def gauss_lobatto_subsets_and_nodes(n, seg_idx, compressed=False):
"""
Returns the subset dictionary corresponding to the Gauss-Lobatto transcription.
Parameters
----------
n : int
The total number of nodes in the Gauss-Lobatto segment. Must be
an odd number.
seg_idx : int
The index of this segment within its phase.
compressed : bool
True if the subset requested is for a phase with compressed transcription.
Returns
-------
dict
A dictionary with the following keys:
'disc' gives the indices of the state discretization nodes (deprecated)
'state_disc' gives the indices of the state discretization nodes
'state_input' gives the indices of the state input nodes
'control_disc' gives the indices of the control discretization nodes
'control_input' gives the indices of the control input nodes
'segment_ends' gives the indices of the nodes at the start (even) and end (odd) of a segment
'col' gives the indices of the collocation nodes
'all' gives all node indices.
Notes
-----
Subset 'state_input' is the same as subset 'state_disc' if `compressed == False` or
`first_seg == True`. The same is true of subsets 'control_input' and 'control_disc'.
"""
if n % 2 == 0:
raise ValueError(
'A Gauss-Lobatto scheme must use an odd number of points')
subsets = {
'disc':
np.arange(0, n, 2, dtype=int),
'state_disc':
np.arange(0, n, 2, dtype=int),
'state_input':
np.arange(0, n, 2, dtype=int)
if not compressed or seg_idx == 0 else np.arange(2, n, 2, dtype=int),
'control_disc':
np.arange(n, dtype=int),
'control_input':
np.arange(n, dtype=int)
if not compressed or seg_idx == 0 else np.arange(1, n, dtype=int),
'segment_ends':
np.array([0, n - 1], dtype=int),
'col':
np.arange(1, n, 2, dtype=int),
'all':
np.arange(n, dtype=int),
'solution':
np.arange(n, dtype=int),
}
return subsets, lgl(n)[0]
def test_single_phase_reverse_propagation_rk(self):
prob = om.Problem()
num_seg = 10
seg_ends, _ = lgl(num_seg + 1)
# First phase: normal operation.
transcription = dm.RungeKutta(num_segments=num_seg)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = prob.model.add_subsystem('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge',
fix_initial=True,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
prob.setup()
prob['phase0.t_initial'] = 0
prob['phase0.t_duration'] = -1.0 * 3600
prob['phase0.states:state_of_charge'][:] = 0.63464982
prob.set_solver_print(level=0)
prob.run_model()
soc0 = prob['phase0.states:state_of_charge']
assert_near_equal(soc0[-1], 1.0, 1e-6)
def test_solver_defects_single_phase_reverse_propagation(self):
prob = Problem()
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
# First phase: normal operation.
transcription = Radau(num_segments=5,
order=5,
segment_ends=seg_ends,
compressed=True)
phase0 = Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = prob.model.add_subsystem('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.set_state_options('state_of_charge',
fix_initial=True,
fix_final=False,
solve_segments=True)
prob.setup()
prob['phase0.t_initial'] = 0
prob['phase0.t_duration'] = -1.0 * 3600
prob['phase0.states:state_of_charge'][:] = 0.63464982
prob.set_solver_print(level=0)
prob.run_model()
soc0 = prob['phase0.states:state_of_charge']
assert_rel_error(self, soc0[-1], 1.0, 1e-6)
def test_static_input_parameter_connections_gl(self):
class TrajectoryODE(om.Group):
def initialize(self):
self.options.declare('num_nodes', types=int)
def setup(self):
nn = self.options['num_nodes']
self.add_subsystem('sum', om.ExecComp('m_tot = sum(m)',
m={'value': np.zeros((2, 2)),
'units': 'kg'},
m_tot={'value': np.zeros(nn),
'units': 'kg'}))
self.add_subsystem('eom', FlightPathEOM2D(num_nodes=nn))
self.connect('sum.m_tot', 'eom.m')
optimizer = 'SLSQP'
num_segments = 1
transcription_order = 5
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
p.driver.options['optimizer'] = optimizer
p.driver.declare_coloring()
seg_ends, _ = lgl(num_segments + 1)
phase = dm.Phase(ode_class=TrajectoryODE,
transcription=dm.GaussLobatto(num_segments=num_segments,
order=transcription_order,
segment_ends=seg_ends))
p.model.add_subsystem('phase0', phase)
phase.set_time_options(initial_bounds=(0.0, 100.0), duration_bounds=(0., 100.), units='s')
phase.add_state('h', fix_initial=True, fix_final=True, lower=0.0, units='m', rate_source='eom.h_dot')
phase.add_state('v', fix_initial=True, fix_final=False, units='m/s', rate_source='eom.v_dot')
phase.add_input_parameter('m', val=[[1, 2], [3, 4]], units='kg', targets='sum.m', dynamic=False)
p.model.linear_solver = om.DirectSolver()
p.setup(check=True, force_alloc_complex=True)
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 100.0
p['phase0.states:h'] = phase.interpolate(ys=[20, 0], nodes='state_input')
p['phase0.states:v'] = phase.interpolate(ys=[0, -5], nodes='state_input')
p.run_model()
expected = np.array([[1, 2], [3, 4]])
assert_rel_error(self, p.get_val('phase0.rhs_disc.sum.m'), expected)
assert_rel_error(self, p.get_val('phase0.rhs_col.sum.m'), expected)
def test_solver_defects_reverse_propagation(self):
prob = om.Problem()
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
# First phase: normal operation.
transcription = dm.Radau(num_segments=5,
order=5,
segment_ends=seg_ends,
compressed=True)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge',
fix_initial=True,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase: normal operation.
phase1 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
traj_p1.add_objective('time', loc='final')
traj.link_phases(phases=['phase0', 'phase1'],
vars=['state_of_charge', 'time'],
connected=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = -1.0 * 3600
prob['traj.phase0.states:state_of_charge'][:] = 0.23794217
prob['traj.phase1.t_initial'] = 0
prob['traj.phase1.t_duration'] = -1.0 * 3600
prob.set_solver_print(level=0)
prob.run_model()
soc1 = prob['traj.phase1.states:state_of_charge']
assert_near_equal(soc1[-1], 1.0, 1e-6)
def explicit_subsets_and_nodes(n, seg_idx, compressed=False, shooting='single'):
"""
Returns the subset dictionary corresponding to the Runge-Kutta transcription.
