driver.opt_settings['print_level'] = 5
p.driver.opt_settings['max_iter'] = 1000
p.driver.opt_settings['nlp_scaling_method'] = 'gradient-based'
p.driver.declare_coloring(tol=1.0E-12)
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
# p.driver.add_recorder(om.SqliteRecorder('ex_water_rocket.db'))
p.setup()
set_sane_initial_guesses(p, phases)
p.run_driver()
summary = summarize_results(p)
for key, entry in summary.items():
print(f'{key}: {entry.value:6.4f} {entry.unit}')
exp_out = traj.simulate(times_per_seg=200)
# NOTE: only the last figure is shown in the generated docs
plot_propelled_ascent(p, exp_out)
plot_trajectory(p, exp_out)
plot_states(p, exp_out)
plt.show()
# Check results (tolerance is relative unless value is zero)
assert_near_equal(summary['Launch angle'].value, 46, 0.02)
assert_near_equal(summary['Flight angle at end of propulsion'].value,
38, 0.02)
assert_near_equal(summary['Empty mass'].value, 0.189, 1e-2)
assert_near_equal(summary['Water volume'].value, 1.026, 1e-2)
assert_near_equal(summary['Maximum range'].value, 85.11, 1e-2)
assert_near_equal(summary['Maximum height'].value, 23.08, 1e-2)
assert_near_equal(summary['Maximum velocity'].value, 41.31, 1e-2)
def test_water_rocket_height_for_docs(self):
p = om.Problem(model=om.Group())
traj, phases = new_water_rocket_trajectory(objective='height')
traj = p.model.add_subsystem('traj', traj)
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=False)
p.driver = om.pyOptSparseDriver(optimizer='IPOPT')
p.driver.opt_settings['print_level'] = 5
p.driver.opt_settings['max_iter'] = 1000
p.driver.declare_coloring()
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup()
set_sane_initial_guesses(p, phases)
p.run_driver()
summary = summarize_results(p)
for key, entry in summary.items():
print(f'{key}: {entry.value:6.4f} {entry.unit}')
exp_out = traj.simulate(times_per_seg=200)
# NOTE: only the last figure is shown in the generated docs
plot_propelled_ascent(p, exp_out)
plot_trajectory(p, exp_out)
plot_states(p, exp_out)
plt.show()
# Check results (tolerance is relative unless value is zero)
assert_near_equal(summary['Launch angle'].value, 85, .02)
assert_near_equal(summary['Flight angle at end of propulsion'].value,
85, .02)
assert_near_equal(summary['Empty mass'].value, 0.1425114, 1e-3)
assert_near_equal(summary['Water volume'].value, 0.868281, 1e-3)
assert_near_equal(summary['Maximum range'].value, 15.78, 5)
assert_near_equal(summary['Maximum height'].value, 54.133184, 1e-3)
assert_near_equal(summary['Maximum velocity'].value, 47.320298, 1e-3)
import unittest
import numpy as np
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.general_utils import set_pyoptsparse_opt
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
import dymos as dm
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
from openmdao.utils.testing_utils import use_tempdirs
def make_traj(transcription='gauss-lobatto',
transcription_order=3,
compressed=False,
connected=False,
param_mode='param_sequence'):
t = {
'gauss-lobatto':
dm.GaussLobatto(num_segments=5,
order=transcription_order,
compressed=compressed),
'radau':
dm.Radau(num_segments=20,
order=transcription_order,
compressed=compressed),
'runge-kutta':
dm.RungeKutta(num_segments=5, compressed=compressed)
from openmdao.utils.assert_utils import assert_warning
from openmdao.utils.general_utils import set_pyoptsparse_opt
from openmdao.utils.reports_system import set_default_reports_dir, _reports_dir, register_report, \
list_reports, clear_reports, run_n2_report, setup_default_reports, report_function
from openmdao.utils.testing_utils import use_tempdirs
from openmdao.utils.mpi import MPI
from openmdao.utils.tests.test_hooks import hooks_active
from openmdao.visualization.n2_viewer.n2_viewer import _default_n2_filename
from openmdao.visualization.scaling_viewer.scaling_report import _default_scaling_filename
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
import dymos as dm
from dymos.examples.brachistochrone.brachistochrone_ode import BrachistochroneODE
@use_tempdirs
class TestSubproblemReportToggle(unittest.TestCase):
def setUp(self):
self.n2_filename = _default_n2_filename
self.scaling_filename = _default_scaling_filename
# set things to a known initial state for all the test runs
def test_distributed_list_vars(self):
from openmdao.utils.general_utils import set_pyoptsparse_opt
from openmdao.utils.mpi import MPI
if MPI:
from openmdao.api import PETScVector
vector_class = PETScVector
else:
PETScVector = None
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
else:
raise unittest.SkipTest("pyOptSparseDriver is required.")
from openmdao.core.parallel_group import ParallelGroup
from openmdao.components.exec_comp import ExecComp
class Mygroup(Group):
def setup(self):
self.add_subsystem('indep_var_comp', IndepVarComp('x'), promotes=['*'])
self.add_subsystem('Cy', ExecComp('y=2*x'), promotes=['*'])
self.add_subsystem('Cc', ExecComp('c=x+2'), promotes=['*'])
self.add_design_var('x')
self.add_constraint('c', lower=-3.)
prob = Problem()
prob.model.add_subsystem('par', ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.setup(vector_class=PETScVector)
prob.run_driver()
prob.cleanup()
stream = cStringIO()
inputs = sorted(prob.model.list_inputs(values=True, print_arrays=True, out_stream=stream))
self.assertEqual(inputs[0][0], 'Obj.y1')
self.assertEqual(inputs[1][0], 'Obj.y2')
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(inputs[2][0], 'par.G2.Cc.x')
self.assertEqual(inputs[3][0], 'par.G2.Cy.x')
else:
self.assertEqual(inputs[2][0], 'par.G1.Cc.x')
self.assertEqual(inputs[3][0], 'par.G1.Cy.x')
self.assertTrue('value' in inputs[0][1])
self.assertEqual(4, len(inputs))
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("6 Input(s) in 'model'"), 1)
self.assertEqual(1, text.count('value'))
self.assertEqual(1, text.count(' par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(4, text.count(' x'))
self.assertEqual(1, text.count(' Obj'))
self.assertEqual(1, text.count(' y1'))
self.assertEqual(1, text.count(' y2'))
stream = cStringIO()
outputs = sorted(prob.model.list_outputs(values=True,
units=True,
shape=True,
bounds=True,
residuals=True,
scaling=True,
hierarchical=True,
print_arrays=True,
out_stream=stream))
self.assertEqual(outputs[0][0], 'Obj.obj')
if prob.comm.rank: # outputs only return what is on their proc
self.assertEqual(outputs[1][0], 'par.G2.Cc.c')
self.assertEqual(outputs[2][0], 'par.G2.Cy.y')
self.assertEqual(outputs[3][0], 'par.G2.indep_var_comp.x')
else:
self.assertEqual(outputs[1][0], 'par.G1.Cc.c')
self.assertEqual(outputs[2][0], 'par.G1.Cy.y')
self.assertEqual(outputs[3][0], 'par.G1.indep_var_comp.x')
self.assertEqual(4, len(outputs))
self.assertTrue('value' in outputs[0][1])
self.assertTrue('units' in outputs[0][1])
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("7 Explicit Output(s) in 'model'"))
self.assertEqual(1, text.count('value'))
self.assertEqual(1, text.count('units'))
self.assertEqual(1, text.count(' par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(2, text.count(' indep_var_comp'))
self.assertEqual(2, text.count(' x'))
self.assertEqual(2, text.count(' y'))
self.assertEqual(2, text.count(' c'))
self.assertEqual(1, text.count(' Obj'))
self.assertEqual(1, text.count(' obj'))
def test_brachistochrone_for_docs_coloring_demo_solve_segments(self):
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
import dymos as dm
from dymos.examples.plotting import plot_results
from dymos.examples.brachistochrone import BrachistochroneODE
from openmdao.utils.general_utils import set_pyoptsparse_opt
#
# Initialize the Problem and the optimization driver
#
p = om.Problem(model=om.Group())
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
p.driver = om.pyOptSparseDriver(optimizer=optimizer)
p.driver.opt_settings['print_level'] = 5
# p.driver.declare_coloring()
#
# Create a trajectory and add a phase to it
#
traj = p.model.add_subsystem('traj', dm.Trajectory())
#
# In this case the phase has many segments to demonstrate the impact of coloring.
#
phase = traj.add_phase(
'phase0',
dm.Phase(ode_class=BrachistochroneODE,
transcription=dm.Radau(num_segments=100,
solve_segments=True)))
#
# Set the variables
#
phase.set_time_options(initial_bounds=(0, 0), duration_bounds=(.5, 10))
phase.add_state(
'x',
rate_source=BrachistochroneODE.states['x']['rate_source'],
units=BrachistochroneODE.states['x']['units'],
fix_initial=True)
phase.add_state(
'y',
rate_source=BrachistochroneODE.states['y']['rate_source'],
units=BrachistochroneODE.states['y']['units'],
fix_initial=True)
phase.add_state(
'v',
rate_source=BrachistochroneODE.states['v']['rate_source'],
targets=BrachistochroneODE.states['v']['targets'],
units=BrachistochroneODE.states['v']['units'],
fix_initial=True)
phase.add_control(
'theta',
targets=BrachistochroneODE.parameters['theta']['targets'],
continuity=True,
rate_continuity=True,
units='deg',
lower=0.01,
upper=179.9)
phase.add_parameter(
'g',
targets=BrachistochroneODE.parameters['g']['targets'],
units='m/s**2',
val=9.80665)
#
# Replace state terminal bounds with nonlinear constraints
#
phase.add_boundary_constraint('x', loc='final', equals=10)
phase.add_boundary_constraint('y', loc='final', equals=5)
#
# Minimize time at the end of the phase
#
phase.add_objective('time', loc='final', scaler=10)
p.model.linear_solver = om.DirectSolver()
#
# Setup the Problem
#
p.setup()
#
# Set the initial values
#
p['traj.phase0.t_initial'] = 0.0
p['traj.phase0.t_duration'] = 2.0
p['traj.phase0.states:x'] = phase.interpolate(ys=[0, 10],
nodes='state_input')
p['traj.phase0.states:y'] = phase.interpolate(ys=[10, 5],
nodes='state_input')
p['traj.phase0.states:v'] = phase.interpolate(ys=[0, 9.9],
nodes='state_input')
p['traj.phase0.controls:theta'] = phase.interpolate(
ys=[5, 100.5], nodes='control_input')
#
# Solve for the optimal trajectory
#
dm.run_problem(p)
# Test the results
assert_near_equal(p.get_val('traj.phase0.timeseries.time')[-1],
1.8016,
tolerance=1.0E-3)
# Generate the explicitly simulated trajectory
exp_out = traj.simulate()
plot_results(
[('traj.phase0.timeseries.states:x',
'traj.phase0.timeseries.states:y', 'x (m)', 'y (m)'),
('traj.phase0.timeseries.time',
'traj.phase0.timeseries.controls:theta', 'time (s)',
'theta (deg)')],
title='Brachistochrone Solution\nRadau Pseudospectral Method',
p_sol=p,
p_sim=exp_out)
plt.show()
def test_distributed_list_vars(self):
from openmdao.utils.general_utils import set_pyoptsparse_opt
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
else:
raise unittest.SkipTest("pyOptSparseDriver is required.")
from openmdao.core.parallel_group import ParallelGroup
from openmdao.components.exec_comp import ExecComp
class Mygroup(Group):
def setup(self):
self.add_subsystem('indep_var_comp', IndepVarComp('x'), promotes=['*'])
self.add_subsystem('Cy', ExecComp('y=2*x'), promotes=['*'])
self.add_subsystem('Cc', ExecComp('c=x+2'), promotes=['*'])
self.add_design_var('x')
self.add_constraint('c', lower=-3.)
