Aviary Model Without AviaryProblem#
Because Aviary is, at its core, a very sophisticated wrapper for creating an OpenMDAO problem, an advanced user can replicate Aviary’s capabilities in a custom Python script. At this level, users have full access to Python and OpenMDAO methods that Aviary calls. They can use the complete set of Aviary’s methods, functionalities, and classes to construct and fine-tune their aircraft models.
This enables users to have supreme control over every aspect of the model, including subsystems, connections, and advanced optimization techniques.
We will show this approach using an example that implements all methods that are available in aviary/core/aviary_problem.py but without directly calling AviaryProblem.
Note
For each of these examples we have set max_iter = 0, which means that the optimization will not run. This is done to reduce the computational time for the examples. If you want to run the optimization, you can set max_iter = 100 or some similar value.
# Testing Cell
import os
from aviary.utils.functions import get_path
folder = 'validation_cases/benchmark_tests'
file = 'test_FLOPS_based_sizing_N3CC.py'
get_path(os.path.join(folder, file))
PosixPath('/home/runner/work/Aviary/Aviary/aviary/validation_cases/benchmark_tests/test_FLOPS_based_sizing_N3CC.py')
Advanced Single Aisle Example#
Earlier, we have shown how to follow the standard steps to build (and slightly customize) an Aviary problem, but sometimes you may find that the model you have in mind does not match that predefined structure. If your needs differ from the setup Aviary uses, we can write special methods ourselves. This example will show you how to do that.
In aviary/validation_cases/benchmark_tests folder, there is an aircraft full mission benchmark test test_FLOPS_based_sizing_N3CC.py. Now, we will show how to recreate that example, without using the AviaryProblem. The key is that we follow the same steps:
__init__()load_inputs()check_and_preprocess_inputs()build_model()add_pre_mission_systems()add_phases()add_post_mission_systems()link_phases()add_driver()add_design_variables()(optional)add_objective()setup()run_aviary_problem()
Examining the design case#
"""
This is an example of running a coupled aircraft design-mission optimization in Aviary without using
the lower level APIs.
It runs the same aircraft and mission as the `simple mission` example.
The aircraft is loaded from .csv and the default energy-state phase_info dictionary is imported
from the file.
This script unwraps the subsystem and trajectory builders, exposing how Aviary interacts with
openMDAO and Dymos. It is divided into sections separated by '####' with the level2 api calls
commented out so you can see what happens in each level2 method.
"""
import warnings
import dymos as dm
import openmdao.api as om
import aviary.api as av
from aviary.core.static_group import StaticGroup
from aviary.mission.energy_state.ode.energy_state_ODE import EnergyStateODE
from aviary.mission.flight_phase_builder import FlightPhaseOptions
from aviary.models.missions.energy_state_default import phase_info
from aviary.utils.aviary_values import AviaryValues
from aviary.variable_info.enums import Verbosity
from aviary.variable_info.functions import setup_model_options, setup_trajectory_params
from aviary.variable_info.variable_meta_data import CoreMetaData
from aviary.variable_info.variables import Aircraft, Dynamic, Mission
class L3SubsystemsGroup(om.Group):
"""Group that contains all pre-mission groups of core Aviary subsystems (geometry, mass, propulsion, aerodynamics)."""
def initialize(self):
self.options.declare(
'aviary_options',
types=AviaryValues,
desc='collection of Aircraft/Mission specific options',
)
self.code_origin_overrides = []
# Toggle this boolean option to run with shooting vs collocation transcription:
# shooting = True
shooting = False
prob = av.AviaryProblem()
#####
# prob.load_inputs(csv_path, phase_info)
csv_path = 'models/aircraft/advanced_single_aisle/advanced_single_aisle_FLOPS.csv'
aviary_inputs, _ = av.create_vehicle(csv_path)
engine = av.build_engine_deck(aviary_inputs)
prob.model.mission_info = {}
for phase_name in phase_info:
if phase_name not in ['pre_mission', 'post_mission']:
prob.model.mission_info[phase_name] = phase_info[phase_name]
#####
# prob.check_and_preprocess_inputs()
av.preprocess_options(aviary_inputs, engine_models=[engine])
#####
# prob.add_pre_mission_systems()
aerodynamics = av.CoreAerodynamicsBuilder(code_origin=av.LegacyCode('FLOPS'))
geometry = av.CoreGeometryBuilder(code_origin=av.LegacyCode('FLOPS'))
mass = av.CoreMassBuilder(code_origin=av.LegacyCode('FLOPS'))
propulsion = av.CorePropulsionBuilder(engine_models=engine)
prob.model.core_subsystems = {
'propulsion': propulsion,
'geometry': geometry,
'mass': mass,
'aerodynamics': aerodynamics,
}
prob.meta_data = CoreMetaData.copy()
prob.model.add_subsystem(
'pre_mission',
StaticGroup(),
promotes_inputs=['aircraft:*', 'mission:*'],
promotes_outputs=['aircraft:*', 'mission:*'],
)
# This is a combination of prob.add_pre_mission_systems and prob.setup()
# In the aviary code add_pre_mission_systems only instantiates the objects and methods, the build method is called in prob.setup()
prob.model.pre_mission.add_subsystem(
'core_propulsion',
propulsion.build_pre_mission(aviary_inputs, subsystem_options={}),
)
# adding another group subsystem to match the L2 example
prob.model.pre_mission.add_subsystem(
'core_subsystems',
L3SubsystemsGroup(aviary_options=aviary_inputs),