Parameters
----------
n : int
The total number of control discretization nodes in the Runge-Kutta segment.
seg_idx : int
The index of this segment within its phase.
compressed : bool
True if the subset requested is for a phase with compressed transcription.
shooting : str
One of the shooting method types for explicit phases ('single', 'hybrid', or 'multiple')
Returns
-------
subsets : dict of {str: np.ndarray}
'state_disc' gives the indices of the state discretization nodes
'state_input' gives the indices of the state input nodes
'control_disc' gives the indices of the control discretization nodes
'control_input' gives the indices of the control input nodes
'segment_ends' gives the indices of the nodes at the start (even) and end (odd) of a segment
'step' gives the indices of the nodes at step boundaries
'all' gives all node indices
nodes : np.ndarray
The location of all nodes on the interval -1, 1.
Notes
-----
(subsets, nodes)
Subset 'state_input' is the same as subset 'state_disc'.
"""
subsets = {
'disc': np.arange(0, n, 2, dtype=int),
'state_disc': np.zeros((1,), dtype=int) if seg_idx == 0 or shooting == 'multiple'
else np.empty((0,), dtype=int),
'state_input': np.zeros((1,), dtype=int) if seg_idx == 0 or shooting == 'multiple'
else np.empty((0,), dtype=int),
'control_disc': np.arange(n, dtype=int),
'control_input': np.arange(n, dtype=int) if not compressed or seg_idx == 0
else np.arange(1, n, dtype=int),
'segment_ends': np.array([0, n-1], dtype=int),
'col': np.arange(1, n, 2, dtype=int),
'all': np.arange(n, dtype=int),
'solution': np.arange(n, dtype=int),
}
return subsets, lgl(n)[0]
def test_polynomial_control(self):
tx = dm.GaussLobatto(num_segments=8, order=5, compressed=True)
phase = dm.Phase(ode_class=BrachistochroneODE, transcription=tx)
phase.add_polynomial_control('u',
fix_initial=True,
fix_final=True,
order=3)
xs = np.linspace(-10, 10, 100)
ys = xs**3
input_nodes, _ = lgl(4)
expected = np.atleast_2d((10 * input_nodes)**3).T
assert_near_equal(phase.interp('u', ys=ys, xs=xs, kind='cubic'),
expected)
def test_basic(self):
import matplotlib.pyplot as plt
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
import dymos as dm
from dymos.examples.battery_multibranch.battery_multibranch_ode import BatteryODE
from dymos.utils.lgl import lgl
prob = om.Problem()
opt = prob.driver = om.ScipyOptimizeDriver()
opt.declare_coloring()
opt.options['optimizer'] = 'SLSQP'
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
# First phase: normal operation.
transcription = dm.Radau(num_segments=num_seg,
order=5,
segment_ends=seg_ends,
compressed=False)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge',
fix_initial=True,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase: normal operation.
phase1 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
traj_p1.add_objective('time', loc='final')
# Second phase, but with battery failure.
phase1_bfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_battery': 2},
transcription=transcription)
traj_p1_bfail = traj.add_phase('phase1_bfail', phase1_bfail)
traj_p1_bfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_bfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase, but with motor failure.
phase1_mfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_motor': 2},
transcription=transcription)
traj_p1_mfail = traj.add_phase('phase1_mfail', phase1_mfail)
traj_p1_mfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_mfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
traj.link_phases(phases=['phase0', 'phase1'],
vars=['state_of_charge', 'time'])
traj.link_phases(phases=['phase0', 'phase1_bfail'],
vars=['state_of_charge', 'time'])
traj.link_phases(phases=['phase0', 'phase1_mfail'],
vars=['state_of_charge', 'time'])
prob.model.options['assembled_jac_type'] = 'csc'
prob.model.linear_solver = om.DirectSolver(assemble_jac=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = 1.0 * 3600
prob['traj.phase1.t_initial'] = 1.0 * 3600
prob['traj.phase1.t_duration'] = 1.0 * 3600
prob['traj.phase1_bfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_bfail.t_duration'] = 1.0 * 3600
prob['traj.phase1_mfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_mfail.t_duration'] = 1.0 * 3600
prob.set_solver_print(level=0)
dm.run_problem(prob)
soc0 = prob['traj.phase0.states:state_of_charge']
soc1 = prob['traj.phase1.states:state_of_charge']
soc1b = prob['traj.phase1_bfail.states:state_of_charge']
soc1m = prob['traj.phase1_mfail.states:state_of_charge']
