Cite Us

See the following papers for technical background of the above modules. If you use these modules for publications, please cite the corresponding papers.

  • pyOptSparse [1]

  • pyGeo [2]

  • ADflow [3, 4, 5]

  • DAFoam [6]

1

Ruben E. Perez, Peter W. Jansen, and Joaquim R. R. A. Martins. pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization. Structural and Multidisciplinary Optimization, 45(1):101–118, January 2012. doi:10.1007/s00158-011-0666-3.

2

Gaetan K.W. Kenway, Graeme. J. Kennedy, and Joaquim R. R. A. Martins. A CAD-free approach to high-fidelity aerostructural optimization. In Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, number AIAA 2010-9231. Fort Worth, TX, September 2010. doi:10.2514/6.2010-9231.

3

Charles A. Mader, Gaetan K. W. Kenway, Anil Yildirim, and Joaquim R. R. A. Martins. ADflow—an open-source computational fluid dynamics solver for aerodynamic and multidisciplinary optimization. Journal of Aerospace Information Systems, 2020. doi:10.2514/1.I010796.

4

Gaetan K. W. Kenway, Charles A. Mader, Ping He, and Joaquim R. R. A. Martins. Effective adjoint approaches for computational fluid dynamics. Progress in Aerospace Sciences, 110:100542, October 2019. doi:10.1016/j.paerosci.2019.05.002.

5

Anil Yildirim, Gaetan K. W. Kenway, Charles A. Mader, and Joaquim R. R. A. Martins. A Jacobian-free approximate Newton–Krylov startup strategy for RANS simulations. Journal of Computational Physics, 397:108741, November 2019. doi:10.1016/j.jcp.2019.06.018.

6

Ping He, Charles A. Mader, Joaquim R. R. A. Martins, and Kevin J. Maki. An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM. Computers & Fluids, 168:285–303, May 2018. doi:10.1016/j.compfluid.2018.04.012.