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Solve a 2D L-shape topology optimization problem¶
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()
<RootLogger root (INFO)>
Setup the topology optimization problem¶
Define the target volume fractio:
volume_fraction = 0.3
Define the problem type:
problem_name = "L-Shape"
Define the number of elements in the x- and y- directions:
n_x = 25
n_y = 25
Define the full material Young’s modulus and Poisson’s ratio:
e0 = 1
nu = 0.3
Define the penalty of the SIMP approach:
penalty = 3
Define the minimum member size in the solution:
min_member_size = 1.5
Instantiate the DesignSpace and the disciplines:
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,
)
Solve the topology optimization problem¶
Generate an MDOScenario:
scenario = create_scenario(
disciplines,
formulation="DisciplinaryOpt",
objective_name="compliance",
design_space=design_space,
)
Add the volume fraction constraint to the scenario:
scenario.add_constraint("volume fraction", "ineq", value=volume_fraction)
Generate the XDSM
scenario.xdsmize()
Execute the scenario
scenario.execute({"max_iter": 200, "algo": "NLOPT_MMA"})
INFO - 13:54:17:
INFO - 13:54:17: *** Start MDOScenario execution ***
INFO - 13:54:17: MDOScenario
INFO - 13:54:17: Disciplines: DensityFilter FininiteElementAnalysis MaterialModelInterpolation VolumeFraction
INFO - 13:54:17: MDO formulation: DisciplinaryOpt
INFO - 13:54:17: Optimization problem:
INFO - 13:54:17: minimize compliance(x)
INFO - 13:54:17: with respect to x
INFO - 13:54:17: subject to constraints:
INFO - 13:54:17: volume fraction(x) <= 0.3
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INFO - 13:54:26: ... 96%|█████████▌| 191/200 [00:09<00:00, 20.27 it/sec, obj=152]
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INFO - 13:54:26: ... 97%|█████████▋| 194/200 [00:09<00:00, 20.28 it/sec, obj=152]
INFO - 13:54:26: ... 98%|█████████▊| 195/200 [00:09<00:00, 20.29 it/sec, obj=152]
INFO - 13:54:26: ... 98%|█████████▊| 196/200 [00:09<00:00, 20.29 it/sec, obj=152]
INFO - 13:54:27: ... 98%|█████████▊| 197/200 [00:09<00:00, 20.30 it/sec, obj=152]
INFO - 13:54:27: ... 99%|█████████▉| 198/200 [00:09<00:00, 20.30 it/sec, obj=152]
INFO - 13:54:27: ... 100%|█████████▉| 199/200 [00:09<00:00, 20.31 it/sec, obj=152]
INFO - 13:54:27: ... 100%|██████████| 200/200 [00:09<00:00, 20.38 it/sec, obj=152]
INFO - 13:54:27: Optimization result:
INFO - 13:54:27: Optimizer info:
INFO - 13:54:27: Status: None
INFO - 13:54:27: Message: Maximum number of iterations reached. GEMSEO Stopped the driver
INFO - 13:54:27: Number of calls to the objective function by the optimizer: 201
INFO - 13:54:27: Solution:
INFO - 13:54:27: The solution is feasible.
INFO - 13:54:27: Objective: 151.6287318635838
INFO - 13:54:27: Standardized constraints:
INFO - 13:54:27: [volume fraction-0.3] = 1.0976701955156543e-06
INFO - 13:54:27: *** End MDOScenario execution (time: 0:00:09.832214) ***
{'max_iter': 200, 'algo': 'NLOPT_MMA'}
Results¶
Post-process the optimization history:
scenario.post_process(
"BasicHistory",
variable_names=["compliance"],
file_name=f"{problem_name}_history.png",
)
<gemseo.post.basic_history.BasicHistory object at 0x7f0098a88910>
Plot the solution
scenario.post_process(
"TopologyView",
n_x=n_x,
n_y=n_y,
file_name=f"{problem_name}_solution.png",
)
<gemseo.post.topology_view.TopologyView object at 0x7f006e87f910>
Total running time of the script: (0 minutes 10.264 seconds)