# Final value for State of Chrage in each segment should be a good test.
print('State of Charge after 1 hour')
assert_near_equal(soc0[-1], 0.63464982, 1e-6)
print('State of Charge after 2 hours')
assert_near_equal(soc1[-1], 0.23794217, 1e-6)
print('State of Charge after 2 hours, battery fails at 1 hour')
assert_near_equal(soc1b[-1], 0.0281523, 1e-6)
print('State of Charge after 2 hours, motor fails at 1 hour')
assert_near_equal(soc1m[-1], 0.18625395, 1e-6)
# Plot Results
t0 = prob['traj.phases.phase0.time.time'] / 3600
t1 = prob['traj.phases.phase1.time.time'] / 3600
t1b = prob['traj.phases.phase1_bfail.time.time'] / 3600
t1m = prob['traj.phases.phase1_mfail.time.time'] / 3600
plt.subplot(2, 1, 1)
plt.plot(t0, soc0, 'b')
plt.plot(t1, soc1, 'b')
plt.plot(t1b, soc1b, 'r')
plt.plot(t1m, soc1m, 'c')
plt.xlabel('Time (hour)')
plt.ylabel('State of Charge (percent)')
I_Li0 = prob['traj.phases.phase0.rhs_all.pwr_balance.I_Li']
I_Li1 = prob['traj.phases.phase1.rhs_all.pwr_balance.I_Li']
I_Li1b = prob['traj.phases.phase1_bfail.rhs_all.pwr_balance.I_Li']
I_Li1m = prob['traj.phases.phase1_mfail.rhs_all.pwr_balance.I_Li']
plt.subplot(2, 1, 2)
plt.plot(t0, I_Li0, 'b')
plt.plot(t1, I_Li1, 'b')
plt.plot(t1b, I_Li1b, 'r')
plt.plot(t1m, I_Li1m, 'c')
plt.xlabel('Time (hour)')
plt.ylabel('Line Current (A)')
plt.legend([
'Phase 1', 'Phase 2', 'Phase 2 Battery Fail', 'Phase 2 Motor Fail'
],
loc=2)
plt.show()
def test_steady_aircraft_for_docs(self):
import matplotlib.pyplot as plt
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
import dymos as dm
from dymos.examples.aircraft_steady_flight.aircraft_ode import AircraftODE
from dymos.examples.plotting import plot_results
from dymos.utils.lgl import lgl
p = om.Problem(model=om.Group())
p.driver = om.pyOptSparseDriver()
p.driver.options['optimizer'] = 'SLSQP'
p.driver.declare_coloring()
num_seg = 15
seg_ends, _ = lgl(num_seg + 1)
traj = p.model.add_subsystem('traj', dm.Trajectory())
phase = traj.add_phase(
'phase0',
dm.Phase(ode_class=AircraftODE,
transcription=dm.Radau(num_segments=num_seg,
segment_ends=seg_ends,
order=3,
compressed=False)))
# 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')
phase.set_time_options(fix_initial=True,
duration_bounds=(300, 10000),
duration_ref=5600)
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',
fix_initial=True,
fix_final=True,
upper=1.5E5,
lower=0.0,
ref=1e2,
defect_ref=1e2)
phase.add_state('alt',
units='kft',
rate_source='climb_rate',
fix_initial=True,
fix_final=True,
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_parameter(
'S',
targets=['aero.S', 'flight_equilibrium.S', 'propulsion.S'],
units='m**2')
phase.add_parameter('mass_empty',
targets=['mass_comp.mass_empty'],
units='kg')
phase.add_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', 'traj.phase0.parameters:S')
p.model.connect('assumptions.mass_empty',
'traj.phase0.parameters:mass_empty')
p.model.connect('assumptions.mass_payload',
'traj.phase0.parameters:mass_payload')
phase.add_objective('range', loc='final', ref=-1.0e-4)
phase.add_timeseries_output('aero.CL')
phase.add_timeseries_output('aero.CD')
p.setup()
p['traj.phase0.t_initial'] = 0.0
p['traj.phase0.t_duration'] = 3600.0
p['traj.phase0.states:range'][:] = phase.interpolate(
ys=(0, 724.0), nodes='state_input')
p['traj.phase0.states:mass_fuel'][:] = phase.interpolate(
ys=(30000, 1e-3), nodes='state_input')
p['traj.phase0.states:alt'][:] = 10.0
p['traj.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)
assert_near_equal(p.get_val('traj.phase0.timeseries.states:range',
units='NM')[-1],
726.85,
tolerance=1.0E-2)
exp_out = traj.simulate()
assert_near_equal(p.get_val('traj.phase0.timeseries.CL')[0],
exp_out.get_val('traj.phase0.timeseries.CL')[0],
tolerance=1.0E-9)
assert_near_equal(p.get_val('traj.phase0.timeseries.CD')[0],
exp_out.get_val('traj.phase0.timeseries.CD')[0],
tolerance=1.0E-9)
plot_results(
[('traj.phase0.timeseries.states:range',
'traj.phase0.timeseries.states:alt', 'range (NM)',
'altitude (kft)'),
('traj.phase0.timeseries.time',
'traj.phase0.timeseries.states:mass_fuel', 'time (s)',
'fuel mass (lbm)'),
('traj.phase0.timeseries.time', 'traj.phase0.timeseries.CL',
'time (s)', 'lift coefficient'),
('traj.phase0.timeseries.time', 'traj.phase0.timeseries.CD',
'time (s)', 'drag coefficient')],
title='Commercial Aircraft Optimization',
p_sol=p,
p_sim=exp_out)
plt.show()
def test_optimizer_segments_direct_connections(self):
prob = Problem()
if optimizer == 'SNOPT':
opt = prob.driver = pyOptSparseDriver()
opt.options['optimizer'] = optimizer
opt.options['dynamic_simul_derivs'] = True
opt.opt_settings['Major iterations limit'] = 1000
opt.opt_settings['Major feasibility tolerance'] = 1.0E-6
opt.opt_settings['Major optimality tolerance'] = 1.0E-6
opt.opt_settings["Linesearch tolerance"] = 0.10
opt.opt_settings['iSumm'] = 6
else:
opt = prob.driver = ScipyOptimizeDriver()
opt.options['dynamic_simul_derivs'] = True
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', Trajectory())
# First phase: normal operation.
transcription = Radau(num_segments=5,
order=5,
segment_ends=seg_ends,
compressed=True)
phase0 = Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.set_state_options('state_of_charge',
fix_initial=True,
fix_final=False)
# Second phase: normal operation.
phase1 = Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.set_state_options('state_of_charge',
fix_initial=False,
fix_final=False)
traj_p1.add_objective('time', loc='final')
# Second phase, but with battery failure.
phase1_bfail = Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_battery': 2},
transcription=transcription)
traj_p1_bfail = traj.add_phase('phase1_bfail', phase1_bfail)
traj_p1_bfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_bfail.set_state_options('state_of_charge',
fix_initial=False,
fix_final=False)
# Second phase, but with motor failure.
phase1_mfail = Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_motor': 2},
transcription=transcription)
traj_p1_mfail = traj.add_phase('phase1_mfail', phase1_mfail)
traj_p1_mfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_mfail.set_state_options('state_of_charge',
fix_initial=False,
fix_final=False)
traj.link_phases(phases=['phase0', 'phase1'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_bfail'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_mfail'],
vars=['state_of_charge', 'time'],
connected=True)
prob.model.options['assembled_jac_type'] = 'csc'
prob.model.linear_solver = DirectSolver(assemble_jac=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = 1.0 * 3600
prob['traj.phase1.t_initial'] = 1.0 * 3600
prob['traj.phase1.t_duration'] = 1.0 * 3600
prob['traj.phase1_bfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_bfail.t_duration'] = 1.0 * 3600
prob['traj.phase1_mfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_mfail.t_duration'] = 1.0 * 3600
prob.set_solver_print(level=0)
prob.run_driver()
soc0 = prob['traj.phase0.states:state_of_charge']
soc1 = prob['traj.phase1.states:state_of_charge']
soc1b = prob['traj.phase1_bfail.states:state_of_charge']
soc1m = prob['traj.phase1_mfail.states:state_of_charge']