prob = Problem()
prob.model.add_subsystem('par', ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.setup()
prob.run_driver()
prob.cleanup()
stream = cStringIO()
inputs = sorted(prob.model.list_inputs(values=True, print_arrays=True, out_stream=stream))
self.assertEqual(inputs[0][0], 'Obj.y1')
self.assertEqual(inputs[1][0], 'Obj.y2')
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(inputs[2][0], 'par.G2.Cc.x')
self.assertEqual(inputs[3][0], 'par.G2.Cy.x')
else:
self.assertEqual(inputs[2][0], 'par.G1.Cc.x')
self.assertEqual(inputs[3][0], 'par.G1.Cy.x')
self.assertTrue('value' in inputs[0][1])
self.assertEqual(4, len(inputs))
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("6 Input(s) in 'model'"), 1)
self.assertEqual(1, text.count('value'))
self.assertEqual(1, text.count(' par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(4, text.count(' x'))
self.assertEqual(1, text.count(' Obj'))
self.assertEqual(1, text.count(' y1'))
self.assertEqual(1, text.count(' y2'))
stream = cStringIO()
outputs = sorted(prob.model.list_outputs(values=True,
units=True,
shape=True,
bounds=True,
residuals=True,
scaling=True,
hierarchical=True,
print_arrays=True,
out_stream=stream))
self.assertEqual(outputs[0][0], 'Obj.obj')
if prob.comm.rank: # outputs only return what is on their proc
self.assertEqual(outputs[1][0], 'par.G2.Cc.c')
self.assertEqual(outputs[2][0], 'par.G2.Cy.y')
self.assertEqual(outputs[3][0], 'par.G2.indep_var_comp.x')
else:
self.assertEqual(outputs[1][0], 'par.G1.Cc.c')
self.assertEqual(outputs[2][0], 'par.G1.Cy.y')
self.assertEqual(outputs[3][0], 'par.G1.indep_var_comp.x')
self.assertEqual(4, len(outputs))
self.assertTrue('value' in outputs[0][1])
self.assertTrue('units' in outputs[0][1])
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("7 Explicit Output(s) in 'model'"))
self.assertEqual(1, text.count('value'))
self.assertEqual(1, text.count('units'))
self.assertEqual(1, text.count(' par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(2, text.count(' indep_var_comp'))
self.assertEqual(2, text.count(' x'))
self.assertEqual(2, text.count(' y'))
self.assertEqual(2, text.count(' c'))
self.assertEqual(1, text.count(' Obj'))
self.assertEqual(1, text.count(' obj'))
from openmdao.utils.assert_utils import assert_rel_error, assert_warning
from openmdao.utils.general_utils import set_pyoptsparse_opt
from openmdao.utils.coloring import get_simul_meta, _solves_info
from openmdao.utils.mpi import MPI
from openmdao.test_suite.tot_jac_builder import TotJacBuilder
import openmdao.test_suite
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
# check that pyoptsparse is installed
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
class CounterGroup(Group):
def __init__(self, *args, **kwargs):
self._solve_count = 0
super(CounterGroup, self).__init__(*args, **kwargs)
def _solve_linear(self, *args, **kwargs):
super(CounterGroup, self)._solve_linear(*args, **kwargs)
self._solve_count += 1
# note: size must be an even number
def test_two_burn_orbit_raise_gl_list_add_timeseries_output(self):
import numpy as np
import matplotlib.pyplot as plt
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.general_utils import set_pyoptsparse_opt
import dymos as dm
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
traj = dm.Trajectory()
p = om.Problem(model=om.Group())
p.model.add_subsystem('traj', traj)
p.driver = om.pyOptSparseDriver()
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
p.driver.options['optimizer'] = optimizer
p.driver.declare_coloring()
traj.add_parameter('c', opt=False, val=1.5, units='DU/TU',
targets={'burn1': ['c'], 'coast': ['c'], 'burn2': ['c']})
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.GaussLobatto(num_segments=10, order=3, compressed=True))
burn1 = traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', targets=['r'], units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', targets=['theta'], units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True, units='deg',
scaler=0.01,
rate_continuity_scaler=0.001, rate2_continuity_scaler=0.001,
lower=-30, upper=30, targets=['u1'])
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.RungeKutta(num_segments=20))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', targets=['r'], units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
units='rad', rate_source='theta_dot', targets=['theta'])
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=False, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_parameter('u1', targets=['u1'], opt=False, val=0.0, units='deg')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.GaussLobatto(num_segments=10, order=3, compressed=True))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', targets=['r'], units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', targets=['theta'], units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', targets=['u1'], rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_objective('deltav', loc='final', scaler=1.0)
# demonstrate adding multiple variables at once using list in add_timeseries_output
# test dict for units, ignore mismatched unit
burn1.add_timeseries_output(['pos_x', 'pos_y'], units={'pos_x': 'm', 'junk': 'lbm'})
coast.add_timeseries_output(['pos_x', 'pos_y'])
# test list for units
burn2.add_timeseries_output(['pos_x', 'pos_y'], units=['m', 'm'])
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True, force_alloc_complex=True)
# Set Initial Guesses
p.set_val('traj.parameters:c', value=1.5)
p.set_val('traj.burn1.t_initial', value=0.0)
p.set_val('traj.burn1.t_duration', value=2.25)
p.set_val('traj.burn1.states:r',
value=burn1.interpolate(ys=[1, 1.5], nodes='state_input'))
p.set_val('traj.burn1.states:theta',
value=burn1.interpolate(ys=[0, 1.7], nodes='state_input'))
p.set_val('traj.burn1.states:vr',
value=burn1.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('traj.burn1.states:vt',
value=burn1.interpolate(ys=[1, 1], nodes='state_input'))
p.set_val('traj.burn1.states:accel',
value=burn1.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn1.states:deltav',
value=burn1.interpolate(ys=[0, 0.1], nodes='state_input'), )
p.set_val('traj.burn1.controls:u1',
value=burn1.interpolate(ys=[-3.5, 13.0], nodes='control_input'))
p.set_val('traj.coast.t_initial', value=2.25)
p.set_val('traj.coast.t_duration', value=3.0)
p.set_val('traj.coast.states:r',
value=coast.interpolate(ys=[1.3, 1.5], nodes='state_input'))
p.set_val('traj.coast.states:theta',
value=coast.interpolate(ys=[2.1767, 1.7], nodes='state_input'))
p.set_val('traj.coast.states:vr',
value=coast.interpolate(ys=[0.3285, 0], nodes='state_input'))
p.set_val('traj.coast.states:vt',
value=coast.interpolate(ys=[0.97, 1], nodes='state_input'))
p.set_val('traj.coast.states:accel',
value=coast.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('traj.burn2.t_initial', value=5.25)
p.set_val('traj.burn2.t_duration', value=1.75)
p.set_val('traj.burn2.states:r',
value=burn2.interpolate(ys=[1.8, 3], nodes='state_input'))
p.set_val('traj.burn2.states:theta',
value=burn2.interpolate(ys=[3.2, 4.0], nodes='state_input'))
p.set_val('traj.burn2.states:vr',
value=burn2.interpolate(ys=[.5, 0], nodes='state_input'))
p.set_val('traj.burn2.states:vt',
value=burn2.interpolate(ys=[1, np.sqrt(1 / 3)], nodes='state_input'))
p.set_val('traj.burn2.states:accel',
value=burn2.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn2.states:deltav',
value=burn2.interpolate(ys=[0.1, 0.2], nodes='state_input'))
p.set_val('traj.burn2.controls:u1',
value=burn2.interpolate(ys=[1, 1], nodes='control_input'))
p.run_driver()
assert_near_equal(p.get_val('traj.burn2.timeseries.states:deltav')[-1],
0.3995,
tolerance=2.0E-3)
# check units
un = dict(p.model.list_outputs(units=True))
assert(un['traj.phases.burn1.timeseries.pos_x']['units'] == 'm')
assert(un['traj.phases.burn1.timeseries.pos_y']['units'] == 'DU')
assert(un['traj.phases.burn2.timeseries.pos_x']['units'] == 'm')
assert(un['traj.phases.burn2.timeseries.pos_y']['units'] == 'm')
def test_battery_power(self):
"""
for battery explicit integration testings
"""
_, local_opt = set_pyoptsparse_opt('SNOPT')
if local_opt != 'SNOPT':
raise unittest.SkipTest("pyoptsparse is not providing SNOPT")
p = om.Problem()
p.driver = om.pyOptSparseDriver()
p.driver.options['optimizer'] = 'SNOPT'
p.driver.opt_settings['Major iterations limit'] = 100
p.driver.opt_settings['Major optimality tolerance'] = 5.0E-3
p.driver.opt_settings['Major feasibility tolerance'] = 1e-6
p.driver.opt_settings['iSumm'] = 6
transcription = Radau(num_segments=15, order=3, compressed=True)
phase0 = Phase(transcription=transcription, ode_class=BatteryGroup)
phase0.set_time_options(fix_initial=True, duration_bounds=(30, 30))
p.model.add_subsystem(name='phase0', subsys=phase0)
phase0.add_state('SOC',
fix_initial=True,
rate_source='dXdt:SOC',
lower=0.0,
upper=1.)
phase0.add_state('U_Th',
units='V',
fix_initial=False,
rate_source='dXdt:V_{thev}',
lower=0.0,
upper=5.0)
# phase0.add_parameter('P_out', units='W', opt=False)
# phase0.add_boundary_constraint('U_pack', units='V', loc='initial', equals=5100)
phase0.add_objective('time', loc='final', ref=1)
p.model.linear_solver = om.DirectSolver(assemble_jac=True)
phase0.add_timeseries_output('Q_{batt}',
output_name='Q_{batt}',
units='W')
# phase0.add_timeseries_output('U_pack', output_name='V', units='V')
p.setup()
# p.check_partials()
T0 = 10 + 273
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 30
p['phase0.states:SOC'] = phase0.interpolate(ys=[1.0, 0.0],
nodes='state_input')
p['phase0.states:U_Th'] = phase0.interpolate(ys=[0.1, 0.1],
nodes='state_input')
# p['phase0.parameters:P_out'][:] = 72000.
p.run_driver()
fig, ax = plt.subplots(3, 1, sharex=True)
fig.suptitle('Temperature Plots')
t_opt = p.get_val('phase0.timeseries.time')
SOC_opt = p.get_val('phase0.timeseries.states:SOC', units=None)
Q_batt_opt = p.get_val('phase0.timeseries.Q_{batt}', units='kW')
ax[1].plot(t_opt, Q_batt_opt * 128 * 40, 'r', label='$Q_{cell}$')
ax[2].plot(t_opt, SOC_opt, 'r', label='$SOC$')
#spot check final values
# assert_rel_error(self, T_batt_opt[-1], 1.25934406, tolerance=1.0E-6)
# ax[3].plot(t_opt, V_opt, 'r', label='$Voltage$')
# axarr = fig.add_subplot(1, 2, 2)
# axarr.plot(sim_out.get_values('time'),sim_out.get_values('electric.battery.I_Li'), 'b')
# # # axarr.plot(p['phase0.state_interp.state_col:r'],
# # # p['phase0.controls:h'], 'bo', ms=4)
# axarr.set_ylabel('I_Li, amps')
# axarr.set_xlabel('time, s')
# axarr.axes.get_xaxis().set_visible(True)
import matplotlib
matplotlib.use(
'agg') # <--- comment out if you want to show this plot.