promotes_inputs=['*'],
promotes_outputs=['*'],
)
prob.model.pre_mission.core_subsystems.add_subsystem(
'core_geometry',
geometry.build_pre_mission(aviary_inputs, subsystem_options={}),
promotes_inputs=['*'],
promotes_outputs=['*'],
)
prob.model.pre_mission.core_subsystems.add_subsystem(
'core_aerodynamics',
aerodynamics.build_pre_mission(aviary_inputs, subsystem_options={}),
promotes_inputs=['*'],
promotes_outputs=['*'],
)
prob.model.pre_mission.core_subsystems.add_subsystem(
'core_mass',
mass.build_pre_mission(aviary_inputs, subsystem_options={}),
promotes_inputs=['*'],
promotes_outputs=['*'],
)
#####
# prob.add_phases()
phase_list = ['climb', 'cruise', 'descent']
prob.traj = prob.model.add_subsystem('traj', dm.Trajectory())
default_mission_subsystems = [
prob.model.core_subsystems['aerodynamics'],
prob.model.core_subsystems['propulsion'],
]
phases = {}
for phase_idx, phase_name in enumerate(phase_list):
base_phase_options = prob.model.mission_info[phase_name]
phase_options = {}
for key, val in base_phase_options.items():
phase_options[key] = val
phase_options['user_options'] = {}
for key, val in base_phase_options['user_options'].items():
phase_options['user_options'][key] = val
# Create the dymos phase object and add it to the trajectory
# phase_builder = EnergyPhase
# Unpack the phase info without using the phase builder
# phase_object = phase_builder.from_phase_info(
# phase_name, phase_options, default_mission_subsystems, meta_data=prob.meta_data
# )
# user_options dict entries need reformatting
# if the value is not a tuple, wrap it in a tuple with the second entry of 'unitless'
for key, value in phase_options['user_options'].items():
if not isinstance(value, tuple):
phase_options['user_options'][key] = (value, 'unitless')
subsystem_options = phase_options.get('subsystem_options', {})
user_options = phase_options.get('user_options', ())
initial_guesses = AviaryValues(
phase_options.get('initial_guesses', ())
) # Not used in this example
# instantiate the PhaseBuilderBaseClass:
# phase_builder = cls(
# name,
# subsystem_options=subsystem_options,
# user_options=user_options,
# initial_guesses=initial_guesses,
# meta_data=meta_data,
# core_subsystems=core_subsystems,
# external_subsystems=external_subsystems,
# transcription=transcription,
# )
# This basically just adds these objects to the class - we have them all available in this L3 script so have no need for the extra class!
transcription = None
ode_class = None
# Now build the phase using the instantiated phase object:
# phase = phase_object.build_phase(aviary_options=aviary_inputs)
# phase: dm.Phase = super().build_phase(aviary_options)
# For L3 we need to do this without the builder
ode_class = EnergyStateODE
user_options = FlightPhaseOptions(user_options)
num_segments = user_options['num_segments']
order = user_options['order']
if shooting:
nodes_per_seg = order * num_segments
transcription = dm.PicardShooting(
num_segments=1, nodes_per_seg=nodes_per_seg, solve_segments='forward'
)
else:
seg_ends, _ = dm.utils.lgl.lgl(num_segments + 1)
transcription = dm.Radau(
num_segments=num_segments, order=order, compressed=True, segment_ends=seg_ends
)
kwargs = {
'meta_data': prob.meta_data,
'subsystem_options': subsystem_options,
'throttle_enforcement': user_options['throttle_enforcement'],
'throttle_allocation': user_options['throttle_allocation'],
'subsystems': default_mission_subsystems,
}
kwargs = {'aviary_options': aviary_inputs, **kwargs}
phase = dm.Phase(ode_class=ode_class, transcription=transcription, ode_init_kwargs=kwargs)
tx_mission_bus = dm.GaussLobatto(
num_segments=transcription.options['num_segments'], order=3, compressed=True
) # equally spaced points in this output - this is going to be better than using picard or radau.
phase.add_timeseries(name='mission_bus_variables', transcription=tx_mission_bus, subset='all')
num_engine_type = len(
aviary_inputs.get_val(Aircraft.Engine.NUM_ENGINES)
) # not used in this example
throttle_enforcement = user_options['throttle_enforcement']
no_descent = user_options['no_descent'] # not used in this example
no_climb = user_options['no_climb'] # not used in this example
constraints = user_options['constraints'] # not used in this example
ground_roll = user_options['ground_roll'] # not used in this example
phase.add_state(
name=Dynamic.Vehicle.MASS,
fix_initial=False,
input_initial=False,
lower=user_options['mass_bounds'][0][0],
upper=user_options['mass_bounds'][0][1],
units=user_options['mass_bounds'][1],
rate_source=Dynamic.Vehicle.Propulsion.FUEL_MASS_FLOW_RATE_NEGATIVE_TOTAL,
ref=user_options['mass_ref'][0],
ref0=user_options['mass_ref0'][0],
defect_ref=user_options['mass_defect_ref'][0],
solve_segments='forward' if user_options['mass_solve_segments'] else None,
)
phase.add_state(
name=Dynamic.Mission.DISTANCE,
fix_initial=False,
input_initial=False,
lower=user_options['distance_bounds'][0][0],
upper=user_options['distance_bounds'][0][1],
units=user_options['distance_bounds'][1],
rate_source=Dynamic.Mission.DISTANCE_RATE,
ref=user_options['distance_ref'][0],
ref0=user_options['distance_ref0'][0],
defect_ref=user_options['distance_defect_ref'][0],
solve_segments='forward' if user_options['distance_solve_segments'] else None,
)
phase.add_control(
Dynamic.Atmosphere.MACH,
targets=Dynamic.Atmosphere.MACH,
opt=user_options['mach_optimize'],
lower=user_options['mach_bounds'][0][0],
upper=user_options['mach_bounds'][0][1],
ref=user_options['mach_ref'][0],
ref0=user_options['mach_ref0'][0],