# Final value for State of Chrage in each segment should be a good test.
assert_rel_error(self, soc0[-1], 0.63464982, 1e-6)
assert_rel_error(self, soc1[-1], 0.23794217, 1e-6)
assert_rel_error(self, soc1b[-1], 0.0281523, 1e-6)
assert_rel_error(self, soc1m[-1], 0.18625395, 1e-6)
def test_connected_linkages_rk(self):
prob = om.Problem()
if optimizer == 'SNOPT':
opt = prob.driver = om.pyOptSparseDriver()
opt.options['optimizer'] = optimizer
opt.declare_coloring()
opt.opt_settings['Major iterations limit'] = 1000
opt.opt_settings['Major feasibility tolerance'] = 1.0E-6
opt.opt_settings['Major optimality tolerance'] = 1.0E-6
opt.opt_settings["Linesearch tolerance"] = 0.10
opt.opt_settings['iSumm'] = 6
else:
opt = prob.driver = om.ScipyOptimizeDriver()
opt.declare_coloring()
num_seg = 20
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
# First phase: normal operation.
transcription = dm.RungeKutta(num_segments=num_seg)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge',
fix_initial=True,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase: normal operation.
phase1 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
traj_p1.add_objective('time', loc='final')
# Second phase, but with battery failure.
phase1_bfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_battery': 2},
transcription=transcription)
traj_p1_bfail = traj.add_phase('phase1_bfail', phase1_bfail)
traj_p1_bfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_bfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase, but with motor failure.
phase1_mfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_motor': 2},
transcription=transcription)
traj_p1_mfail = traj.add_phase('phase1_mfail', phase1_mfail)
traj_p1_mfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_mfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
targets=['SOC'],
rate_source='dXdt:SOC')
traj.link_phases(phases=['phase0', 'phase1'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_bfail'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_mfail'],
vars=['state_of_charge', 'time'],
connected=True)
prob.model.options['assembled_jac_type'] = 'csc'
prob.model.linear_solver = om.DirectSolver(assemble_jac=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = 1.0 * 3600
prob['traj.phase1.t_initial'] = 1.0 * 3600
prob['traj.phase1.t_duration'] = 1.0 * 3600
prob['traj.phase1_bfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_bfail.t_duration'] = 1.0 * 3600
prob['traj.phase1_mfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_mfail.t_duration'] = 1.0 * 3600
prob.set_solver_print(level=0)
dm.run_problem(prob)
soc0 = prob['traj.phase0.states:state_of_charge']
soc1 = prob['traj.phase1.states:state_of_charge']
soc1b = prob['traj.phase1_bfail.states:state_of_charge']
soc1m = prob['traj.phase1_mfail.states:state_of_charge']
# Final value for State of Chrage in each segment should be a good test.
assert_near_equal(soc0[-1], 0.63464982, 5e-5)
assert_near_equal(soc1[-1], 0.23794217, 5e-5)
assert_near_equal(soc1b[-1], 0.0281523, 5e-5)
assert_near_equal(soc1m[-1], 0.18625395, 5e-5)
def test_solver_defects(self):
prob = om.Problem()
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
# First phase: normal operation.
transcription = dm.Radau(num_segments=5,
order=5,
segment_ends=seg_ends,
compressed=True)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge',
fix_initial=True,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase: normal operation.
phase1 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
traj_p1.add_objective('time', loc='final')
# Second phase, but with battery failure.
phase1_bfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_battery': 2},
transcription=transcription)
traj_p1_bfail = traj.add_phase('phase1_bfail', phase1_bfail)
traj_p1_bfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_bfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
# Second phase, but with motor failure.
phase1_mfail = dm.Phase(ode_class=BatteryODE,
ode_init_kwargs={'num_motor': 2},
transcription=transcription)
traj_p1_mfail = traj.add_phase('phase1_mfail', phase1_mfail)
traj_p1_mfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_mfail.add_state('state_of_charge',
fix_initial=False,
fix_final=False,
solve_segments=True,
targets=['SOC'],
rate_source='dXdt:SOC')
traj.link_phases(phases=['phase0', 'phase1'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_bfail'],
vars=['state_of_charge', 'time'],
connected=True)
traj.link_phases(phases=['phase0', 'phase1_mfail'],
vars=['state_of_charge', 'time'],
connected=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = 1.0 * 3600
prob['traj.phase1.t_initial'] = 1.0 * 3600
prob['traj.phase1.t_duration'] = 1.0 * 3600
prob['traj.phase1_bfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_bfail.t_duration'] = 1.0 * 3600
prob['traj.phase1_mfail.t_initial'] = 1.0 * 3600
prob['traj.phase1_mfail.t_duration'] = 1.0 * 3600
prob['traj.phase0.states:state_of_charge'][:] = 1.0
prob.set_solver_print(level=0)
prob.run_model()
soc0 = prob['traj.phase0.states:state_of_charge']
soc1 = prob['traj.phase1.states:state_of_charge']
soc1b = prob['traj.phase1_bfail.states:state_of_charge']
soc1m = prob['traj.phase1_mfail.states:state_of_charge']
