NOTE: this website is for DAFoam v1.1 and is no longer updated. For DAFoam v2.0+, visit dafoam.github.io

There are multiple optimization cases in the tutorials folder.

In each optimization case, the run folder contains all the optimization setup. The optOutput folder stores all the optimization results and logs. We recommend you first read the instructions in NACA0012 airfoil incompressible before running other cases. All these tutorials use very coarse meshes, you need to refine the mesh for more realistic runs.

The optimization configurations are defined in runScript.py. There are seven sections:

  • Imports. Import all external modules. No need to change.
  • Input Parameters. Define the flow, adjoint, and optimization parameters. The explanation of these input parameters is in Python layer. Refer to classes-python-pyDAFoam-PYDAFOAM-aCompleteInputParameterSet()
  • DVGeo. Import FFD files in plot3d format and define design variables.
  • DAFoam. Adjoint misc setup. No need to change.
  • DVCon. Define geometric constraints such as volume, thickness, and curvature constraints.
  • optFuncs. Link optimization functions. No need to change.
  • Task. Define optimization tasks (objective function, physical constraints, etc).

Before running the tutorials, you need to load the DAFoam environment.

  • If you use the pre-compiled package, run this command to start a container:

    docker run -it --rm -u dafoamuser -v $HOME:/home/dafoamuser/mount -w /home/dafoamuser/mount dafoam/opt-packages:v1.1 bash

This will mount your local computer’s home directory to the container’s ~/mount directory and login there. Then, copy the tutorials from ~/repos/dafoam to ~/mount:

cp -r /home/dafoamuser/repos/dafoam/tutorials .

Finally, you can go into the run folder of a tutorial and run the optimization. For example, for the aerodynamic optimization of NACA0012 airfoil, run:

cd tutorials/Aerodynamics/NACA0012_Airfoil_Incompressible/run && ./Allrun.sh 1

The last parameter 1 means running the optimization using 1 CPU core. After this, check the log.opt for the optimization progress. All the intermediate shapes and logs (flow, adjoint, mesh quality, design variables, etc.) are stored in the optOutput directory. Once the optimization is finished, you can run exit to quit the container and use Paraview to post-process the optimization results on your local computer. Remember to choose Case Type-Decomposed Case to view the decomposed (parallel) cases in Paraview.

A few notes:

  • Treat the Docker container as disposable, i.e., start one container for one optimization run. If the optimization is running and you want to kill it, just run exit to quit the container.
  • Do not store simulation results in the container because they will be deleted after you exit. Run simulations on the mounted space ~/mount instead.
  • dafoamuser has the sudo privilege and its password is: dafoamuser.
  • Always run ./Allclean.sh before running ./Allrun.sh.
  • If you have compiled DAFoam from the source code following Installation, load the OpenFOAM environment:

    . $HOME/OpenFOAM/OpenFOAM-v1812/etc/bashrc

Then, copy the tutorials to your local folder:

cp -r $HOME/repos/dafoam/tutorials .

Finally, you can go into the run folder of a tutorial and run:

./Allrun.sh 1

A few notes:

  • Before running the optimization, source the OpenFOAM environment: “. $HOME/OpenFOAM/OpenFOAM-v1812/etc/bashrc”
  • Because the OpenFOAM and Python layers interact through IO, job cleaning needs special attention. We assume you compile DAFoam from source and run it on an HPC system. In this case, the running executives will be automatically cleaned when you kill the job. However, if you compile DAFoam and run it on your local computer (not recommended, use the pre-compiled docker version instead!), you need to manually kill the job and clean the running stuff (e.g., the foamRun.sh script and other running executives).
  • Always run Allclean.sh before running Allrun.sh.