plt.show()
def _run_balanced_field_length_problem(tx=dm.GaussLobatto, timeseries=True, sim=True, solvesegs=False):
p = om.Problem()
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
p.driver = om.pyOptSparseDriver()
p.driver.declare_coloring()
# Use IPOPT if available, with fallback to SLSQP
p.driver.options['optimizer'] = optimizer
p.driver.options['print_results'] = False
if optimizer == 'IPOPT':
p.driver.opt_settings['print_level'] = 5
# First Phase: Brake release to V1 - both engines operable
br_to_v1 = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=tx(num_segments=3, solve_segments=solvesegs),
ode_init_kwargs={'mode': 'runway'})
br_to_v1.set_time_options(fix_initial=True, duration_bounds=(1, 1000), duration_ref=10.0)
br_to_v1.add_state('r', fix_initial=True, lower=0, ref=1000.0, defect_ref=1000.0)
br_to_v1.add_state('v', fix_initial=True, lower=0, ref=100.0, defect_ref=100.0)
br_to_v1.add_parameter('alpha', val=0.0, opt=False, units='deg')
if timeseries:
br_to_v1.add_timeseries_output('*')
# Second Phase: Rejected takeoff at V1 - no engines operable
rto = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3, solve_segments=solvesegs),
ode_init_kwargs={'mode': 'runway'})
rto.set_time_options(fix_initial=False, duration_bounds=(1, 1000), duration_ref=1.0)
rto.add_state('r', fix_initial=False, lower=0, ref=1000.0, defect_ref=1000.0)
rto.add_state('v', fix_initial=False, lower=0, ref=100.0, defect_ref=100.0)
rto.add_parameter('alpha', val=0.0, opt=False, units='deg')
if timeseries:
rto.add_timeseries_output('*')
# Third Phase: V1 to Vr - single engine operable
v1_to_vr = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3, solve_segments=solvesegs),
ode_init_kwargs={'mode': 'runway'})
v1_to_vr.set_time_options(fix_initial=False, duration_bounds=(1, 1000), duration_ref=1.0)
v1_to_vr.add_state('r', fix_initial=False, lower=0, ref=1000.0, defect_ref=1000.0)
v1_to_vr.add_state('v', fix_initial=False, lower=0, ref=100.0, defect_ref=100.0)
v1_to_vr.add_parameter('alpha', val=0.0, opt=False, units='deg')
if timeseries:
v1_to_vr.add_timeseries_output('*')
# Fourth Phase: Rotate - single engine operable
rotate = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3, solve_segments=solvesegs),
ode_init_kwargs={'mode': 'runway'})
rotate.set_time_options(fix_initial=False, duration_bounds=(1.0, 5), duration_ref=1.0)
rotate.add_state('r', fix_initial=False, lower=0, ref=1000.0, defect_ref=1000.0)
rotate.add_state('v', fix_initial=False, lower=0, ref=100.0, defect_ref=100.0)
rotate.add_polynomial_control('alpha', order=1, opt=True, units='deg', lower=0, upper=10,
ref=10, val=[0, 10])
if timeseries:
rotate.add_timeseries_output('*')
# Fifth Phase: Climb to target speed and altitude at end of runway.
climb = dm.Phase(ode_class=BalancedFieldODEComp, transcription=dm.Radau(num_segments=5),
ode_init_kwargs={'mode': 'climb'})
climb.set_time_options(fix_initial=False, duration_bounds=(1, 100), duration_ref=1.0)
climb.add_state('r', fix_initial=False, lower=0, ref=1000.0, defect_ref=1000.0)
climb.add_state('h', fix_initial=True, lower=0, ref=1.0, defect_ref=1.0)
climb.add_state('v', fix_initial=False, lower=0, ref=100.0, defect_ref=100.0)
climb.add_state('gam', fix_initial=True, lower=0, ref=0.05, defect_ref=0.05)
climb.add_control('alpha', opt=True, units='deg', lower=-10, upper=15, ref=10)
if timeseries:
climb.add_timeseries_output('*')
# Instantiate the trajectory and add phases
traj = dm.Trajectory()
p.model.add_subsystem('traj', traj)
traj.add_phase('br_to_v1', br_to_v1)
traj.add_phase('rto', rto)
traj.add_phase('v1_to_vr', v1_to_vr)
traj.add_phase('rotate', rotate)
traj.add_phase('climb', climb)
# Add parameters common to multiple phases to the trajectory
traj.add_parameter('m', val=174200., opt=False, units='lbm',
desc='aircraft mass',
targets={'br_to_v1': ['m'], 'v1_to_vr': ['m'], 'rto': ['m'],
'rotate': ['m'], 'climb': ['m']})
traj.add_parameter('T_nominal', val=27000 * 2, opt=False, units='lbf', dynamic=False,
desc='nominal aircraft thrust',
targets={'br_to_v1': ['T']})
traj.add_parameter('T_engine_out', val=27000, opt=False, units='lbf', dynamic=False,
desc='thrust under a single engine',
targets={'v1_to_vr': ['T'], 'rotate': ['T'], 'climb': ['T']})
traj.add_parameter('T_shutdown', val=0.0, opt=False, units='lbf', dynamic=False,
desc='thrust when engines are shut down for rejected takeoff',
targets={'rto': ['T']})
traj.add_parameter('mu_r_nominal', val=0.03, opt=False, units=None, dynamic=False,
desc='nominal runway friction coefficient',
targets={'br_to_v1': ['mu_r'], 'v1_to_vr': ['mu_r'], 'rotate': ['mu_r']})
traj.add_parameter('mu_r_braking', val=0.3, opt=False, units=None, dynamic=False,
desc='runway friction coefficient under braking',
targets={'rto': ['mu_r']})
traj.add_parameter('h_runway', val=0., opt=False, units='ft',
desc='runway altitude',
targets={'br_to_v1': ['h'], 'v1_to_vr': ['h'], 'rto': ['h'],
'rotate': ['h']})
traj.add_parameter('rho', val=1.225, opt=False, units='kg/m**3', dynamic=False,
desc='atmospheric density',
targets={'br_to_v1': ['rho'], 'v1_to_vr': ['rho'], 'rto': ['rho'],
'rotate': ['rho']})
traj.add_parameter('S', val=124.7, opt=False, units='m**2', dynamic=False,
desc='aerodynamic reference area',
targets={'br_to_v1': ['S'], 'v1_to_vr': ['S'], 'rto': ['S'],
'rotate': ['S'], 'climb': ['S']})
traj.add_parameter('CD0', val=0.03, opt=False, units=None, dynamic=False,
desc='zero-lift drag coefficient',
targets={f'{phase}': ['CD0'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('AR', val=9.45, opt=False, units=None, dynamic=False,
desc='wing aspect ratio',
targets={f'{phase}': ['AR'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('e', val=801, opt=False, units=None, dynamic=False,
desc='Oswald span efficiency factor',
targets={f'{phase}': ['e'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('span', val=35.7, opt=False, units='m', dynamic=False,
desc='wingspan',
targets={f'{phase}': ['span'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('h_w', val=1.0, opt=False, units='m', dynamic=False,
desc='height of wing above CG',
targets={f'{phase}': ['h_w'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('CL0', val=0.5, opt=False, units=None, dynamic=False,
desc='zero-alpha lift coefficient',
targets={f'{phase}': ['CL0'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('CL_max', val=2.0, opt=False, units=None, dynamic=False,
desc='maximum lift coefficient for linear fit',
targets={f'{phase}': ['CL_max'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
traj.add_parameter('alpha_max', val=10.0, opt=False, units='deg', dynamic=False,
desc='angle of attack at maximum lift',
targets={f'{phase}': ['alpha_max'] for phase in ['br_to_v1', 'v1_to_vr',
'rto', 'rotate' 'climb']})
# Standard "end of first phase to beginning of second phase" linkages
# Alpha changes from being a parameter in v1_to_vr to a polynomial control
# in rotate, to a dynamic control in `climb`.
traj.link_phases(['br_to_v1', 'v1_to_vr'], vars=['time', 'r', 'v'])
traj.link_phases(['v1_to_vr', 'rotate'], vars=['time', 'r', 'v', 'alpha'])
traj.link_phases(['rotate', 'climb'], vars=['time', 'r', 'v', 'alpha'])
traj.link_phases(['br_to_v1', 'rto'], vars=['time', 'r', 'v'])
# Less common "final value of r must be the match at ends of two phases".
traj.add_linkage_constraint(phase_a='rto', var_a='r', loc_a='final',
phase_b='climb', var_b='r', loc_b='final',
ref=1000)
# Define the constraints and objective for the optimal control problem
v1_to_vr.add_boundary_constraint('v_over_v_stall', loc='final', lower=1.2, ref=100)
rto.add_boundary_constraint('v', loc='final', equals=0., ref=100, linear=True)
rotate.add_boundary_constraint('F_r', loc='final', equals=0, ref=100000)
climb.add_boundary_constraint('h', loc='final', equals=35, ref=35, units='ft', linear=True)
climb.add_boundary_constraint('gam', loc='final', equals=5, ref=5, units='deg', linear=True)
climb.add_path_constraint('gam', lower=0, upper=5, ref=5, units='deg')
climb.add_boundary_constraint('v_over_v_stall', loc='final', lower=1.25, ref=1.25)
rto.add_objective('r', loc='final', ref=1000.0)
#
# Setup the problem and set the initial guess
#
p.setup(check=True)
p.set_val('traj.br_to_v1.t_initial', 0)
p.set_val('traj.br_to_v1.t_duration', 35)
p.set_val('traj.br_to_v1.states:r', br_to_v1.interpolate(ys=[0, 2500.0], nodes='state_input'))
p.set_val('traj.br_to_v1.states:v', br_to_v1.interpolate(ys=[0, 100.0], nodes='state_input'))
p.set_val('traj.br_to_v1.parameters:alpha', 0, units='deg')
p.set_val('traj.v1_to_vr.t_initial', 35)
p.set_val('traj.v1_to_vr.t_duration', 35)
p.set_val('traj.v1_to_vr.states:r', v1_to_vr.interpolate(ys=[2500, 300.0], nodes='state_input'))
p.set_val('traj.v1_to_vr.states:v', v1_to_vr.interpolate(ys=[100, 110.0], nodes='state_input'))
p.set_val('traj.v1_to_vr.parameters:alpha', 0.0, units='deg')
p.set_val('traj.rto.t_initial', 35)
p.set_val('traj.rto.t_duration', 35)
p.set_val('traj.rto.states:r', rto.interpolate(ys=[2500, 5000.0], nodes='state_input'))
p.set_val('traj.rto.states:v', rto.interpolate(ys=[110, 0], nodes='state_input'))
p.set_val('traj.rto.parameters:alpha', 0.0, units='deg')
p.set_val('traj.rotate.t_initial', 70)
p.set_val('traj.rotate.t_duration', 5)
p.set_val('traj.rotate.states:r', rotate.interpolate(ys=[1750, 1800.0], nodes='state_input'))
p.set_val('traj.rotate.states:v', rotate.interpolate(ys=[80, 85.0], nodes='state_input'))
p.set_val('traj.rotate.polynomial_controls:alpha', 0.0, units='deg')
p.set_val('traj.climb.t_initial', 75)
p.set_val('traj.climb.t_duration', 15)
p.set_val('traj.climb.states:r', climb.interpolate(ys=[5000, 5500.0], nodes='state_input'),
units='ft')
p.set_val('traj.climb.states:v', climb.interpolate(ys=[160, 170.0], nodes='state_input'),
units='kn')
p.set_val('traj.climb.states:h', climb.interpolate(ys=[0, 35.0], nodes='state_input'),
units='ft')
p.set_val('traj.climb.states:gam', climb.interpolate(ys=[0, 5.0], nodes='state_input'),
units='deg')
p.set_val('traj.climb.controls:alpha', 5.0, units='deg')
dm.run_problem(p, run_driver=True, simulate=sim)
# Test this example in Dymos' continuous integration
assert_near_equal(p.get_val('traj.rto.states:r')[-1], 2188.2, tolerance=0.01)
def make_problem(self,
constrained=True,
transcription=GaussLobatto,
optimizer='SLSQP',
numseg=30):
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
p.driver.declare_coloring()
OPT, OPTIMIZER = set_pyoptsparse_opt(optimizer, fallback=False)
p.driver.options['optimizer'] = OPTIMIZER
traj = p.model.add_subsystem('traj', Trajectory())
phase0 = traj.add_phase(
'phase0',
Phase(ode_class=ShuttleODE,
transcription=transcription(num_segments=numseg, order=3)))
phase0.set_time_options(fix_initial=True, units='s', duration_ref=200)
phase0.add_state('h',
fix_initial=True,
fix_final=True,
units='ft',
rate_source='hdot',
targets=['h'],
lower=0,
ref0=75000,
ref=300000,
defect_ref=1000)
phase0.add_state('gamma',
fix_initial=True,
fix_final=True,
units='rad',
rate_source='gammadot',
targets=['gamma'],
lower=-89. * np.pi / 180,
upper=89. * np.pi / 180)
phase0.add_state('phi',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='phidot',
lower=0,
upper=89. * np.pi / 180)
phase0.add_state('psi',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='psidot',
targets=['psi'],
lower=0,
upper=90. * np.pi / 180)
phase0.add_state('theta',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='thetadot',
targets=['theta'],
lower=-89. * np.pi / 180,
upper=89. * np.pi / 180)
phase0.add_state('v',
fix_initial=True,
fix_final=True,
units='ft/s',
rate_source='vdot',
targets=['v'],
lower=500,
ref0=2500,
ref=25000)
phase0.add_control('alpha',
units='rad',
opt=True,
lower=-np.pi / 2,
upper=np.pi / 2,
targets=['alpha'])
phase0.add_control('beta',
units='rad',
opt=True,
lower=-89 * np.pi / 180,
upper=1 * np.pi / 180,
targets=['beta'])
if constrained:
phase0.add_path_constraint('q', lower=0, upper=70, ref=70)
phase0.add_objective('theta', loc='final', ref=-0.01)
p.setup(check=True, force_alloc_complex=True)
p.set_val('traj.phase0.states:h',
phase0.interpolate(ys=[260000, 80000], nodes='state_input'),
units='ft')
p.set_val('traj.phase0.states:gamma',
phase0.interpolate(ys=[-1 * np.pi / 180, -5 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:phi',
phase0.interpolate(ys=[0, 75 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:psi',
phase0.interpolate(ys=[90 * np.pi / 180, 10 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:theta',
phase0.interpolate(ys=[0, 25 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:v',
phase0.interpolate(ys=[25600, 2500], nodes='state_input'),
units='ft/s')
p.set_val('traj.phase0.t_initial', 0, units='s')
p.set_val('traj.phase0.t_duration', 2000, units='s')
p.set_val('traj.phase0.controls:alpha',
phase0.interpolate(
ys=[17.4 * np.pi / 180, 17.4 * np.pi / 180],
nodes='control_input'),
units='rad')
p.set_val('traj.phase0.controls:beta',
phase0.interpolate(ys=[-75 * np.pi / 180, 0 * np.pi / 180],
nodes='control_input'),
units='rad')
return p
def test_distributed_list_vars(self):
from openmdao.utils.general_utils import set_pyoptsparse_opt
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
else:
raise unittest.SkipTest("pyOptSparseDriver is required.")