control_type='polynomial',
order=user_options['mach_polynomial_order'],
rate_targets=[Dynamic.Atmosphere.MACH_RATE],
)
phase.add_timeseries_output(Dynamic.Atmosphere.MACH)
phase.add_control(
Dynamic.Mission.ALTITUDE,
targets=Dynamic.Mission.ALTITUDE,
opt=user_options['altitude_optimize'],
lower=user_options['altitude_bounds'][0][0],
upper=user_options['altitude_bounds'][0][1],
ref=user_options['altitude_ref'][0],
ref0=user_options['altitude_ref0'][0],
control_type='polynomial',
order=user_options['altitude_polynomial_order'],
units=user_options['altitude_bounds'][1],
rate_targets=[Dynamic.Mission.ALTITUDE_RATE],
)
phase.add_timeseries_output(Dynamic.Mission.ALTITUDE)
# if throttle_enforcement == 'control':
# add throttle as control
# not used in this example
phase.add_timeseries_output(
Dynamic.Vehicle.Propulsion.THRUST_TOTAL,
output_name=Dynamic.Vehicle.Propulsion.THRUST_TOTAL,
units='lbf',
)
phase.add_timeseries_output(Dynamic.Vehicle.DRAG, output_name=Dynamic.Vehicle.DRAG, units='lbf')
phase.add_timeseries_output(
Dynamic.Mission.SPECIFIC_ENERGY_RATE_EXCESS,
output_name=Dynamic.Mission.SPECIFIC_ENERGY_RATE_EXCESS,
units='m/s',
)
phase.add_timeseries_output(
Dynamic.Vehicle.Propulsion.FUEL_MASS_FLOW_RATE_NEGATIVE_TOTAL,
output_name=Dynamic.Vehicle.Propulsion.FUEL_MASS_FLOW_RATE_NEGATIVE_TOTAL,
units='lbm/h',
)
phase.add_timeseries_output(
Dynamic.Vehicle.Propulsion.ELECTRIC_POWER_IN_TOTAL,
output_name=Dynamic.Vehicle.Propulsion.ELECTRIC_POWER_IN_TOTAL,
units='kW',
)
phase.add_timeseries_output(
Dynamic.Mission.ALTITUDE_RATE,
output_name=Dynamic.Mission.ALTITUDE_RATE,
units='ft/s',
)
if throttle_enforcement != 'control':
phase.add_timeseries_output(
Dynamic.Vehicle.Propulsion.THROTTLE,
output_name=Dynamic.Vehicle.Propulsion.THROTTLE,
units='unitless',
)
phase.add_timeseries_output(
Dynamic.Mission.VELOCITY,
output_name=Dynamic.Mission.VELOCITY,
units='m/s',
)
phase.add_path_constraint(
Dynamic.Vehicle.Propulsion.THROTTLE,
lower=0.0,
upper=1.0,
units='unitless',
)
prob.traj.add_phase(phase_name, phase)
phases[phase_name] = phase
climb = phases['climb']
cruise = phases['cruise']
descent = phases['descent']
externs = {'climb': {}, 'cruise': {}, 'descent': {}}
for default_subsys in default_mission_subsystems:
params = default_subsys.get_parameters(
aviary_inputs=aviary_inputs,
user_options=None,
subsystem_options=None,
)
for key, val in params.items():
for phname in externs:
externs[phname][key] = val
prob.traj = setup_trajectory_params(
prob.model, prob.traj, aviary_inputs, external_parameters=externs
)
# Need aviary inputs assigned to the problem object for other functions below
prob.aviary_inputs = aviary_inputs
#####
# prob.add_post_mission_systems()
prob.model.add_subsystem(
'post_mission',
om.Group(),
promotes_inputs=['*'],
promotes_outputs=['*'],
)
climb.set_state_options(Dynamic.Vehicle.MASS, fix_initial=False, input_initial=False)
climb.set_state_options(Dynamic.Mission.DISTANCE, fix_initial=True, input_initial=False)
climb.set_time_options(
fix_initial=False,
initial_bounds=(0, 0),
initial_ref=600,
duration_bounds=(3840, 11520),
duration_ref=7680.0,
)
cruise.set_time_options(
duration_bounds=(3390, 18000),
duration_ref=10695.0,
)
descent.set_time_options(
duration_bounds=(1740, 5220),
duration_ref=3480.0,
)
eq = prob.model.add_subsystem(
'link_climb_mass',
om.EQConstraintComp(),
promotes_inputs=[('rhs:mass', Mission.GROSS_MASS)],
)
eq.add_eq_output('mass', eq_units='lbm', normalize=False, ref=100000.0, add_constraint=True)
prob.model.connect(
'traj.climb.states:mass',
'link_climb_mass.lhs:mass',
src_indices=[0],
flat_src_indices=True,
)
target_range = 1906.0
prob.model.add_constraint(Mission.RANGE, equals=target_range, ref=target_range)
# for reference this is the end of builder.add_post_mission_systems()
ecomp = om.ExecComp(
'fuel_burned = initial_mass - mass_final',
initial_mass={'units': 'lbm'},
mass_final={'units': 'lbm'},
fuel_burned={'units': 'lbm'},
)
prob.model.post_mission.add_subsystem(
'fuel_burned',
ecomp,
promotes=[('fuel_burned', Mission.FUEL_MASS)],
)
prob.model.connect(
'traj.climb.timeseries.mass',
'fuel_burned.initial_mass',
src_indices=[0],
)
prob.model.connect(
'traj.descent.timeseries.mass',
'fuel_burned.mass_final',
src_indices=[-1],
)
reserve_fuel_mass_additional = prob.aviary_inputs.get_val(
Mission.RESERVE_FUEL_MASS_ADDITIONAL, units='lbm'
)
reserve_fuel_mass = om.ExecComp(
'reserve_fuel_mass = reserve_fuel_frac_mass + reserve_fuel_mass_additional + reserve_fuel_burned',
reserve_fuel_mass={'units': 'lbm', 'shape': 1},
reserve_fuel_frac_mass={'units': 'lbm', 'val': 0},
reserve_fuel_mass_additional={'units': 'lbm', 'val': reserve_fuel_mass_additional},
reserve_fuel_burned={'units': 'lbm', 'val': 0},
)
prob.model.post_mission.add_subsystem(
'reserve_fuel_mass',
reserve_fuel_mass,
promotes_inputs=[
'reserve_fuel_frac_mass',
('reserve_fuel_mass_additional', Mission.RESERVE_FUEL_MASS_ADDITIONAL),
('reserve_fuel_burned', Mission.RESERVE_FUEL_MASS),
],
promotes_outputs=[('reserve_fuel_mass', Mission.TOTAL_RESERVE_FUEL_MASS)],
)
ecomp = om.ExecComp(
'overall_fuel = fuel_burned + reserve_fuel_mass',
overall_fuel={'units': 'lbm', 'shape': 1},
fuel_burned={'units': 'lbm'}, # from main_phases only
reserve_fuel_mass={'units': 'lbm', 'shape': 1},
)
prob.model.post_mission.add_subsystem(
'fuel_calc',
ecomp,
promotes_inputs=[
('fuel_burned', Mission.FUEL_MASS),
('reserve_fuel_mass', Mission.TOTAL_RESERVE_FUEL_MASS),
],
promotes_outputs=[('overall_fuel', Mission.TOTAL_FUEL_MASS)],
)
