.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/formulations/plot_sobieski_bilevel_example.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_formulations_plot_sobieski_bilevel_example.py: BiLevel-based MDO on the Sobieski SSBJ test case ================================================ .. GENERATED FROM PYTHON SOURCE LINES 26-37 .. code-block:: default from __future__ import division, unicode_literals from copy import deepcopy from matplotlib import pyplot as plt from gemseo.api import configure_logger, create_discipline, create_scenario from gemseo.problems.sobieski.core import SobieskiProblem configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 38-45 Instantiate the disciplines ---------------------------- First, we instantiate the four disciplines of the use case: :class:`~gemseo.problems.sobieski.wrappers.SobieskiPropulsion`, :class:`~gemseo.problems.sobieski.wrappers.SobieskiAerodynamics`, :class:`~gemseo.problems.sobieski.wrappers.SobieskiMission` and :class:`~gemseo.problems.sobieski.wrappers.SobieskiStructure`. .. GENERATED FROM PYTHON SOURCE LINES 45-54 .. code-block:: default propu, aero, mission, struct = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiMission", "SobieskiStructure", ] ) .. GENERATED FROM PYTHON SOURCE LINES 55-63 Build, execute and post-process the scenario -------------------------------------------- Then, we build the scenario which links the disciplines with the formulation and the optimization algorithm. Here, we use the :class:`.BiLevel` formulation. We tell the scenario to minimize -y_4 instead of minimizing y_4 (range), which is the default option. We need to define the design space. .. GENERATED FROM PYTHON SOURCE LINES 63-65 .. code-block:: default design_space = SobieskiProblem().read_design_space() .. GENERATED FROM PYTHON SOURCE LINES 66-69 Then, we build a sub-scenario for each strongly coupled disciplines, using the following algorithm, maximum number of iterations and algorithm options: .. GENERATED FROM PYTHON SOURCE LINES 69-77 .. code-block:: default algo_options = { "xtol_rel": 1e-7, "xtol_abs": 1e-7, "ftol_rel": 1e-7, "ftol_abs": 1e-7, "ineq_tolerance": 1e-4, } sub_sc_opts = {"max_iter": 30, "algo": "SLSQP", "algo_options": algo_options} .. GENERATED FROM PYTHON SOURCE LINES 78-81 Build a sub-scenario for Propulsion ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This sub-scenario will minimize SFC. .. GENERATED FROM PYTHON SOURCE LINES 81-91 .. code-block:: default sc_prop = create_scenario( propu, "DisciplinaryOpt", "y_34", design_space=deepcopy(design_space).filter("x_3"), name="PropulsionScenario", ) sc_prop.default_inputs = sub_sc_opts sc_prop.add_constraint("g_3", constraint_type="ineq") .. GENERATED FROM PYTHON SOURCE LINES 92-95 Build a sub-scenario for Aerodynamics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This sub-scenario will minimize L/D. .. GENERATED FROM PYTHON SOURCE LINES 95-106 .. code-block:: default sc_aero = create_scenario( aero, "DisciplinaryOpt", "y_24", deepcopy(design_space).filter("x_2"), name="AerodynamicsScenario", maximize_objective=True, ) sc_aero.default_inputs = sub_sc_opts sc_aero.add_constraint("g_2", constraint_type="ineq") .. GENERATED FROM PYTHON SOURCE LINES 107-111 Build a sub-scenario for Structure ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This sub-scenario will maximize log(aircraft total weight / (aircraft total weight - fuel weight)). .. GENERATED FROM PYTHON SOURCE LINES 111-122 .. code-block:: default sc_str = create_scenario( struct, "DisciplinaryOpt", "y_11", deepcopy(design_space).filter("x_1"), name="StructureScenario", maximize_objective=True, ) sc_str.add_constraint("g_1", constraint_type="ineq") sc_str.default_inputs = sub_sc_opts .. GENERATED FROM PYTHON SOURCE LINES 123-127 Build a scenario for Mission ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This scenario is based on the three previous sub-scenarios and on the Mission and aims to maximize the range (Breguet). .. GENERATED FROM PYTHON SOURCE LINES 127-148 .. code-block:: default sub_disciplines = [sc_prop, sc_aero, sc_str] + [mission] design_space = deepcopy(design_space).filter("x_shared") system_scenario = create_scenario( sub_disciplines, "BiLevel", "y_4", design_space, apply_cstr_tosub_scenarios=False, parallel_scenarios=False, multithread_scenarios=True, tolerance=1e-14, max_mda_iter=30, maximize_objective=True, ) system_scenario.add_constraint(["g_1", "g_2", "g_3"], "ineq") # system_scenario.xdsmize(open_browser=True) system_scenario.execute( {"max_iter": 50, "algo": "NLOPT_COBYLA", "algo_options": algo_options} ) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 14:43:06: INFO - 14:43:06: *** Start MDO Scenario execution *** INFO - 14:43:06: MDOScenario INFO - 14:43:06: Disciplines: PropulsionScenario AerodynamicsScenario StructureScenario SobieskiMission INFO - 14:43:06: MDOFormulation: BiLevel INFO - 14:43:06: Algorithm: NLOPT_COBYLA INFO - 14:43:06: Optimization problem: INFO - 14:43:06: Minimize: -y_4(x_shared) INFO - 14:43:06: With respect to: x_shared INFO - 14:43:06: Subject to constraints: INFO - 14:43:06: g_1_g_2_g_3(x_shared) <= 0.0 INFO - 14:43:06: Design space: INFO - 14:43:06: +----------+-------------+-------+-------------+-------+ INFO - 14:43:06: | name | lower_bound | value | upper_bound | type | INFO - 14:43:06: +----------+-------------+-------+-------------+-------+ INFO - 14:43:06: | x_shared | 0.01 | 0.05 | 0.09 | float | INFO - 14:43:06: | x_shared | 30000 | 45000 | 60000 | float | INFO - 14:43:06: | x_shared | 1.4 | 1.6 | 1.8 | float | INFO - 14:43:06: | x_shared | 2.5 | 5.5 | 8.5 | float | INFO - 14:43:06: | x_shared | 40 | 55 | 70 | float | INFO - 14:43:06: | x_shared | 500 | 1000 | 1500 | float | INFO - 14:43:06: +----------+-------------+-------+-------------+-------+ INFO - 14:43:06: Optimization: 0%| | 0/50 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sobieski_bilevel_example.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_