.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/topology_optimization/plot_topology_optimization_MBB.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_topology_optimization_plot_topology_optimization_MBB.py: Solve a 2D MBB topology optimization problem ============================================ .. GENERATED FROM PYTHON SOURCE LINES 23-33 .. code-block:: Python from __future__ import annotations from gemseo import configure_logger from gemseo import create_scenario from gemseo.problems.topo_opt.topopt_initialize import ( initialize_design_space_and_discipline_to, ) configure_logger() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 34-36 Setup the topology optimization problem --------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 38-39 Define the target volume fraction: .. GENERATED FROM PYTHON SOURCE LINES 39-41 .. code-block:: Python volume_fraction = 0.3 .. GENERATED FROM PYTHON SOURCE LINES 42-43 Define the problem type: .. GENERATED FROM PYTHON SOURCE LINES 43-45 .. code-block:: Python problem_name = "MBB" .. GENERATED FROM PYTHON SOURCE LINES 46-47 Define the number of elements in x- and y- directions: .. GENERATED FROM PYTHON SOURCE LINES 47-50 .. code-block:: Python n_x = 50 n_y = 25 .. GENERATED FROM PYTHON SOURCE LINES 51-52 Define the full material Young's modulus and the Poisson's ratio: .. GENERATED FROM PYTHON SOURCE LINES 52-55 .. code-block:: Python e0 = 1 nu = 0.3 .. GENERATED FROM PYTHON SOURCE LINES 56-57 Define the penalty of the SIMP approach: .. GENERATED FROM PYTHON SOURCE LINES 57-59 .. code-block:: Python penalty = 3 .. GENERATED FROM PYTHON SOURCE LINES 60-61 Define the minimum member size in the solution: .. GENERATED FROM PYTHON SOURCE LINES 61-62 .. code-block:: Python min_member_size = 1.5 .. GENERATED FROM PYTHON SOURCE LINES 63-64 Instantiate the :class:`.DesignSpace` and the disciplines: .. GENERATED FROM PYTHON SOURCE LINES 64-75 .. code-block:: Python design_space, disciplines = initialize_design_space_and_discipline_to( problem=problem_name, n_x=n_x, n_y=n_y, e0=e0, nu=nu, penalty=penalty, min_member_size=min_member_size, vf0=volume_fraction, ) .. GENERATED FROM PYTHON SOURCE LINES 76-79 Solve the topology optimization problem --------------------------------------- Generate an :class:`.MDOScenario` .. GENERATED FROM PYTHON SOURCE LINES 79-86 .. code-block:: Python scenario = create_scenario( disciplines, "DisciplinaryOpt", "compliance", design_space, ) .. GENERATED FROM PYTHON SOURCE LINES 87-88 Add the volume fraction constraint to the scenario: .. GENERATED FROM PYTHON SOURCE LINES 88-92 .. code-block:: Python scenario.add_constraint( "volume fraction", constraint_type="ineq", value=volume_fraction ) .. GENERATED FROM PYTHON SOURCE LINES 93-94 Generate the XDSM .. GENERATED FROM PYTHON SOURCE LINES 94-96 .. code-block:: Python scenario.xdsmize() .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 97-98 Execute the scenario .. GENERATED FROM PYTHON SOURCE LINES 98-100 .. code-block:: Python scenario.execute({"max_iter": 200, "algo": "NLOPT_MMA"}) .