class Mygroup(om.Group):
def setup(self):
self.add_subsystem('indep_var_comp', om.IndepVarComp('x'), promotes=['*'])
self.add_subsystem('Cy', om.ExecComp('y=2*x'), promotes=['*'])
self.add_subsystem('Cc', om.ExecComp('c=x+2'), promotes=['*'])
self.add_design_var('x')
self.add_constraint('c', lower=-3.)
prob = om.Problem()
prob.model.add_subsystem('par', om.ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', om.ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.setup()
prob.run_driver()
prob.cleanup()
stream = StringIO()
with multi_proc_exception_check(prob.comm):
inputs = sorted(prob.model.list_inputs(values=True, print_arrays=True, out_stream=stream))
if prob.comm.rank:
self.assertEqual(inputs, [])
else:
inames = [t[0] for t in inputs]
self.assertEqual(inames, ['Obj.y1', 'Obj.y2', 'par.G1.Cc.x', 'par.G1.Cy.x', 'par.G2.Cc.x', 'par.G2.Cy.x'])
self.assertTrue('val' in inputs[0][1])
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("6 Input(s) in 'model'"), 1)
self.assertEqual(1, text.count('val'))
self.assertEqual(1, text.count('par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(4, text.count(' x'))
self.assertEqual(1, text.count('Obj'))
self.assertEqual(1, text.count(' y1'))
self.assertEqual(1, text.count(' y2'))
stream = StringIO()
with multi_proc_exception_check(prob.comm):
outputs = sorted(prob.model.list_outputs(values=True,
units=True,
shape=True,
bounds=True,
residuals=True,
scaling=True,
hierarchical=True,
print_arrays=True,
out_stream=stream))
onames = [t[0] for t in outputs]
if prob.comm.rank == 0:
self.assertEqual(onames, ['Obj.obj', 'par.G1.Cc.c', 'par.G1.Cy.y', 'par.G1.indep_var_comp.x', 'par.G2.Cc.c', 'par.G2.Cy.y', 'par.G2.indep_var_comp.x'])
self.assertTrue('val' in outputs[0][1])
self.assertTrue('units' in outputs[0][1])
else:
self.assertEqual(onames, [])
text = stream.getvalue()
if prob.comm.rank: # Only rank 0 prints
self.assertEqual(len(text), 0)
else:
self.assertEqual(1, text.count("7 Explicit Output(s) in 'model'"))
self.assertEqual(1, text.count('val'))
self.assertEqual(1, text.count('units'))
self.assertEqual(1, text.count('par'))
self.assertEqual(1, text.count(' G1'))
self.assertEqual(1, text.count(' G2'))
self.assertEqual(2, text.count(' Cy'))
self.assertEqual(2, text.count(' Cc'))
self.assertEqual(2, text.count(' indep_var_comp'))
self.assertEqual(2, text.count(' x'))
self.assertEqual(2, text.count(' y'))
self.assertEqual(2, text.count(' c'))
self.assertEqual(1, text.count('Obj'))
self.assertEqual(1, text.count(' obj'))
def test_two_burn_orbit_raise_radau_wildcard_add_timeseries_output(self):
import numpy as np
import matplotlib.pyplot as plt
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.general_utils import set_pyoptsparse_opt
import dymos as dm
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
traj = dm.Trajectory()
p = om.Problem(model=om.Group())
p.model.add_subsystem('traj', traj)
p.driver = om.pyOptSparseDriver()
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
p.driver.options['optimizer'] = optimizer
p.driver.declare_coloring()
traj.add_parameter('c', opt=False, val=1.5, units='DU/TU',
targets={'burn1': ['c'], 'coast': ['c'], 'burn2': ['c']})
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.Radau(num_segments=10, order=3, compressed=True))
burn1 = traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', targets=['r'], units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', targets=['theta'], units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True, units='deg',
scaler=0.01,
rate_continuity_scaler=0.001, rate2_continuity_scaler=0.001,
lower=-30, upper=30, targets=['u1'])
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.RungeKutta(num_segments=20))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', targets=['r'], units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
units='rad', rate_source='theta_dot', targets=['theta'])
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=False, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_parameter('u1', targets=['u1'], opt=False, val=0.0, units='deg')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE,
transcription=dm.Radau(num_segments=10, order=3, compressed=True))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', targets=['r'], units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', targets=['theta'], units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', targets=['vr'], units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', targets=['vt'], units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', targets=['accel'], units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', targets=['u1'], rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_objective('deltav', loc='final', scaler=1.0)
# demonstrate adding multiple variables at once using wildcard in add_timeseries_output
burn1.add_timeseries_output('pos_*') # match partial variable in ODE outputs (Radau)
coast.add_timeseries_output('pos_*') # match partial variable in ODE outputs (Runge Kutta)
burn2.add_timeseries_output('*') # add all ODE outputs
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True, force_alloc_complex=True)
# Set Initial Guesses
p.set_val('traj.parameters:c', value=1.5)
p.set_val('traj.burn1.t_initial', value=0.0)
p.set_val('traj.burn1.t_duration', value=2.25)
p.set_val('traj.burn1.states:r',
value=burn1.interpolate(ys=[1, 1.5], nodes='state_input'))
p.set_val('traj.burn1.states:theta',
value=burn1.interpolate(ys=[0, 1.7], nodes='state_input'))
p.set_val('traj.burn1.states:vr',
value=burn1.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('traj.burn1.states:vt',
value=burn1.interpolate(ys=[1, 1], nodes='state_input'))
p.set_val('traj.burn1.states:accel',
value=burn1.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn1.states:deltav',
value=burn1.interpolate(ys=[0, 0.1], nodes='state_input'), )
p.set_val('traj.burn1.controls:u1',
value=burn1.interpolate(ys=[-3.5, 13.0], nodes='control_input'))
p.set_val('traj.coast.t_initial', value=2.25)
p.set_val('traj.coast.t_duration', value=3.0)
p.set_val('traj.coast.states:r',
value=coast.interpolate(ys=[1.3, 1.5], nodes='state_input'))
p.set_val('traj.coast.states:theta',
value=coast.interpolate(ys=[2.1767, 1.7], nodes='state_input'))
p.set_val('traj.coast.states:vr',
value=coast.interpolate(ys=[0.3285, 0], nodes='state_input'))
p.set_val('traj.coast.states:vt',
value=coast.interpolate(ys=[0.97, 1], nodes='state_input'))
p.set_val('traj.coast.states:accel',
value=coast.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('traj.burn2.t_initial', value=5.25)
p.set_val('traj.burn2.t_duration', value=1.75)
p.set_val('traj.burn2.states:r',
value=burn2.interpolate(ys=[1.8, 3], nodes='state_input'))
p.set_val('traj.burn2.states:theta',
value=burn2.interpolate(ys=[3.2, 4.0], nodes='state_input'))
p.set_val('traj.burn2.states:vr',
value=burn2.interpolate(ys=[.5, 0], nodes='state_input'))
p.set_val('traj.burn2.states:vt',
value=burn2.interpolate(ys=[1, np.sqrt(1 / 3)], nodes='state_input'))
p.set_val('traj.burn2.states:accel',
value=burn2.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn2.states:deltav',
value=burn2.interpolate(ys=[0.1, 0.2], nodes='state_input'))
p.set_val('traj.burn2.controls:u1',
value=burn2.interpolate(ys=[1, 1], nodes='control_input'))
p.run_driver()
assert_near_equal(p.get_val('traj.burn2.timeseries.states:deltav')[-1],
0.3995,
tolerance=2.0E-3)
# test ODE output wildcard matching in solve_ivp
exp_out = traj.simulate()
for prob in (p, exp_out):
# check timeseries: pos_x = r * cos(theta) and pos_y = r * sin(theta)
for phs in ['burn1', 'coast', 'burn2']:
x = np.array(prob.get_val('traj.{0}.timeseries.pos_x'.format(phs))).flatten()
y = np.array(prob.get_val('traj.{0}.timeseries.pos_y'.format(phs))).flatten()
t = np.array(prob.get_val('traj.{0}.timeseries.states:theta'.format(phs))).flatten()
r = np.array(prob.get_val('traj.{0}.timeseries.states:r'.format(phs))).flatten()
xerr = x - r * np.cos(t)
yerr = y - r * np.sin(t)
assert_near_equal(np.sqrt(np.mean(xerr*xerr)), 0.0)
assert_near_equal(np.sqrt(np.mean(yerr*yerr)), 0.0)
def test_distributed_driver_debug_print_options(self):
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
else:
raise unittest.SkipTest("pyOptSparseDriver is required.")
class Mygroup(Group):
def setup(self):
self.add_subsystem('indep_var_comp',
IndepVarComp('x'),
promotes=['*'])
self.add_subsystem('Cy', ExecComp('y=2*x'), promotes=['*'])
self.add_subsystem('Cc', ExecComp('c=x+2'), promotes=['*'])
self.add_design_var('x')
self.add_constraint('c', lower=-3.)
