Features to add to a python-accessible, optimization oriented 3D panel method code
 

Hey there!

I've been developing for the past four weeks an open-source optimization oriented 3D panel method code of my own and would like feedback on what features could be added to fit your academic needs.

I'm doing it based on the premise that several researchers tend to resort to either vortex lattice or LLT codes so as to ease interaction with Matlab or Python code and reduce the minimum number of required input parameters, and it would be nice if a higher fidelity method for configuration study could be used with similar performance and ease of access requirements.

Since f2py and scipy's LAPACK routines can optimize panel methods so as to reach similar performances (capable of operating in highly iterative optimization programs), Python can be directly used for input handling and post-processing of a modern implementation of panel method, while FORTRAN wrappings can accelerate AIC generation and matrix inversion. Also, an interpreted language based program can also ease program tweaking and method customization for the user's research purposes.

I intend to have an alpha version of both the geometry creator and the Euler equation solver within two weeks. Any feedback, suggestion or support that can be given until then is highly appreciated.

Here's the repository link for the code I've produced so far:
https://github.com/pedrosecchi67/LovelacePM
Check the long term system requirements I've written in the repos.'s README file, if you'd like =D