DAFoam contains a suite of discrete adjoint solvers for OpenFOAM. These adjoint solvers run as standalone executives to compute derivatives. DAFoam also has a Python interface that allows the adjoint solvers to interact with external modules for high-fidelity design optimization using the MACH framework. DAFoam has the following features:
- It implements an efficient discrete adjoint approach with competitive speed, scalability, accuracy, and compatibility.
- It allows rapid discrete adjoint development for any steady-state OpenFOAM solvers with modifying only a few hundred lines of source codes.
- It supports design optimizations for a wide range of disciplines such as aerodynamics, heat transfer, structures, hydrodynamics, and radiation.
Refer to https://dafoam.rtfd.io for DAFoam installation and tutorials.
To build the documentation locally, go to the doc folder and run:
./Allwmake
The built documentation is located at doc/DAFoamDoc.html
Ping He, Charles A. Mader, Joaquim R.R.A. Martins, Kevin J. Maki. An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM. Computer & Fluids, 168:285-303, 2018. https://doi.org/10.1016/j.compfluid.2018.04.012
@article{DAFoamPaper,
Author = {Ping He and Charles A. Mader and Joaquim R. R. A. Martins and Kevin J. Maki},
Doi = {10.1016/j.compfluid.2018.04.012},
Journal = {Computers \& Fluids},
Pages = {285--303},
Title = {An aerodynamic design optimization framework using a discrete adjoint approach with {OpenFOAM}},
Volume = {168},
Year = {2018}}
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