# Final value for State of Charge in each segment should be a good test.
assert_near_equal(soc0[-1], 0.63464982, 1e-6)
assert_near_equal(soc1[-1], 0.23794217, 1e-6)
assert_near_equal(soc1b[-1], 0.0281523, 1e-6)
assert_near_equal(soc1m[-1], 0.18625395, 1e-6)
scale=.25)
if i == 0:
plt.savefig('lgl_animation_1.png')
# Plot the interpolating polynomials
t_dense = np.linspace(time[0], time[-1], 100)
A_i, B_i, A_d, B_d = hermite_matrices(time[state_disc_idxs], t_dense)
y_dense = (A_i.dot(y[state_disc_idxs]) + B_i.dot(ydot[state_disc_idxs]))
vy_dense = A_i.dot(vy[state_disc_idxs]) + B_i.dot(vydot[state_disc_idxs])
axes[0].plot(t_dense, y_dense, ms=None, ls=':')
axes[1].plot(t_dense, vy_dense, ms=None, ls=':')
# Plot the values and rates at the collocation nodes
tau_s, _ = lgl(3)
tau_disc = tau_s[0::2]
tau_col = tau_s[1::2]
A_i, B_i, A_d, B_d = hermite_matrices(tau_disc, tau_col)
y_col = B_i.dot(ydot[state_disc_idxs]) * dt_dstau[col_idxs] + A_i.dot(
y[state_disc_idxs])
vy_col = B_i.dot(vydot[state_disc_idxs]) * dt_dstau[col_idxs] + A_i.dot(
vy[state_disc_idxs])
yprime_col = A_d.dot(y[state_disc_idxs]) / dt_dstau + B_d.dot(
ydot[state_disc_idxs])
vyprime_col = A_d.dot(vy[state_disc_idxs]) / dt_dstau + B_d.dot(
vydot[state_disc_idxs])
axes[0].plot(time[col_idxs], y_col, marker=(3, 0, 0), ms=8)
def test_nodes_and_weights_2(self):
x_2, w_2 = lgl(2)
assert_almost_equal(x_2, x_i[2], decimal=6)
assert_almost_equal(w_2, w_i[2], decimal=6)
def ex_aircraft_steady_flight(optimizer='SLSQP',
transcription='gauss-lobatto'):
p = Problem(model=Group())
p.driver = pyOptSparseDriver()
p.driver.options['optimizer'] = optimizer
p.driver.options['dynamic_simul_derivs'] = True
if optimizer == 'SNOPT':
p.driver.opt_settings['Major iterations limit'] = 1000
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
num_seg = 15
seg_ends, _ = lgl(num_seg + 1)
phase = Phase(transcription,
ode_class=AircraftODE,
num_segments=num_seg,
segment_ends=seg_ends,
transcription_order=5,
compressed=False)
# Pass Reference Area from an external source
assumptions = p.model.add_subsystem('assumptions', 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=3600)
phase.set_state_options('range',
units='NM',
fix_initial=True,
fix_final=False,
scaler=0.001,
defect_scaler=1.0E-2)
phase.set_state_options('mass_fuel',
units='lbm',
fix_initial=True,
fix_final=True,
upper=1.5E5,
lower=0.0,
scaler=1.0E-5,
defect_scaler=1.0E-1)
phase.add_control('alt',
units='kft',
opt=True,
lower=0.0,
upper=50.0,
rate_param='climb_rate',
rate_continuity=True,
rate_continuity_scaler=1.0,
rate2_continuity=True,
rate2_continuity_scaler=1.0,
ref=1.0,
fix_initial=True,
fix_final=True)
phase.add_control('mach', units=None, opt=False)
phase.add_input_parameter('S', units='m**2')
phase.add_input_parameter('mass_empty', units='kg')
phase.add_input_parameter('mass_payload', units='kg')
phase.add_path_constraint('propulsion.tau', lower=0.01, upper=1.0)
phase.add_path_constraint('alt_rate',
units='ft/min',
lower=-3000,
upper=3000,
ref=3000)
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=-1.0)
p.model.linear_solver = DirectSolver(assemble_jac=True)
p.setup()
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 3600.0
p['phase0.states:range'] = phase.interpolate(ys=(0, 1000.0),
nodes='state_input')
p['phase0.states:mass_fuel'] = phase.interpolate(ys=(30000, 0),
nodes='state_input')
p['phase0.controls:mach'][:] = 0.8
p['phase0.controls:alt'][:] = 10.0
p['assumptions.S'] = 427.8
p['assumptions.mass_empty'] = 0.15E6
p['assumptions.mass_payload'] = 84.02869 * 400
p.run_driver()
exp_out = phase.simulate(
times=np.linspace(0, p['phase0.t_duration'], 500),
record=True,
record_file='test_ex_aircraft_steady_flight_rec.db')
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('alt', nodes='all'), 'ro')
plt.plot(exp_out.get_values('time'), exp_out.get_values('alt'), 'b-')
plt.suptitle('altitude vs time')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('alt_rate', nodes='all', units='ft/min'), 'ro')
plt.plot(exp_out.get_values('time'),
exp_out.get_values('alt_rate', units='ft/min'), 'b-')
plt.suptitle('altitude rate vs time')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('mass_fuel', nodes='all'), 'ro')
plt.plot(exp_out.get_values('time'), exp_out.get_values('mass_fuel'), 'b-')
plt.suptitle('fuel mass vs time')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('propulsion.dXdt:mass_fuel', nodes='all'), 'ro')
plt.plot(exp_out.get_values('time'),
exp_out.get_values('propulsion.dXdt:mass_fuel'), 'b-')
plt.suptitle('fuel mass flow rate vs time')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('mach', nodes='all'), 'ro')
plt.plot(exp_out.get_values('time'), exp_out.get_values('mach'), 'b-')
plt.suptitle('mach vs time')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('mach_rate', nodes='all'), 'ro')
plt.plot(exp_out.get_values('time'), exp_out.get_values('mach_rate'), 'b-')
plt.suptitle('mach rate vs time')
print('time')
print(phase.get_values('time', nodes='all').T)
print('alt')
print(phase.get_values('alt', nodes='all').T)
print('alt_rate')
print(phase.get_values('alt_rate', nodes='all').T)
print('alt_rate2')
print(phase.get_values('alt_rate2', nodes='all').T)
print('range')
