ShapeOpt is based on FEniCS, dolfin-adjoint, IPOPT, cyipopt, pygmsh.
Code repository for the manuscript
J. Haubner, M. Ulbrich: Numerical Methods for Shape Optimal Design of Fluid-Structure Interaction Problems.
based on the implementation for the PhD thesis
J. Haubner: Shape Optimization for Fluid-Structure Interaction, Doctoral Dissertation, Technische Universität München, 2020
The Dockerfile can be used by running:
docker build -t shapeopt .
docker run -it shapeopt
or
docker pull ghcr.io/johanneshaubner/shapeopt:latest
docker run -ti -v ${PWD}:/root/shared -w /root/shared --entrypoint=/bin/bash --rm ghcr.io/johanneshaubner/shapeopt:latest
For runs in parallel, IPOPT needs to be installed with HSL. To do so, the Coin-HSL sources archive needs to be added such that it becomes shapeopt/hsl/coinhsl. Afterwards we build the docker image via Dockerfile_hsl.
If not ran from Docker image: Requires a recent master version of dolfin with MeshView support. Requires the changes propsed in https://bitbucket.org/fenics-project/dolfin/issues/1123/assemble-on-mixed-meshview-forms-returns.
To create the mesh use
python3 example/FSI/create_mesh_FSI.py
To obtain the results presented in the paper we used an IPOPT installation with HSL and ran
mpiexec -n 4 python3 example/FSI/main.py >> example/FSI/mesh/Output/terminal.txt
(FFC Compiler seems to get stuck in the beginning, cancelling when this happens and restarting the simulation seems to fix this.)
To obtain the tables and figures we ran (with setting the corresponding options to True in the script)
mpiexec -n 4 python3 example/FSI/visualize.py
To run tests, run the following command
pytestWe apply a method of mappings approach and solve everything on a reference domain. In order not to have too much redundancy in the representation of the transformations, we choose a scalar valued function on the design boundary as control for the optimization problem and define the deformation field using an extension operator from the design boundary to the whole domain. In the example code, we define the Control_to_Trafo-mapping as a composition of boundary operators and extension operators.
After having defined the transformation, the reduced objective and its gradient (computed using dolfin-adjoint) can be evaluated. An example for a simple Stokes flow and Fluid-Structure Interaction is given.
Collection of constraint for the optimization problem: volume, barycenter, determinant.
Postprocessing step for improving the mesh quality after each optimization problem solve.
Solving the constraint optimization problem is performed using Ipopt.
Here, different tools are collect, e.g. initialization of function spaces, save and load objects, etc.
We would like to acknowledge Jørgen S. Dokken, Henrik N. Finsberg and Simon W. Funke for the help, support and discussions on reproducibility.