prob = Problem()
prob.model.add_subsystem('par', ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['print_results'] = False
prob.driver.options['debug_print'] = [
'desvars', 'ln_cons', 'nl_cons', 'objs'
]
prob.setup()
stdout = sys.stdout
strout = StringIO()
sys.stdout = strout
sys.stdout = strout
try:
prob.run_driver()
finally:
sys.stdout = stdout
output = strout.getvalue().split('\n')
if MPI.COMM_WORLD.rank == 0:
# Just make sure we have more than one. Not sure we will always have the same number
# of iterations
self.assertTrue(
output.count("Design Vars") > 1,
"Should be more than one design vars header printed")
self.assertTrue(
output.count("Nonlinear constraints") > 1,
"Should be more than one nonlinear constraint header printed")
self.assertTrue(
output.count("Linear constraints") > 1,
"Should be more than one linear constraint header printed")
self.assertTrue(
output.count("Objectives") > 1,
"Should be more than one objective header printed")
self.assertTrue(
len([s for s in output if 'par.G1.indep_var_comp.x' in s]) > 1,
"Should be more than one par.G1.indep_var_comp.x printed")
self.assertTrue(
len([s for s in output if 'par.G2.indep_var_comp.x' in s]) > 1,
"Should be more than one par.G2.indep_var_comp.x printed")
self.assertTrue(
len([s for s in output if 'par.G1.Cc.c' in s]) > 1,
"Should be more than one par.G1.Cc.c printed")
self.assertTrue(
len([s for s in output if 'par.G2.Cc.c' in s]) > 1,
"Should be more than one par.G2.Cc.c printed")
self.assertTrue(
len([s for s in output if s.startswith('None')]) > 1,
"Should be more than one None printed")
self.assertTrue(
len([s for s in output if 'Obj.obj' in s]) > 1,
"Should be more than one Obj.obj printed")
else:
self.assertEqual(output, [''])
def test_balanced_field_length_for_docs(self):
import matplotlib.pyplot as plt
import openmdao.api as om
from openmdao.utils.general_utils import set_pyoptsparse_opt
import dymos as dm
from dymos.examples.balanced_field.balanced_field_ode import BalancedFieldODEComp
p = om.Problem()
_, optimizer = set_pyoptsparse_opt('IPOPT', fallback=True)
p.driver = om.pyOptSparseDriver()
p.driver.declare_coloring()
# Use IPOPT if available, with fallback to SLSQP
p.driver.options['optimizer'] = optimizer
p.driver.options['print_results'] = False
if optimizer == 'IPOPT':
p.driver.opt_settings['print_level'] = 5
p.driver.opt_settings['derivative_test'] = 'first-order'
# First Phase: Brake release to V1 - both engines operable
br_to_v1 = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3),
ode_init_kwargs={'mode': 'runway'})
br_to_v1.set_time_options(fix_initial=True,
duration_bounds=(1, 1000),
duration_ref=10.0)
br_to_v1.add_state('r',
fix_initial=True,
lower=0,
ref=1000.0,
defect_ref=1000.0)
br_to_v1.add_state('v',
fix_initial=True,
lower=0,
ref=100.0,
defect_ref=100.0)
br_to_v1.add_parameter('alpha', val=0.0, opt=False, units='deg')
br_to_v1.add_timeseries_output('*')
# Second Phase: Rejected takeoff at V1 - no engines operable
rto = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3),
ode_init_kwargs={'mode': 'runway'})
rto.set_time_options(fix_initial=False,
duration_bounds=(1, 1000),
duration_ref=1.0)
rto.add_state('r',
fix_initial=False,
lower=0,
ref=1000.0,
defect_ref=1000.0)
rto.add_state('v',
fix_initial=False,
lower=0,
ref=100.0,
defect_ref=100.0)
rto.add_parameter('alpha', val=0.0, opt=False, units='deg')
rto.add_timeseries_output('*')
# Third Phase: V1 to Vr - single engine operable
v1_to_vr = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3),
ode_init_kwargs={'mode': 'runway'})
v1_to_vr.set_time_options(fix_initial=False,
duration_bounds=(1, 1000),
duration_ref=1.0)
v1_to_vr.add_state('r',
fix_initial=False,
lower=0,
ref=1000.0,
defect_ref=1000.0)
v1_to_vr.add_state('v',
fix_initial=False,
lower=0,
ref=100.0,
defect_ref=100.0)
v1_to_vr.add_parameter('alpha', val=0.0, opt=False, units='deg')
v1_to_vr.add_timeseries_output('*')
# Fourth Phase: Rotate - single engine operable
rotate = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=3),
ode_init_kwargs={'mode': 'runway'})
rotate.set_time_options(fix_initial=False,
duration_bounds=(1.0, 5),
duration_ref=1.0)
rotate.add_state('r',
fix_initial=False,
lower=0,
ref=1000.0,
defect_ref=1000.0)
rotate.add_state('v',
fix_initial=False,
lower=0,
ref=100.0,
defect_ref=100.0)
rotate.add_polynomial_control('alpha',
order=1,
opt=True,
units='deg',
lower=0,
upper=10,
ref=10,
val=[0, 10])
rotate.add_timeseries_output('*')
# Fifth Phase: Climb to target speed and altitude at end of runway.
climb = dm.Phase(ode_class=BalancedFieldODEComp,
transcription=dm.Radau(num_segments=5),
ode_init_kwargs={'mode': 'climb'})
climb.set_time_options(fix_initial=False,
duration_bounds=(1, 100),
duration_ref=1.0)
climb.add_state('r',
fix_initial=False,
lower=0,
ref=1000.0,
defect_ref=1000.0)
climb.add_state('h',
fix_initial=True,
lower=0,
ref=1.0,
defect_ref=1.0)
climb.add_state('v',
fix_initial=False,
lower=0,
ref=100.0,
defect_ref=100.0)
climb.add_state('gam',
fix_initial=True,
lower=0,
ref=0.05,
defect_ref=0.05)
climb.add_control('alpha',
opt=True,
units='deg',
lower=-10,
upper=15,
ref=10)
climb.add_timeseries_output('*')
# Instantiate the trajectory and add phases
traj = dm.Trajectory()
p.model.add_subsystem('traj', traj)
traj.add_phase('br_to_v1', br_to_v1)
traj.add_phase('rto', rto)
traj.add_phase('v1_to_vr', v1_to_vr)
traj.add_phase('rotate', rotate)
traj.add_phase('climb', climb)
# Add parameters common to multiple phases to the trajectory
traj.add_parameter('m',
val=174200.,
opt=False,
units='lbm',
desc='aircraft mass',
targets={
'br_to_v1': ['m'],
'v1_to_vr': ['m'],
'rto': ['m'],
'rotate': ['m'],
'climb': ['m']
})
traj.add_parameter('T_nominal',
val=27000 * 2,
opt=False,
units='lbf',
dynamic=False,
desc='nominal aircraft thrust',
targets={'br_to_v1': ['T']})
traj.add_parameter('T_engine_out',
val=27000,
opt=False,
units='lbf',
dynamic=False,
desc='thrust under a single engine',
targets={
'v1_to_vr': ['T'],
'rotate': ['T'],
'climb': ['T']
})
traj.add_parameter(
'T_shutdown',
val=0.0,
opt=False,
units='lbf',
dynamic=False,
desc='thrust when engines are shut down for rejected takeoff',
targets={'rto': ['T']})
traj.add_parameter('mu_r_nominal',
val=0.03,
opt=False,
units=None,
dynamic=False,
desc='nominal runway friction coefficient',
targets={
'br_to_v1': ['mu_r'],
'v1_to_vr': ['mu_r'],
'rotate': ['mu_r']
})
traj.add_parameter('mu_r_braking',
val=0.3,
opt=False,
units=None,
dynamic=False,
desc='runway friction coefficient under braking',
targets={'rto': ['mu_r']})
traj.add_parameter('h_runway',
val=0.,
opt=False,
units='ft',
desc='runway altitude',
targets={
'br_to_v1': ['h'],
'v1_to_vr': ['h'],
'rto': ['h'],
'rotate': ['h']
})
traj.add_parameter('rho',
val=1.225,
opt=False,
units='kg/m**3',
dynamic=False,
desc='atmospheric density',
targets={
'br_to_v1': ['rho'],
'v1_to_vr': ['rho'],
'rto': ['rho'],
'rotate': ['rho']
})
traj.add_parameter('S',
val=124.7,
opt=False,
units='m**2',
dynamic=False,
desc='aerodynamic reference area',
targets={
'br_to_v1': ['S'],
'v1_to_vr': ['S'],
'rto': ['S'],
'rotate': ['S'],
'climb': ['S']
})
traj.add_parameter(
'CD0',
val=0.03,
opt=False,
units=None,
dynamic=False,
desc='zero-lift drag coefficient',
targets={
f'{phase}': ['CD0']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'AR',
val=9.45,
opt=False,
units=None,
dynamic=False,
desc='wing aspect ratio',
targets={
f'{phase}': ['AR']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'e',
val=801,
opt=False,
units=None,
dynamic=False,
desc='Oswald span efficiency factor',
targets={
f'{phase}': ['e']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'span',
val=35.7,
opt=False,
units='m',
dynamic=False,
desc='wingspan',
targets={
f'{phase}': ['span']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'h_w',
val=1.0,
opt=False,
units='m',
dynamic=False,
desc='height of wing above CG',
targets={
f'{phase}': ['h_w']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'CL0',
val=0.5,
opt=False,
units=None,
dynamic=False,
desc='zero-alpha lift coefficient',
targets={
f'{phase}': ['CL0']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'CL_max',
val=2.0,
opt=False,
units=None,
dynamic=False,
desc='maximum lift coefficient for linear fit',
targets={
f'{phase}': ['CL_max']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
traj.add_parameter(
'alpha_max',
val=10.0,
opt=False,
units='deg',
dynamic=False,
desc='angle of attack at maximum lift',
targets={
f'{phase}': ['alpha_max']
for phase in ['br_to_v1', 'v1_to_vr', 'rto', 'rotate'
'climb']
})
# Standard "end of first phase to beginning of second phase" linkages
# Alpha changes from being a parameter in v1_to_vr to a polynomial control
# in rotate, to a dynamic control in `climb`.
traj.link_phases(['br_to_v1', 'v1_to_vr'], vars=['time', 'r', 'v'])
traj.link_phases(['v1_to_vr', 'rotate'],
vars=['time', 'r', 'v', 'alpha'])
traj.link_phases(['rotate', 'climb'], vars=['time', 'r', 'v', 'alpha'])
traj.link_phases(['br_to_v1', 'rto'], vars=['time', 'r', 'v'])