# If target distances have been set per phase then there is a block of code to add here.
# In this case individual phases don't have target distances.
ecomp = om.ExecComp(
'mass_resid = operating_empty_mass + overall_fuel + payload_mass - initial_mass',
operating_empty_mass={'units': 'lbm'},
overall_fuel={'units': 'lbm'},
payload_mass={'units': 'lbm'},
initial_mass={'units': 'lbm'},
mass_resid={'units': 'lbm'},
)
prob.model.post_mission.add_subsystem(
'mass_constraint',
ecomp,
promotes_inputs=[
('operating_empty_mass', Mission.OPERATING_MASS),
('overall_fuel', Mission.TOTAL_FUEL_MASS),
('payload_mass', Aircraft.CrewPayload.TOTAL_PAYLOAD_MASS),
('initial_mass', Mission.GROSS_MASS),
],
promotes_outputs=[('mass_resid', Mission.Constraints.MASS_RESIDUAL)],
)
ecomp = om.ExecComp(
'excess_fuel_capacity = total_fuel_capacity - unusable_fuel - overall_fuel',
total_fuel_capacity={'units': 'lbm'},
unusable_fuel={'units': 'lbm'},
overall_fuel={'units': 'lbm'},
excess_fuel_capacity={'units': 'lbm'},
)
prob.model.post_mission.add_subsystem(
'excess_fuel_constraint',
ecomp,
promotes_inputs=[
('total_fuel_capacity', Aircraft.Fuel.MAX_CAPACITY_MASS),
('unusable_fuel', Aircraft.Fuel.UNUSABLE_FUEL_MASS),
('overall_fuel', Mission.TOTAL_FUEL_MASS),
],
promotes_outputs=[('excess_fuel_capacity', Mission.Constraints.EXCESS_FUEL_MASS_CAPACITY)],
)
prob.model.add_constraint(Mission.Constraints.EXCESS_FUEL_MASS_CAPACITY, lower=0, units='lbm')
#####
# prob.link_phases()
all_subsystems = []
all_subsystems.append(prob.model.core_subsystems['propulsion'])
prob.traj.link_phases(phase_list, ['time'], ref=None, connected=True)
prob.traj.link_phases(phase_list, [Dynamic.Vehicle.MASS], ref=None, connected=True)
prob.traj.link_phases(phase_list, [Dynamic.Mission.DISTANCE], ref=None, connected=True)
prob.model.connect(
'traj.descent.timeseries.distance',
Mission.RANGE,
src_indices=[-1],
flat_src_indices=True,
)
#####
# Each driver requires different settings - uncomment the one to be used:
# prob.add_driver('SLSQP', max_iter=50)
# SLSQP Optimizer Settings
# prob.driver = om.ScipyOptimizeDriver()
# prob.driver.options['optimizer'] = 'SLSQP'
# prob.driver.declare_coloring(show_summary=False)
# prob.driver.options['disp'] = True
# prob.driver.options['tol'] = 1e-9
# prob.driver.options['maxiter'] = 50
# prob.add_driver('IPOPT', max_iter=50)
# IPOPT Optimizer Settings
prob.driver = om.pyOptSparseDriver()
prob.driver.options['optimizer'] = 'IPOPT'
prob.driver.declare_coloring(show_summary=False)
prob.driver.opt_settings['print_user_options'] = 'no'
prob.driver.opt_settings['print_frequency_iter'] = 10
prob.driver.opt_settings['print_level'] = 3
prob.driver.opt_settings['tol'] = 1.0e-6
prob.driver.opt_settings['mu_init'] = 1e-5
prob.driver.opt_settings['max_iter'] = 300
prob.driver.opt_settings['nlp_scaling_method'] = 'gradient-based'
prob.driver.opt_settings['alpha_for_y'] = 'safer-min-dual-infeas'
prob.driver.opt_settings['mu_strategy'] = 'monotone'
prob.driver.options['print_results'] = 'minimal'
# prob.add_driver('SNOPT', max_iter=50)
# SNOPT Optimizer Settings #
# prob.driver = om.pyOptSparseDriver()
# prob.driver.options['optimizer'] = 'SNOPT'
# prob.driver.declare_coloring(show_summary=False)
# prob.driver.opt_settings['iSumm'] = 6
# prob.driver.opt_settings['iPrint'] = 0
# prob.driver.opt_settings['Major iterations limit'] = 50
# prob.driver.opt_settings['Major optimality tolerance'] = 1e-4
# prob.driver.opt_settings['Major feasibility tolerance'] = 1e-7
# prob.driver.options['print_results'] = 'minimal'
#####
# prob.add_design_variables()
prob.model.add_design_var(
Aircraft.Design.GROSS_MASS,
lower=100000.0,
upper=None,
units='lbm',
ref=175e3,
)
prob.model.add_design_var(
Mission.GROSS_MASS,
lower=100000.0,
upper=None,
units='lbm',
ref=175e3,
)
prob.model.add_subsystem(
'gtow_constraint',
om.EQConstraintComp(
'GTOW',
eq_units='lbm',
normalize=True,
add_constraint=True,
),
promotes_inputs=[
('lhs:GTOW', Aircraft.Design.GROSS_MASS),
('rhs:GTOW', Mission.GROSS_MASS),
],
)
#####
# prob.add_objective()
prob.model.add_subsystem(
'fuel_obj',
om.ExecComp(
'reg_objective = overall_fuel/10000 + ascent_duration/30.',
reg_objective={'val': 0.0, 'units': 'unitless'},
ascent_duration={'units': 's', 'shape': 1},
overall_fuel={'units': 'lbm'},
),
promotes_inputs=[
('ascent_duration', Mission.Takeoff.ASCENT_DURATION),
('overall_fuel', Mission.TOTAL_FUEL_MASS),
],
promotes_outputs=[('reg_objective', Mission.Objectives.FUEL)],
)
