I am trying to develop a surrogate model for a thermoacoustic engine. The engine is modelled in a program called DeltaEC. I have developed automation code so that I can change parameters, run the model and read output. Each run takes roughly 1 second with my automation code. I want to automate the investigation to gather the most amount of data, quickly.
I am now in the process designing an investigation/sample plan. I want to investigate the effects of 5 parameters, each within a small range.
My greatest problem so far is that the simulation software (DeltaEC) can not cope with a large change in any of the parameters, which will lead to non convergence. DeltaEC solves a problem with a guess-target shooting method. Such that after each iteration during a simulation run, the guesses are updated and then compared to the target. If the target parameter is changed significantly from one run to the next then guesses from the previous run are not accurate enough for the run to converge. In this case DeltaEC will continue iterating often moving guesses entirely in the wrong direction.
Furthermore, there will be some cases where DeltaEC will never converge as a result of the combination of input parameters.
Therefore, I need to find a 'path' through the input space such that moving along the path only changes parameters very slowly. Is there a sampling approach that allows experiment design with only slowly changing parameters (aside from one factor at a time)?
Alternatively I could develop a route finding algorithm to slowly advance my model between treatments.
I would be very grateful for any comments or suggestions.