# Less common "final value of r must be the match at ends of two phases".
traj.add_linkage_constraint(phase_a='rto',
var_a='r',
loc_a='final',
phase_b='climb',
var_b='r',
loc_b='final',
ref=1000)
# Define the constraints and objective for the optimal control problem
v1_to_vr.add_boundary_constraint('v_over_v_stall',
loc='final',
lower=1.2,
ref=100)
rto.add_boundary_constraint('v',
loc='final',
equals=0.,
ref=100,
linear=True)
rotate.add_boundary_constraint('F_r',
loc='final',
equals=0,
ref=100000)
climb.add_boundary_constraint('h',
loc='final',
equals=35,
ref=35,
units='ft',
linear=True)
climb.add_boundary_constraint('gam',
loc='final',
equals=5,
ref=5,
units='deg',
linear=True)
climb.add_path_constraint('gam', lower=0, upper=5, ref=5, units='deg')
climb.add_boundary_constraint('v_over_v_stall',
loc='final',
lower=1.25,
ref=1.25)
rto.add_objective('r', loc='final', ref=1000.0)
#
# Setup the problem and set the initial guess
#
p.setup(check=True)
p.set_val('traj.br_to_v1.t_initial', 0)
p.set_val('traj.br_to_v1.t_duration', 35)
p.set_val('traj.br_to_v1.states:r',
br_to_v1.interpolate(ys=[0, 2500.0], nodes='state_input'))
p.set_val('traj.br_to_v1.states:v',
br_to_v1.interpolate(ys=[0, 100.0], nodes='state_input'))
p.set_val('traj.br_to_v1.parameters:alpha', 0, units='deg')
p.set_val('traj.v1_to_vr.t_initial', 35)
p.set_val('traj.v1_to_vr.t_duration', 35)
p.set_val('traj.v1_to_vr.states:r',
v1_to_vr.interpolate(ys=[2500, 300.0], nodes='state_input'))
p.set_val('traj.v1_to_vr.states:v',
v1_to_vr.interpolate(ys=[100, 110.0], nodes='state_input'))
p.set_val('traj.v1_to_vr.parameters:alpha', 0.0, units='deg')
p.set_val('traj.rto.t_initial', 35)
p.set_val('traj.rto.t_duration', 35)
p.set_val('traj.rto.states:r',
rto.interpolate(ys=[2500, 5000.0], nodes='state_input'))
p.set_val('traj.rto.states:v',
rto.interpolate(ys=[110, 0], nodes='state_input'))
p.set_val('traj.rto.parameters:alpha', 0.0, units='deg')
p.set_val('traj.rotate.t_initial', 70)
p.set_val('traj.rotate.t_duration', 5)
p.set_val('traj.rotate.states:r',
rotate.interpolate(ys=[1750, 1800.0], nodes='state_input'))
p.set_val('traj.rotate.states:v',
rotate.interpolate(ys=[80, 85.0], nodes='state_input'))
p.set_val('traj.rotate.polynomial_controls:alpha', 0.0, units='deg')
p.set_val('traj.climb.t_initial', 75)
p.set_val('traj.climb.t_duration', 15)
p.set_val('traj.climb.states:r',
climb.interpolate(ys=[5000, 5500.0], nodes='state_input'),
units='ft')
p.set_val('traj.climb.states:v',
climb.interpolate(ys=[160, 170.0], nodes='state_input'),
units='kn')
p.set_val('traj.climb.states:h',
climb.interpolate(ys=[0, 35.0], nodes='state_input'),
units='ft')
p.set_val('traj.climb.states:gam',
climb.interpolate(ys=[0, 5.0], nodes='state_input'),
units='deg')
p.set_val('traj.climb.controls:alpha', 5.0, units='deg')
dm.run_problem(p, run_driver=True, simulate=True)
sim_case = om.CaseReader('dymos_solution.db').get_case('final')
fig, axes = plt.subplots(2, 1, sharex=True, gridspec_kw={'top': 0.92})
for phase in ['br_to_v1', 'rto', 'v1_to_vr', 'rotate', 'climb']:
r = sim_case.get_val(f'traj.{phase}.timeseries.states:r',
units='ft')
v = sim_case.get_val(f'traj.{phase}.timeseries.states:v',
units='kn')
t = sim_case.get_val(f'traj.{phase}.timeseries.time', units='s')
axes[0].plot(t, r, '-', label=phase)
axes[1].plot(t, v, '-', label=phase)
fig.suptitle('Balanced Field Length')
axes[1].set_xlabel('time (s)')
axes[0].set_ylabel('range (ft)')
axes[1].set_ylabel('airspeed (kts)')
axes[0].grid(True)
axes[1].grid(True)
tv1 = sim_case.get_val('traj.br_to_v1.timeseries.time', units='s')[-1,
0]
v1 = sim_case.get_val('traj.br_to_v1.timeseries.states:v',
units='kn')[-1, 0]
tf_rto = sim_case.get_val('traj.rto.timeseries.time', units='s')[-1, 0]
rf_rto = sim_case.get_val('traj.rto.timeseries.states:r',
units='ft')[-1, 0]
axes[0].annotate(f'field length = {r[-1, 0]:5.1f} ft',
xy=(t[-1, 0], r[-1, 0]),
xycoords='data',
xytext=(0.7, 0.5),
textcoords='axes fraction',
arrowprops=dict(arrowstyle='->'),
horizontalalignment='center',
verticalalignment='top')
axes[0].annotate(f'',
xy=(tf_rto, rf_rto),
xycoords='data',
xytext=(0.7, 0.5),
textcoords='axes fraction',
arrowprops=dict(arrowstyle='->'),
horizontalalignment='center',
verticalalignment='top')
axes[1].annotate(f'$v1$ = {v1:5.1f} kts',
xy=(tv1, v1),
xycoords='data',
xytext=(0.5, 0.5),
textcoords='axes fraction',
arrowprops=dict(arrowstyle='->'),
horizontalalignment='center',
verticalalignment='top')
plt.legend()
plt.show()
import os
import unittest
from numpy.testing import assert_almost_equal
import openmdao.api as om
import dymos as dm
from dymos.examples.brachistochrone.brachistochrone_ode import BrachistochroneODE
from openmdao.utils.general_utils import set_pyoptsparse_opt
from openmdao.utils.testing_utils import use_tempdirs
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT', fallback=True)
@use_tempdirs
class TestBrachistochroneRefineGrid(unittest.TestCase):
def make_problem(self,
transcription='radau-ps',
num_segments=5,
transcription_order=3,
compressed=True):
self.p = p = om.Problem(model=om.Group())
p.driver = om.ScipyOptimizeDriver()
p.driver.declare_coloring()
if transcription == 'gauss-lobatto':
t = dm.GaussLobatto(num_segments=num_segments,
order=transcription_order,
compressed=compressed)
def ex_aircraft_steady_flight(optimizer='SLSQP',
solve_segments=False,
use_boundary_constraints=False,
compressed=False):
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
_, optimizer = set_pyoptsparse_opt(optimizer, fallback=False)
p.driver.options['optimizer'] = optimizer
p.driver.declare_coloring()
if optimizer == 'SNOPT':
p.driver.opt_settings['Major iterations limit'] = 20
p.driver.opt_settings['Major feasibility tolerance'] = 1.0E-6
p.driver.opt_settings['Major optimality tolerance'] = 1.0E-6
p.driver.opt_settings["Linesearch tolerance"] = 0.10
p.driver.opt_settings['iSumm'] = 6
if optimizer == 'SLSQP':
p.driver.opt_settings['MAXIT'] = 50
num_seg = 15
seg_ends, _ = lgl(num_seg + 1)
phase = dm.Phase(ode_class=AircraftODE,
transcription=dm.Radau(num_segments=num_seg,
segment_ends=seg_ends,
order=3,
compressed=compressed,
solve_segments=solve_segments))
# Pass Reference Area from an external source
assumptions = p.model.add_subsystem('assumptions', om.IndepVarComp())
assumptions.add_output('S', val=427.8, units='m**2')
assumptions.add_output('mass_empty', val=1.0, units='kg')
assumptions.add_output('mass_payload', val=1.0, units='kg')
p.model.add_subsystem('phase0', phase)
phase.set_time_options(initial_bounds=(0, 0),
duration_bounds=(300, 10000),
duration_ref=5600)
fix_final = True
if use_boundary_constraints:
fix_final = False
phase.add_boundary_constraint('mass_fuel',
loc='final',
units='lbm',
equals=1e-3,
linear=False)
phase.add_boundary_constraint('alt',
loc='final',
units='kft',
equals=10.0,
linear=False)
phase.add_state('range',
units='NM',
rate_source='range_rate_comp.dXdt:range',
fix_initial=True,
fix_final=False,
ref=1e-3,
defect_ref=1e-3,
lower=0,
upper=2000)
phase.add_state('mass_fuel',
units='lbm',
rate_source='propulsion.dXdt:mass_fuel',
targets=['mass_comp.mass_fuel'],
fix_initial=True,
fix_final=fix_final,
upper=1.5E5,
lower=0.0,
ref=1e2,
defect_ref=1e2)
phase.add_state('alt',
units='kft',
rate_source='climb_rate',
targets=['atmos.h', 'aero.alt', 'propulsion.alt'],
fix_initial=True,
fix_final=fix_final,
lower=0.0,
upper=60,
ref=1e-3,
defect_ref=1e-3)
phase.add_control('climb_rate',
units='ft/min',
opt=True,
lower=-3000,
upper=3000,
targets=['gam_comp.climb_rate'],
rate_continuity=True,
rate2_continuity=False)
phase.add_control('mach',
targets=['tas_comp.mach', 'aero.mach'],
units=None,
opt=False)
phase.add_input_parameter(
'S',
targets=['aero.S', 'flight_equilibrium.S', 'propulsion.S'],
units='m**2')
phase.add_input_parameter('mass_empty',
targets=['mass_comp.mass_empty'],
units='kg')
phase.add_input_parameter('mass_payload',
targets=['mass_comp.mass_payload'],
units='kg')
phase.add_path_constraint('propulsion.tau',
lower=0.01,
upper=2.0,
shape=(1, ))
p.model.connect('assumptions.S', 'phase0.input_parameters:S')
p.model.connect('assumptions.mass_empty',
'phase0.input_parameters:mass_empty')
p.model.connect('assumptions.mass_payload',
'phase0.input_parameters:mass_payload')
phase.add_objective('range', loc='final', ref=-1.0e-4)
p.setup()
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 3600.0
p['phase0.states:range'][:] = phase.interpolate(ys=(0, 724.0),
nodes='state_input')
p['phase0.states:mass_fuel'][:] = phase.interpolate(ys=(30000, 1e-3),
nodes='state_input')
p['phase0.states:alt'][:] = 10.0
p['phase0.controls:mach'][:] = 0.8
p['assumptions.S'] = 427.8
p['assumptions.mass_empty'] = 0.15E6
p['assumptions.mass_payload'] = 84.02869 * 400
dm.run_problem(p)
return p
def test_two_burn_orbit_raise_for_docs(self):
import numpy as np
import matplotlib.pyplot as plt
from openmdao.api import Problem, pyOptSparseDriver, DirectSolver, SqliteRecorder
from openmdao.utils.assert_utils import assert_rel_error
from openmdao.utils.general_utils import set_pyoptsparse_opt
from dymos import Phase, Trajectory
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
traj = Trajectory()
p = Problem(model=traj)
p.driver = pyOptSparseDriver()
_, optimizer = set_pyoptsparse_opt('SNOPT', fallback=False)
p.driver.options['optimizer'] = 'SNOPT'
p.driver.options['dynamic_simul_derivs'] = True
traj.add_design_parameter('c', opt=False, val=1.5)
# First Phase (burn)
burn1 = Phase('gauss-lobatto',
ode_class=FiniteBurnODE,
num_segments=10,
transcription_order=3,
compressed=True)
burn1 = traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10))
burn1.set_state_options('r',
fix_initial=True,
fix_final=False,
defect_scaler=100.0)
burn1.set_state_options('theta',
fix_initial=True,
fix_final=False,
defect_scaler=100.0)
burn1.set_state_options('vr',
fix_initial=True,
fix_final=False,
defect_scaler=100.0)
burn1.set_state_options('vt',
fix_initial=True,
fix_final=False,
defect_scaler=100.0)
burn1.set_state_options('accel', fix_initial=True, fix_final=False)
burn1.set_state_options('deltav', fix_initial=True, fix_final=False)
burn1.add_control('u1',
rate_continuity=True,
rate2_continuity=True,
units='deg',
scaler=0.01,
rate_continuity_scaler=0.001,
rate2_continuity_scaler=0.001,
lower=-30,
upper=30)
# Second Phase (Coast)
coast = Phase('gauss-lobatto',
ode_class=FiniteBurnODE,
num_segments=10,
transcription_order=3,
compressed=True)
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20),
duration_bounds=(.5, 10),
duration_ref=10)
coast.set_state_options('r',
fix_initial=False,
fix_final=False,
defect_scaler=100.0)
coast.set_state_options('theta',
fix_initial=False,
fix_final=False,
defect_scaler=100.0)
coast.set_state_options('vr',
fix_initial=False,
fix_final=False,
defect_scaler=100.0)
coast.set_state_options('vt',
fix_initial=False,
fix_final=False,
defect_scaler=100.0)
coast.set_state_options('accel', fix_initial=True, fix_final=True)
coast.set_state_options('deltav', fix_initial=False, fix_final=False)
coast.add_control('u1', opt=False, val=0.0, units='deg')
# Third Phase (burn)
burn2 = Phase('gauss-lobatto',
ode_class=FiniteBurnODE,
num_segments=10,
transcription_order=3,
compressed=True)
