SU2 for multiple sampling optimization
 

Hi,
Recently I started exploring possibility of using SU2 for robust optimization where I need to run the direct and adjoint solver of SU2 at multiple sampling points at each iteration of optimization process and need to feed a modified gradient of the objective to the optimizer.

I guess I need to write few scripts to do it. If so, it would be great if someone can provide me some ideas to implement it as:

1. How can I call SU2_CFD multiple times for each iteration of the optimization process.
2. How to modify objective gradient wrt design var. in the iteration process.

Thanks
Raj

Hi Raj,

What you are proposing is a deep modification of the optimization algorithm. There are different ways to proceed and, obviously, the first step is to be familiar with the python part of the code. I think it would be interesting if you take a look at SU2_PY/SU2/opt/scipy_tools.py and SU2_PY/compute_polar.py

The first file is the highest level to define an optimization strategy... in this case SLSQP using Scipy. The second file is just an example of how to run a polar.

Best,
Francisco Palacios

Dear Francisco,

Thank you very much for you kind suggestions. I will try all possibilities.

Regards,
Dinesh

Multi-point optimization
 

Hi Raj,

I find your question very intersting and important. Especially if one looks at multi-points
optimization and and an objective function/restraints that are evaluated at different flow conditions. Would you like to share your experience in this field ?

Best regards,

Eran