prob.model.add_objective(Mission.Objectives.FUEL, ref=1)
prob.model.add_subsystem(
'range_obj',
om.ExecComp(
'reg_objective = -actual_range/1000 + ascent_duration/30.',
reg_objective={'val': 0.0, 'units': 'unitless'},
ascent_duration={'units': 's', 'shape': 1},
actual_range={'val': target_range, 'units': 'NM'},
),
promotes_inputs=[
('actual_range', Mission.RANGE),
('ascent_duration', Mission.Takeoff.ASCENT_DURATION),
],
promotes_outputs=[('reg_objective', Mission.Objectives.RANGE)],
)
#####
# prob.setup()
setup_model_options(prob, prob.aviary_inputs, prob.meta_data)
with warnings.catch_warnings():
prob.model.aviary_inputs = prob.aviary_inputs
prob.model.meta_data = prob.meta_data
with warnings.catch_warnings():
warnings.simplefilter('ignore', om.OpenMDAOWarning)
warnings.simplefilter('ignore', om.PromotionWarning)
om.Problem.setup(prob, check=False)
# set initial guesses manually
control_keys = ['mach', 'altitude']
state_keys = ['mass', Dynamic.Mission.DISTANCE]
guesses = {}
guesses['mach_climb'] = ([0.2, 0.72], 'unitless')
guesses['altitude_climb'] = ([0, 32000.0], 'ft')
guesses['time_climb'] = ([0, 3840.0], 's')
guesses['mach_cruise'] = ([0.72, 0.72], 'unitless')
guesses['altitude_cruise'] = ([32000.0, 34000.0], 'ft')
guesses['time_cruise'] = ([3840.0, 3390.0], 's')
guesses['mach_descent'] = ([0.72, 0.36], 'unitless')
guesses['altitude_descent'] = ([34000.0, 500.0], 'ft')
guesses['time_descent'] = ([7230.0, 1740.0], 's')
climb.set_time_val(
initial=guesses['time_climb'][0][0], duration=guesses['time_climb'][0][1], units='s'
)
climb.set_control_val('mach', vals=guesses['mach_climb'][0], time_vals=[-1, 1], units='unitless')
climb.set_control_val('altitude', vals=guesses['altitude_climb'][0], time_vals=[-1, 1], units='ft')
climb.set_state_val('mass', 125000, units='lbm')
cruise.set_time_val(
initial=guesses['time_cruise'][0][0], duration=guesses['time_cruise'][0][1], units='s'
)
cruise.set_control_val('mach', vals=guesses['mach_cruise'][0], time_vals=[-1, 1], units='unitless')
cruise.set_control_val(
'altitude', vals=guesses['altitude_cruise'][0], time_vals=[-1, 1], units='ft'
)
cruise.set_state_val('mass', 125000, units='lbm')
descent.set_time_val(
initial=guesses['time_descent'][0][0], duration=guesses['time_descent'][0][1], units='s'
)
descent.set_control_val(
'mach', vals=guesses['mach_descent'][0], time_vals=[-1, 1], units='unitless'
)
descent.set_control_val(
'altitude', vals=guesses['altitude_descent'][0], time_vals=[-1, 1], units='ft'
)
descent.set_state_val('mass', 125000, units='lbm')
prob.set_val(Aircraft.Design.GROSS_MASS, 175400, units='lbm')
prob.set_val(Mission.GROSS_MASS, 175400, units='lbm')
prob.verbosity = Verbosity.BRIEF
prob.run_aviary_problem()
# Uncomment these lines to get printouts of every variable in the openmdao model
# prob.model.list_vars(units=True, print_arrays=True)
# prob.list_driver_vars(print_arrays=True)
Warning: Invalid options for non-optimal control 'mach' in phase 'climb': lower, upper, ref
Warning: Invalid options for non-optimal control 'altitude' in phase 'climb': lower, upper, ref
Warning: Invalid options for non-optimal control 'mach' in phase 'cruise': lower, upper, ref
Warning: Invalid options for non-optimal control 'altitude' in phase 'cruise': lower, upper, ref
Warning: Invalid options for non-optimal control 'mach' in phase 'descent': lower, upper, ref
Warning: Invalid options for non-optimal control 'altitude' in phase 'descent': lower, upper, ref
The following variables have been overridden by the aircraft definition:
'aircraft:design:landing_to_takeoff_mass_ratio 0.84 unitless
'aircraft:design:touchdown_mass_max 108976.4 lbm
'aircraft:fuselage:passenger_compartment_length 97.5 ft
'aircraft:fuselage:wetted_area 4235.082096 ft**2
'aircraft:horizontal_tail:wetted_area 576.571192 ft**2
'aircraft:landing_gear:main_gear_oleo_length 106.94 inch
'aircraft:landing_gear:nose_gear_oleo_length 74.86 inch
'aircraft:nacelle:total_wetted_area 488.936564 ft**2
'aircraft:nacelle:wetted_area [244.468282] ft**2
'aircraft:propulsion:total_engine_controls_mass 0.0 lbm
'aircraft:propulsion:total_starter_mass 0.0 lbm
'aircraft:vertical_tail:wetted_area 445.645658 ft**2
'aircraft:wing:aspect_ratio 11.5587605382765 unitless
'aircraft:wing:wetted_area 2210.280228 ft**2
Exception of type: TOO_FEW_DOF in file "../../src/Interfaces/IpIpoptApplication.cpp" at line 662:
Exception message: status != TOO_FEW_DEGREES_OF_FREEDOM evaluated false: Too few degrees of freedom (rethrown)!