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20),
duration_bounds=(.5, 10),
initial_ref=10)
burn2.set_state_options('r',
fix_initial=False,
fix_final=True,
defect_scaler=100.0)
burn2.set_state_options('theta',
fix_initial=False,
fix_final=False,
defect_scaler=100.0)
burn2.set_state_options('vr',
fix_initial=False,
fix_final=True,
defect_scaler=100.0)
burn2.set_state_options('vt',
fix_initial=False,
fix_final=True,
defect_scaler=100.0)
burn2.set_state_options('accel',
fix_initial=False,
fix_final=False,
defect_scaler=1.0)
burn2.set_state_options('deltav',
fix_initial=False,
fix_final=False,
defect_scaler=1.0)
burn2.add_control('u1',
rate_continuity=True,
rate2_continuity=True,
units='deg',
scaler=0.01,
rate_continuity_scaler=0.001,
rate2_continuity_scaler=0.001,
lower=-30,
upper=30)
burn2.add_objective('deltav', loc='final', scaler=1.0)
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
# Finish Problem Setup
p.model.options['assembled_jac_type'] = 'csc'
p.model.linear_solver = DirectSolver(assemble_jac=True)
p.driver.add_recorder(
SqliteRecorder('two_burn_orbit_raise_example_for_docs.db'))
p.setup(check=True)
# Set Initial Guesses
p.set_val('design_parameters:c', value=1.5)
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r',
value=burn1.interpolate(ys=[1, 1.5], nodes='state_input'))
p.set_val('burn1.states:theta',
value=burn1.interpolate(ys=[0, 1.7], nodes='state_input'))
p.set_val('burn1.states:vr',
value=burn1.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('burn1.states:vt',
value=burn1.interpolate(ys=[1, 1], nodes='state_input'))
p.set_val('burn1.states:accel',
value=burn1.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val(
'burn1.states:deltav',
value=burn1.interpolate(ys=[0, 0.1], nodes='state_input'),
)
p.set_val('burn1.controls:u1',
value=burn1.interpolate(ys=[-3.5, 13.0],
nodes='control_input'))
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r',
value=coast.interpolate(ys=[1.3, 1.5], nodes='state_input'))
p.set_val('coast.states:theta',
value=coast.interpolate(ys=[2.1767, 1.7],
nodes='state_input'))
p.set_val('coast.states:vr',
value=coast.interpolate(ys=[0.3285, 0], nodes='state_input'))
p.set_val('coast.states:vt',
value=coast.interpolate(ys=[0.97, 1], nodes='state_input'))
p.set_val('coast.states:accel',
value=coast.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('coast.controls:u1',
value=coast.interpolate(ys=[0, 0], nodes='control_input'))
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r',
value=burn2.interpolate(ys=[1, 3], nodes='state_input'))
p.set_val('burn2.states:theta',
value=burn2.interpolate(ys=[0, 4.0], nodes='state_input'))
p.set_val('burn2.states:vr',
value=burn2.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('burn2.states:vt',
value=burn2.interpolate(ys=[1, np.sqrt(1 / 3)],
nodes='state_input'))
p.set_val('burn2.states:accel',
value=burn2.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('burn2.states:deltav',
value=burn2.interpolate(ys=[0.1, 0.2], nodes='state_input'))
p.set_val('burn2.controls:u1',
value=burn2.interpolate(ys=[1, 1], nodes='control_input'))
p.run_driver()
assert_rel_error(self,
traj.get_values('deltav', flat=True)[-1],
0.3995,
tolerance=2.0E-3)
# Plot results
exp_out = traj.simulate(times=50, num_procs=3)
fig = plt.figure(figsize=(8, 4))
fig.suptitle('Two Burn Orbit Raise Solution')
ax_u1 = plt.subplot2grid((2, 2), (0, 0))
ax_deltav = plt.subplot2grid((2, 2), (1, 0))
ax_xy = plt.subplot2grid((2, 2), (0, 1), rowspan=2)
span = np.linspace(0, 2 * np.pi, 100)
ax_xy.plot(np.cos(span), np.sin(span), 'k--', lw=1)
ax_xy.plot(3 * np.cos(span), 3 * np.sin(span), 'k--', lw=1)
ax_xy.set_xlim(-4.5, 4.5)
ax_xy.set_ylim(-4.5, 4.5)
ax_xy.set_xlabel('x ($R_e$)')
ax_xy.set_ylabel('y ($R_e$)')
ax_u1.set_xlabel('time ($TU$)')
ax_u1.set_ylabel('$u_1$ ($deg$)')
ax_u1.grid(True)
ax_deltav.set_xlabel('time ($TU$)')
ax_deltav.set_ylabel('${\Delta}v$ ($DU/TU$)')
ax_deltav.grid(True)
t_sol = traj.get_values('time')
x_sol = traj.get_values('pos_x')
y_sol = traj.get_values('pos_y')
dv_sol = traj.get_values('deltav')
u1_sol = traj.get_values('u1', units='deg')
t_exp = exp_out.get_values('time')
x_exp = exp_out.get_values('pos_x')
y_exp = exp_out.get_values('pos_y')
dv_exp = exp_out.get_values('deltav')
u1_exp = exp_out.get_values('u1', units='deg')
for phase_name in ['burn1', 'coast', 'burn2']:
ax_u1.plot(t_sol[phase_name], u1_sol[phase_name], 'ro', ms=3)
ax_u1.plot(t_exp[phase_name], u1_exp[phase_name], 'b-')
ax_deltav.plot(t_sol[phase_name], dv_sol[phase_name], 'ro', ms=3)
ax_deltav.plot(t_exp[phase_name], dv_exp[phase_name], 'b-')
ax_xy.plot(x_sol[phase_name],
y_sol[phase_name],
'ro',
ms=3,
label='implicit' if phase_name == 'burn1' else None)
ax_xy.plot(x_exp[phase_name],
y_exp[phase_name],
'b-',
label='explicit' if phase_name == 'burn1' else None)
plt.show()
def test_two_burn_orbit_raise_gl_rk_gl_constrained(self):
import numpy as np
import matplotlib.pyplot as plt
from openmdao.api import Problem, Group, pyOptSparseDriver, DirectSolver
from openmdao.utils.assert_utils import assert_rel_error
from openmdao.utils.general_utils import set_pyoptsparse_opt
from dymos import Phase, GaussLobatto, RungeKutta, Trajectory
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
traj = Trajectory()
p = Problem(model=Group())
p.model.add_subsystem('traj', traj)
p.driver = pyOptSparseDriver()
_, optimizer = set_pyoptsparse_opt('SNOPT', fallback=True)
p.driver.options['optimizer'] = optimizer
p.driver.options['dynamic_simul_derivs'] = True
traj.add_design_parameter('c', opt=False, val=1.5, units='DU/TU')
# First Phase (burn)
burn1 = Phase(ode_class=FiniteBurnODE,
transcription=GaussLobatto(num_segments=10, order=3, compressed=True))
burn1 = traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10))
burn1.set_state_options('r', fix_initial=True, fix_final=False)
burn1.set_state_options('theta', fix_initial=True, fix_final=False)
burn1.set_state_options('vr', fix_initial=True, fix_final=False)
burn1.set_state_options('vt', fix_initial=True, fix_final=False)
burn1.set_state_options('accel', fix_initial=True, fix_final=False)
burn1.set_state_options('deltav', fix_initial=True, fix_final=False)
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True, units='deg',
scaler=0.01, lower=-30, upper=30)
# Second Phase (Coast)
coast = Phase(ode_class=FiniteBurnODE,
transcription=RungeKutta(num_segments=20, compressed=True))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10)
coast.set_state_options('r', fix_initial=False, fix_final=False)
coast.set_state_options('theta', fix_initial=False, fix_final=False)
coast.set_state_options('vr', fix_initial=False, fix_final=False)
coast.set_state_options('vt', fix_initial=False, fix_final=False)
coast.set_state_options('accel', fix_initial=True, fix_final=False)
coast.set_state_options('deltav', fix_initial=False, fix_final=False)
coast.add_design_parameter('u1', opt=False, val=0.0)
# Third Phase (burn)
burn2 = Phase(ode_class=FiniteBurnODE,
transcription=GaussLobatto(num_segments=10, order=3, compressed=True))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10)
burn2.set_state_options('r', fix_initial=False, fix_final=True)
burn2.set_state_options('theta', fix_initial=False, fix_final=False)
burn2.set_state_options('vr', fix_initial=False, fix_final=True)
burn2.set_state_options('vt', fix_initial=False, fix_final=True)
burn2.set_state_options('accel', fix_initial=False, fix_final=False, defect_scaler=1.0)
burn2.set_state_options('deltav', fix_initial=False, fix_final=False, defect_scaler=1.0)
burn2.add_control('u1', rate_continuity=True, rate2_continuity=True, units='deg',
scaler=0.01, lower=-30, upper=30)
burn2.add_objective('deltav', loc='final', scaler=1.0)
burn1.add_timeseries_output('pos_x', units='DU')
coast.add_timeseries_output('pos_x', units='DU')
burn2.add_timeseries_output('pos_x', units='DU')
burn1.add_timeseries_output('pos_y', units='DU')
coast.add_timeseries_output('pos_y', units='DU')
burn2.add_timeseries_output('pos_y', units='DU')
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
# Finish Problem Setup
p.model.linear_solver = DirectSolver()
p.setup(check=True, force_alloc_complex=True)
# Set Initial Guesses
p.set_val('traj.design_parameters:c', value=1.5)
p.set_val('traj.burn1.t_initial', value=0.0)
p.set_val('traj.burn1.t_duration', value=2.25)
p.set_val('traj.burn1.states:r',
value=burn1.interpolate(ys=[1, 1.5], nodes='state_input'))
p.set_val('traj.burn1.states:theta',
value=burn1.interpolate(ys=[0, 1.7], nodes='state_input'))
p.set_val('traj.burn1.states:vr',
value=burn1.interpolate(ys=[0, 0], nodes='state_input'))
p.set_val('traj.burn1.states:vt',
value=burn1.interpolate(ys=[1, 1], nodes='state_input'))
p.set_val('traj.burn1.states:accel',
value=burn1.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn1.states:deltav',
value=burn1.interpolate(ys=[0, 0.1], nodes='state_input'), )
p.set_val('traj.burn1.controls:u1',
value=burn1.interpolate(ys=[-3.5, 13.0], nodes='control_input'))
p.set_val('traj.coast.t_initial', value=2.25)
p.set_val('traj.coast.t_duration', value=3.0)
p.set_val('traj.coast.states:r',
value=coast.interpolate(ys=[1.3, 1.5], nodes='state_input'))
p.set_val('traj.coast.states:theta',
value=coast.interpolate(ys=[2.1767, 1.7], nodes='state_input'))
p.set_val('traj.coast.states:vr',
value=coast.interpolate(ys=[0.3285, 0], nodes='state_input'))
p.set_val('traj.coast.states:vt',
value=coast.interpolate(ys=[0.97, 1], nodes='state_input'))
p.set_val('traj.coast.states:accel',
value=coast.interpolate(ys=[0, 0], nodes='state_input'))
# p.set_val('traj.coast.controls:u1',
# value=coast.interpolate(ys=[0, 0], nodes='control_input'))
p.set_val('traj.burn2.t_initial', value=5.25)
p.set_val('traj.burn2.t_duration', value=1.75)
p.set_val('traj.burn2.states:r',
value=burn2.interpolate(ys=[1.8, 3], nodes='state_input'))
p.set_val('traj.burn2.states:theta',
value=burn2.interpolate(ys=[3.2, 4.0], nodes='state_input'))
p.set_val('traj.burn2.states:vr',
value=burn2.interpolate(ys=[.5, 0], nodes='state_input'))
p.set_val('traj.burn2.states:vt',
value=burn2.interpolate(ys=[1, np.sqrt(1 / 3)], nodes='state_input'))
p.set_val('traj.burn2.states:accel',
value=burn2.interpolate(ys=[0.1, 0], nodes='state_input'))
p.set_val('traj.burn2.states:deltav',
value=burn2.interpolate(ys=[0.1, 0.2], nodes='state_input'))
p.set_val('traj.burn2.controls:u1',
value=burn2.interpolate(ys=[1, 1], nodes='control_input'))
p.run_driver()
assert_rel_error(self,
p.get_val('traj.burn2.timeseries.states:deltav')[-1],
0.3995,
tolerance=2.0E-3)
# Plot results
exp_out = traj.simulate()
fig = plt.figure(figsize=(8, 4))
fig.suptitle('Two Burn Orbit Raise Solution')
ax_u1 = plt.subplot2grid((2, 2), (0, 0))
ax_deltav = plt.subplot2grid((2, 2), (1, 0))
ax_xy = plt.subplot2grid((2, 2), (0, 1), rowspan=2)
span = np.linspace(0, 2 * np.pi, 100)
ax_xy.plot(np.cos(span), np.sin(span), 'k--', lw=1)
ax_xy.plot(3 * np.cos(span), 3 * np.sin(span), 'k--', lw=1)
ax_xy.set_xlim(-4.5, 4.5)
ax_xy.set_ylim(-4.5, 4.5)
ax_xy.set_xlabel('x ($R_e$)')
ax_xy.set_ylabel('y ($R_e$)')
ax_u1.set_xlabel('time ($TU$)')
ax_u1.set_ylabel('$u_1$ ($deg$)')
ax_u1.grid(True)
ax_deltav.set_xlabel('time ($TU$)')
ax_deltav.set_ylabel('${\Delta}v$ ($DU/TU$)')
