Source code for gemseo.problems.topo_opt.volume_fraction_disc

# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Simone Coniglio
"""A discipline for topology optimization volume fraction."""
from __future__ import annotations

from typing import Sequence

from numpy import array
from numpy import atleast_2d
from numpy import mean
from numpy import ones
from numpy import ones_like
from numpy import size

from gemseo.core.discipline import MDODiscipline

[docs]class VolumeFraction(MDODiscipline): """Compute the volume fraction from the density. Volume fraction is computed as the average of the density value (rho) on each finite element. """ def __init__( self, n_x: int = 100, n_y: int = 100, empty_elements: Sequence[int] | None = None, full_elements: Sequence[int] | None = None, name: str | None = None, ) -> None: """ Args: n_x: The number of elements in the x-direction. n_y: The number of elements in the y-direction. empty_elements: The index of the empty element ids that are not part of the design space. full_elements: The index of the full element ids that are not part of the design space. name: The name of the discipline. If ``None``, use the class name. """ # noqa: D205, D212, D415 super().__init__(name=name) self.n_x = n_x self.n_y = n_y self.input_grammar.update_from_names(["rho"]) self.output_grammar.update_from_names(["volume fraction"]) self.default_inputs = {"rho": ones(n_x * n_y)} def _run(self) -> None: rho = self.get_inputs_by_name("rho") self.local_data["volume fraction"] = array([mean(rho.ravel())]) self._is_linearized = True self._init_jacobian() self.jac["volume fraction"] = {"rho": atleast_2d(ones_like(rho).T / size(rho))}