EXIT: Problem has too few degrees of freedom.
Optimization Problem -- Optimization using pyOpt_sparse
================================================================================
Objective Function: _objfunc
Objectives
Index Name Value
0 mission:objectives:fuel 0.000000E+00
Variables (c - continuous, i - integer, d - discrete)
Index Name Type Lower Bound Value Upper Bound Status
2 traj.climb.t_initial_0 c 0.000000E+00 0.000000E+00 0.000000E+00 lu
3 traj.climb.t_duration_0 c 5.000000E-01 5.000000E-01 1.500000E+00 l
20 traj.climb.states:distance_0 c 0.000000E+00 1.000000E-06 1.000000E+20 l
21 traj.climb.states:distance_1 c 0.000000E+00 1.000000E-06 1.000000E+20 l
22 traj.climb.states:distance_2 c 0.000000E+00 1.000000E-06 1.000000E+20 l
23 traj.climb.states:distance_3 c 0.000000E+00 1.000000E-06 1.000000E+20 l
24 traj.climb.states:distance_4 c 0.000000E+00 1.000000E-06 1.000000E+20 l
25 traj.climb.states:distance_5 c 0.000000E+00 1.000000E-06 1.000000E+20 l
26 traj.climb.states:distance_6 c 0.000000E+00 1.000000E-06 1.000000E+20 l
27 traj.climb.states:distance_7 c 0.000000E+00 1.000000E-06 1.000000E+20 l
28 traj.climb.states:distance_8 c 0.000000E+00 1.000000E-06 1.000000E+20 l
29 traj.climb.states:distance_9 c 0.000000E+00 1.000000E-06 1.000000E+20 l
30 traj.climb.states:distance_10 c 0.000000E+00 1.000000E-06 1.000000E+20 l
31 traj.climb.states:distance_11 c 0.000000E+00 1.000000E-06 1.000000E+20 l
32 traj.climb.states:distance_12 c 0.000000E+00 1.000000E-06 1.000000E+20 l
33 traj.climb.states:distance_13 c 0.000000E+00 1.000000E-06 1.000000E+20 l
34 traj.climb.states:distance_14 c 0.000000E+00 1.000000E-06 1.000000E+20 l
35 traj.cruise.t_duration_0 c 3.169705E-01 3.169705E-01 1.683029E+00 l
51 traj.cruise.states:distance_0 c 0.000000E+00 1.000000E-06 1.000000E+20 l
52 traj.cruise.states:distance_1 c 0.000000E+00 1.000000E-06 1.000000E+20 l
53 traj.cruise.states:distance_2 c 0.000000E+00 1.000000E-06 1.000000E+20 l
54 traj.cruise.states:distance_3 c 0.000000E+00 1.000000E-06 1.000000E+20 l
55 traj.cruise.states:distance_4 c 0.000000E+00 1.000000E-06 1.000000E+20 l
56 traj.cruise.states:distance_5 c 0.000000E+00 1.000000E-06 1.000000E+20 l
57 traj.cruise.states:distance_6 c 0.000000E+00 1.000000E-06 1.000000E+20 l
58 traj.cruise.states:distance_7 c 0.000000E+00 1.000000E-06 1.000000E+20 l
59 traj.cruise.states:distance_8 c 0.000000E+00 1.000000E-06 1.000000E+20 l
60 traj.cruise.states:distance_9 c 0.000000E+00 1.000000E-06 1.000000E+20 l
61 traj.cruise.states:distance_10 c 0.000000E+00 1.000000E-06 1.000000E+20 l
62 traj.cruise.states:distance_11 c 0.000000E+00 1.000000E-06 1.000000E+20 l
63 traj.cruise.states:distance_12 c 0.000000E+00 1.000000E-06 1.000000E+20 l
64 traj.cruise.states:distance_13 c 0.000000E+00 1.000000E-06 1.000000E+20 l
65 traj.cruise.states:distance_14 c 0.000000E+00 1.000000E-06 1.000000E+20 l
66 traj.descent.t_duration_0 c 5.000000E-01 5.000000E-01 1.500000E+00 l
82 traj.descent.states:distance_0 c 0.000000E+00 1.000000E-06 1.000000E+20 l
83 traj.descent.states:distance_1 c 0.000000E+00 1.000000E-06 1.000000E+20 l
84 traj.descent.states:distance_2 c 0.000000E+00 1.000000E-06 1.000000E+20 l
85 traj.descent.states:distance_3 c 0.000000E+00 1.000000E-06 1.000000E+20 l
86 traj.descent.states:distance_4 c 0.000000E+00 1.000000E-06 1.000000E+20 l
87 traj.descent.states:distance_5 c 0.000000E+00 1.000000E-06 1.000000E+20 l
88 traj.descent.states:distance_6 c 0.000000E+00 1.000000E-06 1.000000E+20 l
89 traj.descent.states:distance_7 c 0.000000E+00 1.000000E-06 1.000000E+20 l
90 traj.descent.states:distance_8 c 0.000000E+00 1.000000E-06 1.000000E+20 l
91 traj.descent.states:distance_9 c 0.000000E+00 1.000000E-06 1.000000E+20 l
92 traj.descent.states:distance_10 c 0.000000E+00 1.000000E-06 1.000000E+20 l
93 traj.descent.states:distance_11 c 0.000000E+00 1.000000E-06 1.000000E+20 l
94 traj.descent.states:distance_12 c 0.000000E+00 1.000000E-06 1.000000E+20 l
95 traj.descent.states:distance_13 c 0.000000E+00 1.000000E-06 1.000000E+20 l
96 traj.descent.states:distance_14 c 0.000000E+00 1.000000E-06 1.000000E+20 l
Constraints (i - inequality, e - equality)
Index Name Type Lower Value Upper Status Lagrange Multiplier (N/A)
0 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
1 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
2 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
3 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
4 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
5 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
6 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
7 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
8 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
9 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
10 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
11 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
12 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
13 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
14 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
15 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
16 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
17 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
18 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
19 mission:range e 1.000000E+00 5.246590E-04 1.000000E+00 E 9.00000E+100
21 link_climb_mass.mass e 0.000000E+00 -5.040000E-01 0.000000E+00 E 9.00000E+100
22 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.416625E-02 0.000000E+00 E 9.00000E+100
23 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.290187E-02 0.000000E+00 E 9.00000E+100