print(phase.get_values('range', nodes='all').T)
print('flight path angle')
print(phase.get_values('gam_comp.gam').T)
print('true airspeed')
print(phase.get_values('tas_comp.TAS', units='m/s').T)
print('coef of lift')
print(phase.get_values('aero.CL').T)
print('coef of drag')
print(phase.get_values('aero.CD').T)
print('atmos density')
print(phase.get_values('atmos.rho').T)
print('alpha')
print(phase.get_values('flight_equilibrium.alpha', units='rad').T)
print('coef of thrust')
print(phase.get_values('flight_equilibrium.CT').T)
print('fuel flow rate')
print(phase.get_values('propulsion.dXdt:mass_fuel').T)
print('max_thrust')
print(phase.get_values('propulsion.max_thrust', units='N').T)
print('tau')
print(phase.get_values('propulsion.tau').T)
print('dynamic pressure')
print(phase.get_values('q_comp.q', units='Pa').T)
print('S')
print(phase.get_values('S', units='m**2').T)
plt.show()
return p
def test_polynomial_control_group_scalar_gl(self):
transcription = 'gauss-lobatto'
compressed = True
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=5,
segment_ends=segends,
transcription=transcription,
compressed=compressed)
p = om.Problem(model=om.Group())
controls = {
'a': PolynomialControlOptionsDictionary(),
'b': PolynomialControlOptionsDictionary()
}
controls['a']['units'] = 'm'
controls['a']['order'] = 3
controls['a']['shape'] = (1, )
controls['a']['opt'] = True
controls['b']['units'] = 'm'
controls['b']['order'] = 3
controls['b']['shape'] = (1, )
controls['b']['opt'] = True
ivc = om.IndepVarComp()
p.model.add_subsystem('ivc', ivc, promotes_outputs=['*'])
ivc.add_output('t_initial', val=0.0, units='s')
ivc.add_output('t_duration', val=10.0, units='s')
p.model.add_subsystem('time_comp',
subsys=TimeComp(
num_nodes=gd.num_nodes,
node_ptau=gd.node_ptau,
node_dptau_dstau=gd.node_dptau_dstau,
units='s'),
promotes_inputs=['t_initial', 't_duration'],
promotes_outputs=['time', 'dt_dstau'])
polynomial_control_group = PolynomialControlGroup(
grid_data=gd, polynomial_control_options=controls, time_units='s')
p.model.add_subsystem('polynomial_control_group',
subsys=polynomial_control_group,
promotes_inputs=['*'],
promotes_outputs=['*'])
p.setup(force_alloc_complex=True)
p['t_initial'] = 0.0
p['t_duration'] = 3.0
p.run_model()
control_nodes_ptau, _ = lgl(controls['a']['order'] + 1)
t_control_input = p['t_initial'] + 0.5 * (control_nodes_ptau +
1) * p['t_duration']
t_all = p['time']
p['polynomial_controls:a'][:, 0] = f_a(t_control_input)
p['polynomial_controls:b'][:, 0] = f_b(t_control_input)
p.run_model()
a_value_expected = f_a(t_all)
b_value_expected = f_b(t_all)
a_rate_expected = f1_a(t_all)
b_rate_expected = f1_b(t_all)
a_rate2_expected = f2_a(t_all)
b_rate2_expected = f2_b(t_all)
assert_almost_equal(p['polynomial_control_values:a'],
np.atleast_2d(a_value_expected).T)
assert_almost_equal(p['polynomial_control_values:b'],
np.atleast_2d(b_value_expected).T)
assert_almost_equal(p['polynomial_control_rates:a_rate'],
np.atleast_2d(a_rate_expected).T)
assert_almost_equal(p['polynomial_control_rates:b_rate'],
np.atleast_2d(b_rate_expected).T)
assert_almost_equal(p['polynomial_control_rates:a_rate2'],
np.atleast_2d(a_rate2_expected).T)
assert_almost_equal(p['polynomial_control_rates:b_rate2'],
np.atleast_2d(b_rate2_expected).T)
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(compact_print=False,
out_stream=None,
method='cs')
assert_check_partials(cpd)
def test_nodes_and_weights_3(self):
x_3, w_3 = lgl(3)
assert_almost_equal(x_3, x_i[3], decimal=6)
assert_almost_equal(w_3, w_i[3], decimal=6)
def test_polynomial_control_group_matrix_rungekutta(self):
transcription = 'runge-kutta'
compressed = True
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order='RK4',
segment_ends=segends,
transcription=transcription,
compressed=compressed)
p = om.Problem(model=om.Group())
controls = {'a': PolynomialControlOptionsDictionary()}
controls['a']['units'] = 'm'
controls['a']['order'] = 3
controls['a']['opt'] = True
controls['a']['shape'] = (3, 1)
ivc = om.IndepVarComp()
p.model.add_subsystem('ivc', ivc, promotes_outputs=['*'])
ivc.add_output('t_initial', val=0.0, units='s')
ivc.add_output('t_duration', val=10.0, units='s')
p.model.add_subsystem('time_comp',
subsys=TimeComp(num_nodes=gd.num_nodes, node_ptau=gd.node_ptau,
node_dptau_dstau=gd.node_dptau_dstau, units='s'),
promotes_inputs=['t_initial', 't_duration'],
promotes_outputs=['time', 'dt_dstau'])
polynomial_control_group = PolynomialControlGroup(grid_data=gd,
polynomial_control_options=controls,
time_units='s')
p.model.add_subsystem('polynomial_control_group',
subsys=polynomial_control_group,
promotes_inputs=['*'],
promotes_outputs=['*'])
# p.model.connect('dt_dstau', 'control_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
p['t_initial'] = 0.0
p['t_duration'] = 3.0
p.run_model()
control_nodes_ptau, _ = lgl(controls['a']['order'] + 1)
t_control_input = p['t_initial'] + 0.5 * (control_nodes_ptau + 1) * p['t_duration']
t_all = p['time']
p['polynomial_controls:a'][:, 0, 0] = f_a(t_control_input)
p['polynomial_controls:a'][:, 1, 0] = f_b(t_control_input)
p['polynomial_controls:a'][:, 2, 0] = f_c(t_control_input)
p.run_model()
a0_value_expected = f_a(t_all)
a1_value_expected = f_b(t_all)
