# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com # # This work is licensed under a BSD 0-Clause License. # # Permission to use, copy, modify, and/or distribute this software # for any purpose with or without fee is hereby granted. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL # WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL # THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, # OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING # FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, # NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION # WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. # Contributors: # INITIAL AUTHORS - API and implementation and/or documentation # :author: François Gallard # OTHER AUTHORS - MACROSCOPIC CHANGES """ BiLevel-based MDO on the Sobieski SSBJ test case ================================================ """ from __future__ import annotations from copy import deepcopy from logging import WARNING from gemseo import configure_logger from gemseo import create_discipline from gemseo import create_scenario from gemseo import execute_post from gemseo.algos.opt.nlopt.settings.nlopt_cobyla_settings import NLOPT_COBYLA_Settings from gemseo.algos.opt.scipy_local.settings.slsqp import SLSQP_Settings from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace configure_logger() # %% # Instantiate the disciplines # ---------------------------- # First, we instantiate the four disciplines of the use case: # :class:`.SobieskiPropulsion`, # :class:`.SobieskiAerodynamics`, # :class:`.SobieskiMission` # and :class:`.SobieskiStructure`. propu, aero, mission, struct = create_discipline([ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiMission", "SobieskiStructure", ]) # %% # 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. design_space = SobieskiDesignSpace() # %% # Then, we build a sub-scenario for each strongly coupled disciplines, # using the following algorithm, maximum number of iterations and # algorithm settings: slsqp_settings = SLSQP_Settings( max_iter=30, xtol_rel=1e-7, xtol_abs=1e-7, ftol_rel=1e-7, ftol_abs=1e-7, ineq_tolerance=1e-4, ) cobyla_settings = NLOPT_COBYLA_Settings( max_iter=50, xtol_rel=1e-7, xtol_abs=1e-7, ftol_rel=1e-7, ftol_abs=1e-7, ineq_tolerance=1e-4, ) # %% # Build a sub-scenario for Propulsion # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # This sub-scenario will minimize SFC. sc_prop = create_scenario( propu, "y_34", design_space.filter("x_3", copy=True), name="PropulsionScenario", formulation_name="DisciplinaryOpt", ) sc_prop.set_algorithm(slsqp_settings) sc_prop.add_constraint("g_3", constraint_type="ineq") # %% # Build a sub-scenario for Aerodynamics # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # This sub-scenario will minimize L/D. sc_aero = create_scenario( aero, "y_24", design_space.filter("x_2", copy=True), name="AerodynamicsScenario", maximize_objective=True, formulation_name="DisciplinaryOpt", ) sc_aero.set_algorithm(slsqp_settings) sc_aero.add_constraint("g_2", constraint_type="ineq") # %% # Build a sub-scenario for Structure # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # This sub-scenario will maximize # log(aircraft total weight / (aircraft total weight - fuel weight)). sc_str = create_scenario( struct, "y_11", design_space.filter("x_1", copy=True), name="StructureScenario", maximize_objective=True, formulation_name="DisciplinaryOpt", ) sc_str.add_constraint("g_1", constraint_type="ineq") sc_str.set_algorithm(slsqp_settings) # %% # 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). system_scenario = create_scenario( [sc_prop, sc_aero, sc_str, mission], "y_4", design_space.filter("x_shared", copy=True), apply_cstr_tosub_scenarios=False, parallel_scenarios=False, multithread_scenarios=True, main_mda_settings={"tolerance": 1e-14, "max_mda_iter": 30}, maximize_objective=True, sub_scenarios_log_level=WARNING, formulation_name="BiLevel", ) system_scenario.add_constraint(["g_1", "g_2", "g_3"], constraint_type="ineq") # %% # .. tip:: # # When running BiLevel scenarios, it is interesting to access the optimization # history of the sub-scenarios for each system iteration. By default, the setting # ``keep_opt_history`` is set to ``True``. This allows you to store in memory the # databases of the sub-scenarios (see the last section of this example for more # details). # In some cases, storing the databases in memory can take up too much space and cause # performance issues. In these cases, set ``keep_opt_history=False`` and save the # databases to the disk using ``save_opt_history=True``. # %% # Visualize the XDSM # ^^^^^^^^^^^^^^^^^^ # Generate the XDSM on the fly: # # - ``log_workflow_status=True`` will log the status of the workflow in the console, # - ``save_html`` (default ``True``) will generate a self-contained HTML file, # that can be automatically opened using ``show_html=True``. system_scenario.xdsmize(save_html=False, pdf_build=False) # %% # Execute the main scenario # ^^^^^^^^^^^^^^^^^^^^^^^^^ system_scenario.execute(cobyla_settings) # %% # Plot the history of the MDA residuals # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # For the first MDA: system_scenario.formulation.mda1.plot_residual_history(save=False, show=True) # %% # For the second MDA: system_scenario.formulation.mda2.plot_residual_history(save=False, show=True) # %% # Plot the system optimization history view # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ system_scenario.post_process(post_name="OptHistoryView", save=False, show=True) # %% # Plot the structure optimization histories of the 2 first iterations # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ struct_databases = system_scenario.formulation.scenario_adapters[2].databases for database in struct_databases[:2]: opt_problem = deepcopy(sc_str.formulation.optimization_problem) opt_problem.database = database execute_post(opt_problem, post_name="OptHistoryView", save=False, show=True) for disc in [propu, aero, mission, struct]: print(f"{disc.name}: {disc.execution_statistics.n_executions} calls.")