I'm currently working on a dynamic simulation of an already existing product. The simulation is supposed to help in future development of this product by enabling the engineers to get a better understanding of the influence different parameters have.
However the mathematical and physical models manifesting the simulation are only simplifications of the real system because of its sheer complexity. Therefore no quantitative results are created. Nevertheless this simulation is useful as it is faster and cheaper to change a parameter in a simulation rather than building a prototype and physically test it.
Now being at the point that the simulation runs as expected with acceptable performance I'm starting to implement the evaluation of the data.
Which gives me the problem: What am I even supposed to do with all of this data?
The Question now is: What are useful methods to analyze moderate sets of data of real world (mechanical) systems.
My discipline is hardware development and not data analysis so this topic is mostly new to me. (Apart from outdated mathematical lectures about statistics). Additionally I'm really junior in this industry so experience is rather limited.
I think this question (or more precisely the answers) could be a good resource for engineers in the same positions as me independent from the application.
A bit of background for my application:
- 40 static inputs/starting parameters
- 30 outputs presenting physical properties "plotted" over time (e.g. speed, height, current, etc.)
- Monte Carlo method is already implemented
This question might me considered as to broad or not answerable, because of this I'm open for feedback or accept the fact that it might be closed.