a2_value_expected = f_c(t_all)
a0_rate_expected = f1_a(t_all)
a1_rate_expected = f1_b(t_all)
a2_rate_expected = f1_c(t_all)
a0_rate2_expected = f2_a(t_all)
a1_rate2_expected = f2_b(t_all)
a2_rate2_expected = f2_c(t_all)
assert_almost_equal(p['polynomial_control_values:a'][:, 0, 0],
a0_value_expected)
assert_almost_equal(p['polynomial_control_values:a'][:, 1, 0],
a1_value_expected)
assert_almost_equal(p['polynomial_control_values:a'][:, 2, 0],
a2_value_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate'][:, 0, 0],
a0_rate_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate'][:, 1, 0],
a1_rate_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate'][:, 2, 0],
a2_rate_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate2'][:, 0, 0],
a0_rate2_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate2'][:, 1, 0],
a1_rate2_expected)
assert_almost_equal(p['polynomial_control_rates:a_rate2'][:, 2, 0],
a2_rate2_expected)
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def main():
prob = om.Problem()
model = prob.model
num_seg = 15
seg_ends, _ = lgl(num_seg + 1)
traj = model.add_subsystem('traj', dm.Trajectory())
phase = traj.add_phase('phase0',
dm.Phase(ode_class=AircraftODE,
transcription=dm.Radau(num_segments=num_seg,
segment_ends=seg_ends,
order=3, compressed=False)))
assumptions = 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')
prob.driver = om.pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SNOPT'
phase.set_time_options(initial_bounds=(0, 0), duration_bounds=(300, 10000))
phase.set_state_options('range', units='NM', fix_initial=True,
fix_final=False, lower=0, upper=2000)
phase.set_state_options('mass_fuel', units='lbm', fix_initial=True, fix_final=True,
upper=1.5E5, lower=0.0)
phase.set_state_options('alt', units='kft', fix_initial=True, fix_final=True,
lower=0.0, upper=60)
phase.add_control('climb_rate', units='ft/min', opt=True, lower=-3000, upper=3000,
rate_continuity=True, rate2_continuity=False)
phase.add_control('mach', units=None, opt=False)
phase.add_input_parameter('S', units='m**2')
phase.add_input_parameter('mass_empty', units='kg')
phase.add_input_parameter('mass_payload', units='kg')
phase.add_path_constraint('propulsion.tau', lower=0.01, upper=2.0, shape=(1,))
model.connect('assumptions.S', 'traj.phase0.input_parameters:S')
model.connect('assumptions.mass_empty', 'traj.phase0.input_parameters:mass_empty')
model.connect('assumptions.mass_payload', 'traj.phase0.input_parameters:mass_payload')
phase.add_objective('range', loc='final')
prob.setup()
prob['traj.phase0.t_initial'] = 0.0
prob['traj.phase0.t_duration'] = 3600.0
prob['traj.phase0.states:range'][:] = phase.interpolate(ys=(0, 724.0), nodes='state_input')
prob['traj.phase0.states:mass_fuel'][:] = phase.interpolate(
ys=(30000, 1e-3), nodes='state_input')
prob['traj.phase0.states:alt'][:] = 10.0
prob['traj.phase0.controls:mach'][:] = 0.8
prob['assumptions.S'] = 427.8
prob['assumptions.mass_empty'] = 0.15E6
prob['assumptions.mass_payload'] = 84.02869 * 400
with open('total_jac_info.pickle', 'rb') as file:
jac = pickle.load(file)
with open('lower_bounds_info.pickle', 'rb') as file:
lbs = pickle.load(file)
with open('upper_bounds_info.pickle', 'rb') as file:
ubs = pickle.load(file)
# sc = None
# sc = IsoScaler(jac, lbs, ubs)
sc = PJRNScaler(jac, lbs, ubs)
autoscale(prob, autoscaler=sc)
prob.run_driver()
return prob
def test_nodes_and_weights_6(self):
x_6, w_6 = lgl(6)
assert_almost_equal(x_6, x_i[6], decimal=6)
assert_almost_equal(w_6, w_i[6], decimal=6)
def test_nodes_and_weights_5(self):
x_5, w_5 = lgl(5)
assert_almost_equal(x_5, x_i[5], decimal=6)
assert_almost_equal(w_5, w_i[5], decimal=6)
def test_nodes_and_weights_4(self):
x_4, w_4 = lgl(4)
assert_almost_equal(x_4, x_i[4], decimal=6)
assert_almost_equal(w_4, w_i[4], decimal=6)
def test_overlapping_phases_make_plot(self):
prob = om.Problem()
opt = prob.driver = om.ScipyOptimizeDriver()
opt.declare_coloring()
opt.options['optimizer'] = 'SLSQP'
num_seg = 5
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
# First phase: normal operation.
transcription = dm.Radau(num_segments=num_seg, order=5, segment_ends=seg_ends,
compressed=False)
phase0 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p0 = traj.add_phase('phase0', phase0)
traj_p0.set_time_options(fix_initial=True, fix_duration=True)
traj_p0.add_state('state_of_charge', fix_initial=True, fix_final=False,
targets=['SOC'], rate_source='dXdt:SOC')
# Second phase: normal operation.
phase1 = dm.Phase(ode_class=BatteryODE, transcription=transcription)
traj_p1 = traj.add_phase('phase1', phase1)
traj_p1.set_time_options(fix_initial=False, fix_duration=True)
traj_p1.add_state('state_of_charge', fix_initial=False, fix_final=False,
targets=['SOC'], rate_source='dXdt:SOC')
traj_p1.add_objective('time', loc='final')
# Second phase, but with battery failure.
phase1_bfail = dm.Phase(ode_class=BatteryODE, ode_init_kwargs={'num_battery': 2},
transcription=transcription)
traj_p1_bfail = traj.add_phase('phase1_bfail', phase1_bfail)
traj_p1_bfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_bfail.add_state('state_of_charge', fix_initial=False, fix_final=False,
targets=['SOC'], rate_source='dXdt:SOC')