ax_deltav.grid(True)
t_sol = dict((phs, p.get_val('traj.{0}.timeseries.time'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
x_sol = dict((phs, p.get_val('traj.{0}.timeseries.pos_x'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
y_sol = dict((phs, p.get_val('traj.{0}.timeseries.pos_y'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
dv_sol = dict((phs, p.get_val('traj.{0}.timeseries.states:deltav'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
u1_sol = dict((phs, p.get_val('traj.{0}.timeseries.controls:u1'.format(phs), units='deg'))
for phs in ['burn1', 'burn2'])
t_exp = dict((phs, exp_out.get_val('traj.{0}.timeseries.time'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
x_exp = dict((phs, exp_out.get_val('traj.{0}.timeseries.pos_x'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
y_exp = dict((phs, exp_out.get_val('traj.{0}.timeseries.pos_y'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
dv_exp = dict((phs, exp_out.get_val('traj.{0}.timeseries.states:deltav'.format(phs)))
for phs in ['burn1', 'coast', 'burn2'])
u1_exp = dict((phs, exp_out.get_val('traj.{0}.timeseries.controls:u1'.format(phs),
units='deg'))
for phs in ['burn1', 'burn2'])
for phs in ['burn1', 'coast', 'burn2']:
try:
ax_u1.plot(t_sol[phs], u1_sol[phs], 'ro', ms=3)
ax_u1.plot(t_exp[phs], u1_exp[phs], 'b-')
except KeyError:
pass
ax_deltav.plot(t_sol[phs], dv_sol[phs], 'ro', ms=3)
ax_deltav.plot(t_exp[phs], dv_exp[phs], 'b-')
ax_xy.plot(x_sol[phs], y_sol[phs], 'ro', ms=3, label='implicit')
ax_xy.plot(x_exp[phs], y_exp[phs], 'b-', label='explicit')
plt.show()
from shutil import rmtree
from tempfile import mkdtemp
import numpy as np
from openmdao.utils.general_utils import set_pyoptsparse_opt
from openmdao.utils.mpi import MPI
if MPI:
from openmdao.api import PETScVector
vector_class = PETScVector
else:
PETScVector = None
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
from openmdao.api import ExecComp, ExplicitComponent, Problem, \
Group, ParallelGroup, IndepVarComp, SqliteRecorder
from openmdao.utils.array_utils import evenly_distrib_idxs
from openmdao.recorders.tests.sqlite_recorder_test_utils import assertDriverIterationDataRecorded
from openmdao.recorders.tests.recorder_test_utils import run_driver
class DistributedAdder(ExplicitComponent):
"""
Distributes the work of adding 10 to every item in the param vector
"""
def test_reentry_mixed_controls(self):
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
p.driver.declare_coloring()
OPT, OPTIMIZER = set_pyoptsparse_opt('IPOPT', fallback=False)
p.driver.options['optimizer'] = OPTIMIZER
traj = p.model.add_subsystem('traj', Trajectory())
phase0 = traj.add_phase(
'phase0',
Phase(ode_class=ShuttleODE,
transcription=GaussLobatto(num_segments=20, order=3)))
phase0.set_time_options(fix_initial=True, units='s', duration_ref=200)
phase0.add_state('h',
fix_initial=True,
fix_final=True,
units='ft',
rate_source='hdot',
targets=['h'],
lower=0,
ref0=75000,
ref=300000,
defect_ref=1000)
phase0.add_state('gamma',
fix_initial=True,
fix_final=True,
units='rad',
rate_source='gammadot',
targets=['gamma'],
lower=-89. * np.pi / 180,
upper=89. * np.pi / 180)
phase0.add_state('phi',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='phidot',
lower=0,
upper=89. * np.pi / 180)
phase0.add_state('psi',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='psidot',
targets=['psi'],
lower=0,
upper=90. * np.pi / 180)
phase0.add_state('theta',
fix_initial=True,
fix_final=False,
units='rad',
rate_source='thetadot',
targets=['theta'],
lower=-89. * np.pi / 180,
upper=89. * np.pi / 180)
phase0.add_state('v',
fix_initial=True,
fix_final=True,
units='ft/s',
rate_source='vdot',
targets=['v'],
lower=500,
ref0=2500,
ref=25000)
phase0.add_control('alpha',
units='rad',
opt=True,
lower=-np.pi / 2,
upper=np.pi / 2)
phase0.add_polynomial_control('beta',
order=5,
units='rad',
opt=True,
lower=-89 * np.pi / 180,
upper=1 * np.pi / 180)
phase0.add_objective('theta', loc='final', ref=-0.01)
p.setup(check=True, force_alloc_complex=True)
p.set_val('traj.phase0.states:h',
phase0.interpolate(ys=[260000, 80000], nodes='state_input'),
units='ft')
p.set_val('traj.phase0.states:gamma',
phase0.interpolate(ys=[-1 * np.pi / 180, -5 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:phi',
phase0.interpolate(ys=[0, 75 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:psi',
phase0.interpolate(ys=[90 * np.pi / 180, 10 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:theta',
phase0.interpolate(ys=[0, 25 * np.pi / 180],
nodes='state_input'),
units='rad')
p.set_val('traj.phase0.states:v',
phase0.interpolate(ys=[25600, 2500], nodes='state_input'),
units='ft/s')
p.set_val('traj.phase0.t_initial', 0, units='s')
p.set_val('traj.phase0.t_duration', 2000, units='s')
p.set_val('traj.phase0.controls:alpha',
phase0.interpolate(
ys=[17.4 * np.pi / 180, 17.4 * np.pi / 180],
nodes='control_input'),
units='rad')
p.set_val('traj.phase0.polynomial_controls:beta', np.radians(-75))
p.run_driver()
exp_out = traj.simulate()
from scipy.interpolate import interp1d
t_sol = p.get_val('traj.phase0.timeseries.time')
beta_sol = p.get_val('traj.phase0.timeseries.polynomial_controls:beta')
t_sim = exp_out.get_val('traj.phase0.timeseries.time')
beta_sim = exp_out.get_val(
'traj.phase0.timeseries.polynomial_controls:beta')
sol_interp = interp1d(t_sol.ravel(), beta_sol.ravel())
sim_interp = interp1d(t_sim.ravel(), beta_sim.ravel())
t = np.linspace(0, t_sol.ravel()[-1], 100)
assert_near_equal(sim_interp(t), sol_interp(t), tolerance=1.0E-3)
def test_distributed_driver_debug_print_options(self):
# check that pyoptsparse is installed. if it is, try to use SLSQP.
OPT, OPTIMIZER = set_pyoptsparse_opt('SLSQP')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
else:
raise unittest.SkipTest("pyOptSparseDriver is required.")
class Mygroup(Group):
def setup(self):
self.add_subsystem('indep_var_comp', IndepVarComp('x'), promotes=['*'])
self.add_subsystem('Cy', ExecComp('y=2*x'), promotes=['*'])
self.add_subsystem('Cc', ExecComp('c=x+2'), promotes=['*'])
self.add_design_var('x')
self.add_constraint('c', lower=-3.)
prob = Problem()
prob.model.add_subsystem('par', ParallelGroup())
prob.model.par.add_subsystem('G1', Mygroup())
prob.model.par.add_subsystem('G2', Mygroup())
prob.model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
prob.model.connect('par.G1.y', 'Obj.y1')
prob.model.connect('par.G2.y', 'Obj.y2')
prob.model.add_objective('Obj.obj')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['print_results'] = False
prob.driver.options['debug_print'] = ['desvars', 'ln_cons', 'nl_cons', 'objs']
prob.setup()
stdout = sys.stdout
strout = StringIO()
sys.stdout = strout
sys.stdout = strout
try:
prob.run_driver()
finally:
sys.stdout = stdout
output = strout.getvalue().split('\n')
if MPI.COMM_WORLD.rank == 0:
# Just make sure we have more than one. Not sure we will always have the same number
# of iterations
self.assertTrue(output.count("Design Vars") > 1,
"Should be more than one design vars header printed")
self.assertTrue(output.count("Nonlinear constraints") > 1,
"Should be more than one nonlinear constraint header printed")
self.assertTrue(output.count("Linear constraints") > 1,
"Should be more than one linear constraint header printed")
self.assertTrue(output.count("Objectives") > 1,
"Should be more than one objective header printed")
self.assertTrue(len([s for s in output if 'par.G1.indep_var_comp.x' in s]) > 1,
"Should be more than one par.G1.indep_var_comp.x printed")
self.assertTrue(len([s for s in output if 'par.G2.indep_var_comp.x' in s]) > 1,
"Should be more than one par.G2.indep_var_comp.x printed")
self.assertTrue(len([s for s in output if 'par.G1.Cc.c' in s]) > 1,
"Should be more than one par.G1.Cc.c printed")
self.assertTrue(len([s for s in output if 'par.G2.Cc.c' in s]) > 1,
"Should be more than one par.G2.Cc.c printed")
self.assertTrue(len([s for s in output if s.startswith('None')]) > 1,
"Should be more than one None printed")
self.assertTrue(len([s for s in output if 'Obj.obj' in s]) > 1,
"Should be more than one Obj.obj printed")
else:
self.assertEqual(output, [''])
import unittest
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, ExecComp, AnalysisError, ExplicitComponent
from openmdao.devtools.testutil import assert_rel_error
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.test_suite.components.expl_comp_array import TestExplCompArrayDense
from openmdao.test_suite.components.sellar import SellarDerivativesGrouped
from openmdao.utils.general_utils import set_pyoptsparse_opt
# check that pyoptsparse is installed
# if it is, try to use SNOPT but fall back to SLSQP
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
class ParaboloidAE(ExplicitComponent):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
This version raises an analysis error 50% of the time.
The AE in ParaboloidAE stands for AnalysisError."""
def __init__(self):
super(ParaboloidAE, self).__init__()
self.fail_hard = False
def setup(self):
def test_promotes_parameter(self):
for transcription in ['radau-ps', 'gauss-lobatto']:
with self.subTest(msg=transcription):
optimizer = 'SLSQP'
num_segments = 10
transcription_order = 3
compressed = False
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
OPT, OPTIMIZER = set_pyoptsparse_opt(optimizer, fallback=True)
p.driver.options['optimizer'] = OPTIMIZER
p.driver.declare_coloring()
if transcription == 'gauss-lobatto':
t = dm.GaussLobatto(num_segments=num_segments,
order=transcription_order,
compressed=compressed)
elif transcription == 'radau-ps':
t = dm.Radau(num_segments=num_segments,
order=transcription_order,
compressed=compressed)
traj = dm.Trajectory()
phase = dm.Phase(ode_class=BrachistochroneODE, transcription=t)
traj.add_phase('phase0',
phase,
promotes_inputs=[
't_initial', 't_duration', 'parameters:g'
])
p.model.add_subsystem('traj', traj)
phase.set_time_options(fix_initial=True,
duration_bounds=(.5, 10),
units='s')
phase.add_state('x',
fix_initial=True,
fix_final=False,
solve_segments=False,
units='m',
rate_source='xdot')
phase.add_state('y',
fix_initial=True,
fix_final=False,
solve_segments=False,
units='m',
rate_source='ydot')
phase.add_state('v',
fix_initial=True,
fix_final=False,
solve_segments=False,
units='m/s',
rate_source='vdot',
targets=['v'])
phase.add_control('theta',
continuity=True,
rate_continuity=True,
units='deg',
lower=0.01,
upper=179.9,
targets=['theta'])
phase.add_parameter('g',
units='m/s**2',
val=9.80665,
targets=['g'])
phase.add_boundary_constraint('x', loc='final', equals=10)
phase.add_boundary_constraint('y', loc='final', equals=5)
# Minimize time at the end of the phase
phase.add_objective('time_phase', loc='final', scaler=10)
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
p.set_val('traj.t_initial', 0.0)
p.set_val('traj.t_duration', 2.0)
p.set_val('traj.phase0.states:x', phase.interp('x', ys=[0,
10]))
p.set_val('traj.phase0.states:y', phase.interp('y', ys=[10,
5]))
p.set_val('traj.phase0.states:v', phase.interp('v',
ys=[0, 9.9]))
p.set_val('traj.phase0.controls:theta',
phase.interp('theta', ys=[5, 100]))
p.set_val('traj.parameters:g', 9.80665)
p.run_driver()
assert_near_equal(p['traj.t_duration'],
1.8016,
tolerance=1.0E-4)
