# 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: Francois Gallard # OTHER AUTHORS - MACROSCOPIC CHANGES """ MDF-based MDO on the Sobieski SSBJ test case ============================================ """ from __future__ import annotations from gemseo import configure_logger from gemseo import create_discipline from gemseo import create_scenario from gemseo import generate_n2_plot from gemseo.problems.sobieski.core.problem import SobieskiProblem configure_logger() # %% # Instantiate the disciplines # --------------------------- # First, we instantiate the four disciplines of the use case: # :class:`.SobieskiPropulsion`, # :class:`.SobieskiAerodynamics`, # :class:`.SobieskiMission` # and :class:`.SobieskiStructure`. disciplines = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiMission", "SobieskiStructure", ] ) # %% # We can quickly access the most relevant information of any discipline (name, inputs, # and outputs) with Python's ``print()`` function. Moreover, we can get the default # input values of a discipline with the attribute :attr:`.MDODiscipline.default_inputs` for discipline in disciplines: print(discipline) print(f"Default inputs: {discipline.default_inputs}") # %% # You may also be interested in plotting the couplings of your disciplines. # A quick way of getting this information is the high-level function # :func:`.generate_n2_plot`. A much more detailed explanation of coupling # visualization is available :ref:`here `. generate_n2_plot(disciplines, save=False, show=True) # %% # 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:`.MDF` formulation. We tell the scenario to minimize -y_4 instead of # minimizing y_4 (range), which is the default option. # # Instantiate the scenario # ^^^^^^^^^^^^^^^^^^^^^^^^ # During the instantiation of the scenario, we provide some options for the # MDF formulations: formulation_options = { "tolerance": 1e-14, "max_mda_iter": 50, "warm_start": True, "use_lu_fact": False, "linear_solver_tolerance": 1e-14, } # %% # # - ``'warm_start``: warm starts MDA, # - ``'warm_start``: optimize the adjoints resolution by storing # the Jacobian matrix LU factorization for the multiple RHS # (objective + constraints). This saves CPU time if you can pay for # the memory and have the full Jacobians available, not just matrix vector # products. # - ``'linear_solver_tolerance'``: set the linear solver tolerance, # idem we need full convergence # design_space = SobieskiProblem().design_space design_space # %% scenario = create_scenario( disciplines, "MDF", objective_name="y_4", design_space=design_space, maximize_objective=True, **formulation_options, ) # %% # Set the design constraints # ^^^^^^^^^^^^^^^^^^^^^^^^^^ for c_name in ["g_1", "g_2", "g_3"]: scenario.add_constraint(c_name, "ineq") # %% # XDSMIZE the scenario # ^^^^^^^^^^^^^^^^^^^^ # Generate the XDSM file 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``. scenario.xdsmize(save_html=False) # %% # Define the algorithm inputs # ^^^^^^^^^^^^^^^^^^^^^^^^^^^ # We set the maximum number of iterations, the optimizer # and the optimizer options. Algorithm specific options are passed there. # Use the high-level function :func:`.get_algorithm_options_schema` # for more information or read the documentation. # # Here ftol_rel option is a stop criteria based on the relative difference # in the objective between two iterates ineq_tolerance the tolerance # determination of the optimum; this is specific to the |g| wrapping and not # in the solver. algo_options = { "ftol_rel": 1e-10, "ineq_tolerance": 2e-3, "normalize_design_space": True, } scn_inputs = {"max_iter": 15, "algo": "SLSQP", "algo_options": algo_options} # %% # .. seealso:: # # We can also generate a backup file for the optimization, # as well as plots on the fly of the optimization history if option # ``generate_opt_plot`` is ``True``. # This slows down a lot the process, here since SSBJ is very light # # .. code:: # # scenario.set_optimization_history_backup(file_path="mdf_backup.h5", # each_new_iter=True, # each_store=False, erase=True, # pre_load=False, # generate_opt_plot=True) # %% # Execute the scenario # ^^^^^^^^^^^^^^^^^^^^ scenario.execute(scn_inputs) # %% # Save the optimization history # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # We can save the whole optimization problem and its history for further post # processing: scenario.save_optimization_history("mdf_history.h5", file_format="hdf5") # %% # We can also save only calls to functions and design variables history: scenario.save_optimization_history("mdf_history.xml", file_format="ggobi") # %% # Print optimization metrics # ^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.print_execution_metrics() # %% # Post-process the results # ------------------------ # # Plot the optimization history view # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("OptHistoryView", save=False, show=True) # %% # Plot the basic history view # ^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process( "BasicHistory", variable_names=["x_shared"], save=False, show=True ) # %% # Plot the constraints and objective history # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("ObjConstrHist", save=False, show=True) # %% # Plot the constraints history # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process( "ConstraintsHistory", constraint_names=["g_1", "g_2", "g_3"], save=False, show=True, ) # %% # Plot the constraints history using a radar chart # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process( "RadarChart", constraint_names=["g_1", "g_2", "g_3"], save=False, show=True, ) # %% # Plot the quadratic approximation of the objective # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("QuadApprox", function="-y_4", save=False, show=True) # %% # Plot the functions using a SOM # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("SOM", save=False, show=True) # %% # Plot the scatter matrix of variables of interest # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process( "ScatterPlotMatrix", variable_names=["-y_4", "g_1"], save=False, show=True, fig_size=(14, 14), ) # %% # Plot the variables using the parallel coordinates # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("ParallelCoordinates", save=False, show=True) # %% # Plot the robustness of the solution # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("Robustness", save=True, show=True) # %% # Plot the influence of the design variables # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ scenario.post_process("VariableInfluence", fig_size=(14, 14), save=False, show=True)