# Second phase, but with motor failure.
phase1_mfail = dm.Phase(ode_class=BatteryODE, ode_init_kwargs={'num_motor': 2},
transcription=transcription)
traj_p1_mfail = traj.add_phase('phase1_mfail', phase1_mfail)
traj_p1_mfail.set_time_options(fix_initial=False, fix_duration=True)
traj_p1_mfail.add_state('state_of_charge', fix_initial=False, fix_final=False,
targets=['SOC'], rate_source='dXdt:SOC')
traj.link_phases(phases=['phase0', 'phase1'], vars=['state_of_charge', 'time'])
traj.link_phases(phases=['phase0', 'phase1_bfail'], vars=['state_of_charge', 'time'])
traj.link_phases(phases=['phase0', 'phase1_mfail'], vars=['state_of_charge', 'time'])
prob.model.options['assembled_jac_type'] = 'csc'
prob.model.linear_solver = om.DirectSolver(assemble_jac=True)
prob.setup()
prob['traj.phase0.t_initial'] = 0
prob['traj.phase0.t_duration'] = 1.0*3600
prob['traj.phase1.t_initial'] = 1.0*3600
prob['traj.phase1.t_duration'] = 1.0*3600
prob['traj.phase1_bfail.t_initial'] = 1.0*3600
prob['traj.phase1_bfail.t_duration'] = 1.0*3600
prob['traj.phase1_mfail.t_initial'] = 1.0*3600
prob['traj.phase1_mfail.t_duration'] = 1.0*3600
prob.set_solver_print(level=0)
dm.run_problem(prob, simulate=True, make_plots=False,
simulation_record_file='simulation_record_file.db')
timeseries_plots('dymos_solution.db', simulation_record_file='simulation_record_file.db')
self.assertTrue(os.path.exists('plots/time_phase.png'))
self.assertTrue(os.path.exists('plots/states_state_of_charge.png'))
self.assertTrue(os.path.exists('plots/state_rates_state_of_charge.png'))
0.0, 0.0, 0.0, 0.0, 457.0, 1744.0, 1744.0, 457.0, 457.0, 457.0, 457.0,
457.0
])
h_seg += 690.0 # add ground level
# Create the problem
prob = om.Problem()
opt = prob.driver = om.ScipyOptimizeDriver()
opt.declare_coloring()
opt.options['optimizer'] = 'SLSQP'
# Set up the segments based on the type of mission
num_seg = 11
seg_ends, _ = lgl(num_seg + 1)
traj = prob.model.add_subsystem('traj', dm.Trajectory())
'''
Set the values of the design parameters
'''
# Geometric parameters
# --------------------
traj.add_design_parameter('x',
val=10e-3,
lower=1e-3,
upper=80e-3,
units='m',
opt=True,
dynamic=False)
# targets={'traj_p1':'x', 'traj_p2':'x', 'traj_p3':'x',
# 'traj_p4':'x', 'traj_p5':'x', 'traj_p6':'x',
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()
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(fix_initial=True,
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',
equals=1e-3,
linear=False)
phase.add_boundary_constraint('alt',
loc='final',
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',
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',
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'],
opt=False)
phase.add_parameter(
'S',
targets=['aero.S', 'flight_equilibrium.S', 'propulsion.S'],
units='m**2')
phase.add_parameter('mass_empty',
targets=['mass_comp.mass_empty'],
units='kg')
phase.add_parameter('mass_payload',
targets=['mass_comp.mass_payload'],
units='kg')
phase.add_path_constraint('propulsion.tau', lower=0.01, upper=2.0)
p.model.connect('assumptions.S', 'phase0.parameters:S')
p.model.connect('assumptions.mass_empty', 'phase0.parameters:mass_empty')
p.model.connect('assumptions.mass_payload',
'phase0.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.interp('range', (0, 724.0))
p['phase0.states:mass_fuel'] = phase.interp('mass_fuel', (30000, 1e-3))
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_steady_aircraft_for_docs(self):
import numpy as np
import matplotlib.pyplot as plt
from openmdao.api import Problem, Group, pyOptSparseDriver, DirectSolver, IndepVarComp
from openmdao.utils.assert_utils import assert_rel_error
from dymos import Phase
from dymos.examples.aircraft_steady_flight.aircraft_ode import AircraftODE
from dymos.utils.lgl import lgl
p = Problem(model=Group())
p.driver = pyOptSparseDriver()
p.driver.options['optimizer'] = 'SLSQP'
p.driver.options['dynamic_simul_derivs'] = True
num_seg = 15
seg_ends, _ = lgl(num_seg + 1)
phase = Phase('gauss-lobatto',
ode_class=AircraftODE,
num_segments=num_seg,
segment_ends=seg_ends,
transcription_order=5,
compressed=False)
# Pass design parameters in externally from an external source
assumptions = p.model.add_subsystem('assumptions', IndepVarComp())
assumptions.add_output('mach', val=0.8, units=None)
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(fix_initial=True,
duration_bounds=(300, 10000),
duration_ref=3600)
phase.set_state_options('range', units='NM', fix_initial=True, fix_final=False,
scaler=0.001,
defect_scaler=1.0E-2)
phase.set_state_options('mass_fuel', units='lbm', fix_initial=True, fix_final=True,
upper=1.5E5, lower=0.0, scaler=1.0E-5, defect_scaler=1.0E-1)
phase.add_control('alt', units='kft', opt=True, lower=0.0, upper=50.0,
rate_param='climb_rate',
rate_continuity=True, rate_continuity_scaler=1.0,
rate2_continuity=True, rate2_continuity_scaler=1.0, ref=1.0,
fix_initial=True, fix_final=True)
phase.add_input_parameter('mach', units=None)
phase.add_input_parameter('S', units='m**2')
phase.add_input_parameter('mass_empty', units='kg')
phase.add_input_parameter('mass_payload', units='kg')
phase.add_path_constraint('propulsion.tau', lower=0.01, upper=1.0)
phase.add_path_constraint('alt_rate', units='ft/min', lower=-3000, upper=3000, ref=3000)
p.model.connect('assumptions.mach', 'phase0.input_parameters:mach')
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=-1.0)
p.model.linear_solver = DirectSolver(assemble_jac=True)
p.setup()
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 3600.0
p['phase0.states:range'] = phase.interpolate(ys=(0, 1000.0), nodes='state_input')
p['phase0.states:mass_fuel'] = phase.interpolate(ys=(30000, 0), nodes='state_input')
p['phase0.controls:alt'][:] = 10.0
p['assumptions.mach'][:] = 0.8
p['assumptions.S'] = 427.8
p['assumptions.mass_empty'] = 0.15E6
p['assumptions.mass_payload'] = 84.02869 * 400
p.run_driver()
exp_out = phase.simulate(times=np.linspace(0, p['phase0.t_duration'], 500), record=True,
record_file='test_doc_aircraft_steady_flight_rec.db')
assert_rel_error(self, phase.get_values('range', units='NM')[-1], 726.7, tolerance=1.0E-2)
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('alt', nodes='all', units='ft'),
'ro')
plt.plot(exp_out.get_values('time'),
exp_out.get_values('alt', units='ft'),
'b-')
plt.suptitle('Altitude vs Time')
plt.xlabel('Time (s)')
plt.ylabel('Altitude (ft)')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('alt_rate', nodes='all', units='ft/min'),
'ro')
plt.plot(exp_out.get_values('time'),
exp_out.get_values('alt_rate', units='ft/min'),
'b-')
plt.suptitle('Climb Rate vs Time')
plt.xlabel('Time (s)')
plt.ylabel('Climb Rate (ft/min)')
plt.figure()
plt.plot(phase.get_values('time', nodes='all'),
phase.get_values('mass_fuel', nodes='all', units='lbm'),
'ro')
plt.plot(exp_out.get_values('time'),
exp_out.get_values('mass_fuel', units='lbm'),
'b-')
plt.suptitle('Fuel Mass vs Time')
plt.xlabel('Time (s)')
plt.ylabel('Fuel Mass (lbm)')
plt.show()