24 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.174341E-02 0.000000E+00 E 9.00000E+100
25 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.342733E-02 0.000000E+00 E 9.00000E+100
26 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.172378E-02 0.000000E+00 E 9.00000E+100
27 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.066144E-02 0.000000E+00 E 9.00000E+100
28 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.428696E-02 0.000000E+00 E 9.00000E+100
29 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.374105E-02 0.000000E+00 E 9.00000E+100
30 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.386129E-02 0.000000E+00 E 9.00000E+100
31 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.015110E-02 0.000000E+00 E 9.00000E+100
32 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.047743E-02 0.000000E+00 E 9.00000E+100
33 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 2.099251E-02 0.000000E+00 E 9.00000E+100
34 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.035922E-02 0.000000E+00 E 9.00000E+100
35 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.042770E-02 0.000000E+00 E 9.00000E+100
36 traj.climb.collocation_constraint.defects:mass e 0.000000E+00 1.054689E-02 0.000000E+00 E 9.00000E+100
37 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -1.533828E-02 0.000000E+00 E 9.00000E+100
38 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -1.692552E-02 0.000000E+00 E 9.00000E+100
39 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -1.908952E-02 0.000000E+00 E 9.00000E+100
40 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -4.037206E-02 0.000000E+00 E 9.00000E+100
41 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -4.677011E-02 0.000000E+00 E 9.00000E+100
42 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -5.537116E-02 0.000000E+00 E 9.00000E+100
43 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -6.900124E-02 0.000000E+00 E 9.00000E+100
44 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -7.746934E-02 0.000000E+00 E 9.00000E+100
45 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -8.875504E-02 0.000000E+00 E 9.00000E+100
46 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -7.757492E-02 0.000000E+00 E 9.00000E+100
47 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -8.308906E-02 0.000000E+00 E 9.00000E+100
48 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -9.044768E-02 0.000000E+00 E 9.00000E+100
49 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -4.539785E-02 0.000000E+00 E 9.00000E+100
50 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -4.662540E-02 0.000000E+00 E 9.00000E+100
51 traj.climb.collocation_constraint.defects:distance e 0.000000E+00 -4.828864E-02 0.000000E+00 E 9.00000E+100
52 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.382893E-03 0.000000E+00 E 9.00000E+100
53 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.369773E-03 0.000000E+00 E 9.00000E+100
54 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.351938E-03 0.000000E+00 E 9.00000E+100
55 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.500335E-02 0.000000E+00 E 9.00000E+100
56 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.495089E-02 0.000000E+00 E 9.00000E+100
57 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.488078E-02 0.000000E+00 E 9.00000E+100
58 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.766604E-02 0.000000E+00 E 9.00000E+100
59 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.759756E-02 0.000000E+00 E 9.00000E+100
60 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.750855E-02 0.000000E+00 E 9.00000E+100
61 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.470444E-02 0.000000E+00 E 9.00000E+100
62 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.466272E-02 0.000000E+00 E 9.00000E+100
63 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 1.460751E-02 0.000000E+00 E 9.00000E+100
64 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.144499E-03 0.000000E+00 E 9.00000E+100
65 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.136043E-03 0.000000E+00 E 9.00000E+100
66 traj.cruise.collocation_constraint.defects:mass e 0.000000E+00 7.124548E-03 0.000000E+00 E 9.00000E+100
67 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.309092E-02 0.000000E+00 E 9.00000E+100
68 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.307513E-02 0.000000E+00 E 9.00000E+100
69 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.305334E-02 0.000000E+00 E 9.00000E+100
70 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.791306E-02 0.000000E+00 E 9.00000E+100
71 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.784712E-02 0.000000E+00 E 9.00000E+100
72 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.775605E-02 0.000000E+00 E 9.00000E+100
73 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -1.042990E-01 0.000000E+00 E 9.00000E+100
74 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -1.042056E-01 0.000000E+00 E 9.00000E+100
75 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -1.040766E-01 0.000000E+00 E 9.00000E+100
76 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.750576E-02 0.000000E+00 E 9.00000E+100
77 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.743952E-02 0.000000E+00 E 9.00000E+100
78 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -8.734804E-02 0.000000E+00 E 9.00000E+100
79 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.275559E-02 0.000000E+00 E 9.00000E+100
80 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.273968E-02 0.000000E+00 E 9.00000E+100
