MDO formulations for a toy example in aerostructure

from __future__ import absolute_import, division, print_function, unicode_literals

from copy import deepcopy

from future import standard_library

from gemseo.api import (
    configure_logger,
    create_discipline,
    create_scenario,
    generate_n2_plot,
)
from gemseo.problems.aerostructure.aerostructure_design_space import (
    AerostructureDesignSpace,
)

configure_logger()

standard_library.install_aliases()

algo_options = {
    "xtol_rel": 1e-8,
    "xtol_abs": 1e-8,
    "ftol_rel": 1e-8,
    "ftol_abs": 1e-8,
    "ineq_tolerance": 1e-5,
    "eq_tolerance": 1e-3,
}

Create discipline

First, we create disciplines (aero, structure, mission) with dummy formulas using the AnalyticDiscipline class.

aero_formulas = {
    "drag": "0.1*((sweep/360)**2 + 200 + "
    + "thick_airfoils**2-thick_airfoils -4*displ)",
    "forces": "10*sweep + 0.2*thick_airfoils-0.2*displ",
    "lift": "(sweep + 0.2*thick_airfoils-2.*displ)/3000.",
}
aerodynamics = create_discipline(
    "AnalyticDiscipline", name="Aerodynamics", expressions_dict=aero_formulas
)
struc_formulas = {
    "mass": "4000*(sweep/360)**3 + 200000 + " + "100*thick_panels +200.0*forces",
    "reserve_fact": "-3*sweep " + "-6*thick_panels+0.1*forces+55",
    "displ": "2*sweep + 3*thick_panels-2.*forces",
}
structure = create_discipline(
    "AnalyticDiscipline", name="Structure", expressions_dict=struc_formulas
)
mission_formulas = {"range": "8e11*lift/(mass*drag)"}
mission = create_discipline(
    "AnalyticDiscipline", name="Mission", expressions_dict=mission_formulas
)

disciplines = [aerodynamics, structure, mission]

We can see that structure and aerodynamics are strongly coupled:

generate_n2_plot(disciplines, save=False, show=True)

Create a MDO scenario with MDF formulation

Then, we create a MDO scenario based on the MDF formulation

design_space = AerostructureDesignSpace()
scenario = create_scenario(
    disciplines=disciplines,
    formulation="MDF",
    objective_name="range",
    design_space=design_space,
    maximize_objective=True,
)
scenario.add_constraint("reserve_fact", "ineq", value=0.5)
scenario.add_constraint("lift", "eq", value=0.5)
scenario.execute({"algo": "NLOPT_SLSQP", "max_iter": 10, "algo_options": algo_options})
scenario.post_process("OptHistoryView", save=False, show=True)

• 
• 
• 
• 
• 
• 

Out:

/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:312: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax1.set_yticklabels(y_labels)
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:716: MatplotlibDeprecationWarning: default base will change from np.e to 10 in 3.4.  To suppress this warning specify the base keyword argument.
  norm=SymLogNorm(linthresh=linthresh, vmin=-vmax, vmax=vmax),
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:626: MatplotlibDeprecationWarning: default base will change from np.e to 10 in 3.4.  To suppress this warning specify the base keyword argument.
  norm=SymLogNorm(linthresh=1.0, vmin=-vmax, vmax=vmax),
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:619: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.
  im1 = ax1.imshow(

<gemseo.post.opt_history_view.OptHistoryView object at 0x7fc298922550>

Create a MDO scenario with bilevel formulation

Then, we create a MDO scenario based on the bilevel formulation

sub_scenario_options = {
    "max_iter": 5,
    "algo": "NLOPT_SLSQP",
    "algo_options": algo_options,
}
design_space_ref = AerostructureDesignSpace()

Create the aeronautics sub-scenario

For this purpose, we create a first sub-scenario to maximize the range with respect to the thick airfoils, based on the aerodynamics discipline.

design_space_aero = deepcopy(design_space_ref).filter(["thick_airfoils"])
aero_scenario = create_scenario(
    disciplines=[aerodynamics, mission],
    formulation="DisciplinaryOpt",
    objective_name="range",
    design_space=design_space_aero,
    maximize_objective=True,
)
aero_scenario.default_inputs = sub_scenario_options

Create the structure sub-scenario

We create a second sub-scenario to maximize the range with respect to the thick panels, based on the structure discipline.

design_space_struct = deepcopy(design_space_ref).filter(["thick_panels"])
struct_scenario = create_scenario(
    disciplines=[structure, mission],
    formulation="DisciplinaryOpt",
    objective_name="range",
    design_space=design_space_struct,
    maximize_objective=True,
)
struct_scenario.default_inputs = sub_scenario_options

Create the system scenario

Lastly, we build a system scenario to maximize the range with respect to the sweep, which is a shared variable, based on the previous sub-scenarios.

design_space_system = deepcopy(design_space_ref).filter(["sweep"])
system_scenario = create_scenario(
    disciplines=[aero_scenario, struct_scenario, mission],
    formulation="BiLevel",
    objective_name="range",
    design_space=design_space_system,
    maximize_objective=True,
    mda_name="MDAJacobi",
    tolerance=1e-8,
)
system_scenario.add_constraint("reserve_fact", "ineq", value=0.5)
system_scenario.add_constraint("lift", "eq", value=0.5)
system_scenario.execute(
    {"algo": "NLOPT_COBYLA", "max_iter": 7, "algo_options": algo_options}
)
system_scenario.post_process("OptHistoryView", save=False, show=True)

• 
• 
• 
• 
• 

Out:

/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:312: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax1.set_yticklabels(y_labels)
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:626: MatplotlibDeprecationWarning: default base will change from np.e to 10 in 3.4.  To suppress this warning specify the base keyword argument.
  norm=SymLogNorm(linthresh=1.0, vmin=-vmax, vmax=vmax),
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:619: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.
  im1 = ax1.imshow(

<gemseo.post.opt_history_view.OptHistoryView object at 0x7fc299651fa0>