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:57:26: INFO - 13:57:26: *** Start MDOScenario execution *** INFO - 13:57:26: MDOScenario INFO - 13:57:26: Disciplines: DensityFilter FininiteElementAnalysis MaterialModelInterpolation VolumeFraction INFO - 13:57:26: MDO formulation: DisciplinaryOpt INFO - 13:57:26: Optimization problem: INFO - 13:57:26: minimize compliance(x) INFO - 13:57:26: with respect to x INFO - 13:57:26: subject to constraints: INFO - 13:57:26: volume fraction(x) <= 0.3 INFO - 13:57:26: Solving optimization problem with algorithm NLOPT_MMA: INFO - 13:57:26: 1%| | 2/200 [00:00<00:13, 14.71 it/sec, obj=1.79e+3] INFO - 13:57:26: 2%|▏ | 3/200 [00:00<00:11, 16.60 it/sec, obj=1.78e+3] INFO - 13:57:26: 2%|▏ | 4/200 [00:00<00:10, 17.82 it/sec, obj=1.77e+3] INFO - 13:57:26: 2%|▎ | 5/200 [00:00<00:10, 18.60 it/sec, obj=1.75e+3] INFO - 13:57:26: 3%|▎ | 6/200 [00:00<00:10, 19.13 it/sec, obj=1.71e+3] INFO - 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13:57:34: 95%|█████████▌| 190/200 [00:07<00:00, 24.29 it/sec, obj=170] INFO - 13:57:34: 96%|█████████▌| 191/200 [00:07<00:00, 24.29 it/sec, obj=170] INFO - 13:57:34: 96%|█████████▌| 192/200 [00:07<00:00, 24.30 it/sec, obj=170] INFO - 13:57:34: 96%|█████████▋| 193/200 [00:07<00:00, 24.30 it/sec, obj=170] INFO - 13:57:34: 97%|█████████▋| 194/200 [00:07<00:00, 24.30 it/sec, obj=170] INFO - 13:57:34: 98%|█████████▊| 195/200 [00:08<00:00, 24.30 it/sec, obj=170] INFO - 13:57:34: 98%|█████████▊| 196/200 [00:08<00:00, 24.30 it/sec, obj=170] INFO - 13:57:34: 98%|█████████▊| 197/200 [00:08<00:00, 24.31 it/sec, obj=170] INFO - 13:57:34: 99%|█████████▉| 198/200 [00:08<00:00, 24.31 it/sec, obj=170] INFO - 13:57:34: 100%|█████████▉| 199/200 [00:08<00:00, 24.32 it/sec, obj=170] INFO - 13:57:34: 100%|██████████| 200/200 [00:08<00:00, 24.41 it/sec, obj=170] INFO - 13:57:34: Optimization result: INFO - 13:57:34: Optimizer info: INFO - 13:57:34: Status: None INFO - 13:57:34: Message: Maximum number of iterations reached. GEMSEO Stopped the driver INFO - 13:57:34: Number of calls to the objective function by the optimizer: 201 INFO - 13:57:34: Solution: INFO - 13:57:34: The solution is feasible. INFO - 13:57:34: Objective: 169.89161468983534 INFO - 13:57:34: Standardized constraints: INFO - 13:57:34: [volume fraction-0.3] = -4.312432086428508e-07 INFO - 13:57:34: *** End MDOScenario execution (time: 0:00:08.212408) *** {'max_iter': 200, 'algo': 'NLOPT_MMA'} .. GENERATED FROM PYTHON SOURCE LINES 101-104 Results ------- Post-process the optimization history: .. GENERATED FROM PYTHON SOURCE LINES 104-108 .. code-block:: Python scenario.post_process( "BasicHistory", variable_names=["compliance"], show=True, save=False ) .. image-sg:: /examples/topology_optimization/images/sphx_glr_plot_topology_optimization_MBB_001.png :alt: History plot :srcset: /examples/topology_optimization/images/sphx_glr_plot_topology_optimization_MBB_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 109-110 Plot the solution .. GENERATED FROM PYTHON SOURCE LINES 110-111 .. code-block:: Python scenario.post_process("TopologyView", n_x=n_x, n_y=n_y, show=True, save=False) .. image-sg:: /examples/topology_optimization/images/sphx_glr_plot_topology_optimization_MBB_002.png :alt: plot topology optimization MBB :srcset: /examples/topology_optimization/images/sphx_glr_plot_topology_optimization_MBB_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.554 seconds) .. _sphx_glr_download_examples_topology_optimization_plot_topology_optimization_MBB.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_topology_optimization_MBB.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_topology_optimization_MBB.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_