81 traj.cruise.collocation_constraint.defects:distance e 0.000000E+00 -4.271771E-02 0.000000E+00 E 9.00000E+100
82 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.495142E-03 0.000000E+00 E 9.00000E+100
83 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.423682E-03 0.000000E+00 E 9.00000E+100
84 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.321006E-03 0.000000E+00 E 9.00000E+100
85 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 2.633531E-03 0.000000E+00 E 9.00000E+100
86 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 2.348910E-03 0.000000E+00 E 9.00000E+100
87 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.914355E-03 0.000000E+00 E 9.00000E+100
88 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 2.094449E-03 0.000000E+00 E 9.00000E+100
89 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.569265E-03 0.000000E+00 E 9.00000E+100
90 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.326178E-03 0.000000E+00 E 9.00000E+100
91 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.525209E-03 0.000000E+00 E 9.00000E+100
92 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 2.117140E-03 0.000000E+00 E 9.00000E+100
93 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 1.799090E-03 0.000000E+00 E 9.00000E+100
94 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 8.235764E-04 0.000000E+00 E 9.00000E+100
95 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 7.641268E-04 0.000000E+00 E 9.00000E+100
96 traj.descent.collocation_constraint.defects:mass e 0.000000E+00 6.844861E-04 0.000000E+00 E 9.00000E+100
97 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -2.191414E-02 0.000000E+00 E 9.00000E+100
98 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -2.159049E-02 0.000000E+00 E 9.00000E+100
99 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -2.113329E-02 0.000000E+00 E 9.00000E+100
100 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -4.285946E-02 0.000000E+00 E 9.00000E+100
101 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -4.142034E-02 0.000000E+00 E 9.00000E+100
102 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -3.934751E-02 0.000000E+00 E 9.00000E+100
103 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -4.597574E-02 0.000000E+00 E 9.00000E+100
104 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -4.372660E-02 0.000000E+00 E 9.00000E+100
105 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -4.048477E-02 0.000000E+00 E 9.00000E+100
106 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -3.316166E-02 0.000000E+00 E 9.00000E+100
107 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -3.140955E-02 0.000000E+00 E 9.00000E+100
108 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -2.891377E-02 0.000000E+00 E 9.00000E+100
109 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -1.376166E-02 0.000000E+00 E 9.00000E+100
110 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -1.331261E-02 0.000000E+00 E 9.00000E+100
111 traj.descent.collocation_constraint.defects:distance e 0.000000E+00 -1.268412E-02 0.000000E+00 E 9.00000E+100
167 traj.descent.throttle[path] i 0.000000E+00 -3.395342E-02 1.000000E+00 L 9.00000E+100
168 traj.descent.throttle[path] i 0.000000E+00 -4.597057E-02 1.000000E+00 L 9.00000E+100
169 traj.descent.throttle[path] i 0.000000E+00 -4.597057E-02 1.000000E+00 L 9.00000E+100
170 traj.descent.throttle[path] i 0.000000E+00 -5.902633E-02 1.000000E+00 L 9.00000E+100
171 traj.descent.throttle[path] i 0.000000E+00 -7.601254E-02 1.000000E+00 L 9.00000E+100
172 traj.descent.throttle[path] i 0.000000E+00 -8.093719E-02 1.000000E+00 L 9.00000E+100
/home/runner/work/Aviary/Aviary/.openmdao-pixi/.pixi/envs/py313/lib/python3.13/site-packages/openmdao/core/total_jac.py:1953: DerivativesWarning:The following design variables have no impact on the constraints or objective at the current design point:
traj.climb.t_initial, inds=[0]
Warning:
Aviary run failed. See the dashboard for more details.
This model demonstrates the flexibility of a custom Python script. For example, we do not load the aircraft model from a .csv file but from a Python file using the get_flops_inputs() method.
This function not only reads Aviary and mission variables but also builds the engine. More information can be found in validation_cases/validation_data/test_data/advanced_single_aisle_data.py.
Note that we can read large single aisle aircraft inputs this way as well:
aviary_inputs = get_flops_inputs('LargeSingleAisle1FLOPS')
aviary_inputs = get_flops_inputs('LargeSingleAisle2FLOPS')
aviary_inputs = get_flops_inputs('LargeSingleAisle2FLOPSdw')
aviary_inputs = get_flops_inputs('LargeSingleAisle2FLOPSalt')
The data files are at:
aviary/models/aircraft/large_single_aisle_1/large_single_aisle_1_FLOPS_data.py
aviary/models/aircraft/large_single_aisle_2/large_single_aisle_2_FLOPS_data.py
aviary/models/aircraft/large_single_aisle_2/large_single_aisle_2_detailwing_FLOPS_data.py
aviary/models/aircraft/large_single_aisle_2/large_single_aisle_2_altwt_FLOPS_data.py
respectively.
Discussing this example in more detail#
We move all the code blocks on taxi to add_pre_mission_systems() function because it is how it is done in aviary/core/aviary_problem.py. Similarly, all the code blocks on landing are moved to add_post_mission_systems() function. Be careful! Generally speaking, not all components can be moved around due to the expected order of execution.
In aviary/validation_cases/benchmark_tests folder, there is another N3CC model test_FLOPS_based_sizing_N3CC.py. If we had started from that model, you would need to have an add_design_variables() function.