The difference is that there is a simple physics model that's driving the dimensions. So the arms are getting resized according to beam bending, the battery is a certain size for the energy requirement, motors are sized by kw/kg and kw/m^3 and so on.
There's definitely ways to make this sort of model in something like Solidworks through the use of a variables interface that interacts with the drawing dimensions. Though it can't do any sort of fast optimization with the physics relations. What's happening here is a guaranteed global optimal design is being returned for a given set of inputs and objectives in a few seconds.
While this is well made, in production you would probably put the physics model in Excel with a bunch of VB scripting, and interface it to parameters and equations in solidworks. This isn't something I've done, but I absolutely wouldn't want to trade in a cad engine that can do everything badly just for some design tool that does a few things well.
if you want to get somewhere with this, you should consider the engineering work flow. usually there is a conceptual/design phase that consists of a lot of photographed whiteboards, physical mockups, Matlab, even Mathematica Notebooks if you're like that. Or actually, some cfd and multiphysics fem modelling if you are serious about your flying objects.
You seem to fit into this chaos sonewhere, but I wouldn't quite know at which point I should use this. After all, cad models are always parametric until you start adding all the details, and actual physics modelling even on a xflr5 level goes well beyond this.
This sounds interesting but I can't react the site. Could you please explain how you get a guaranteed optimum? What if you have conflicting objectives?
I think by react you meant react to, which was fair enough, it was dodgy as. Now running well.
The property of getting a guaranteed optimum is something that arises from setting the design problem up as a geometric program. You won't always get a solution (for example, a certain size quad has a minimum prop diameter). Though if you do, it is guaranteed to be optimum because the problem is convex as outlined here: https://gpkit.readthedocs.io/en/latest/gp101.html
Also, conflicting objectives are naturally traded off until you find a Pareto optimal solution. Using geometric programs lets you balance conflicts in a design quickly. This is why they are super useful for aerospace applications, where you are always looking at a tradeoff between weight and at least one other variable.
It forces you to assign a weight to each objective, converting from a multi-objective to a single-objective optimization problem (the weighted sum method).
By varying the weights and re-running, you could identify a set a solutions on [convex regions of] the Pareto front.
There's definitely ways to make this sort of model in something like Solidworks through the use of a variables interface that interacts with the drawing dimensions. Though it can't do any sort of fast optimization with the physics relations. What's happening here is a guaranteed global optimal design is being returned for a given set of inputs and objectives in a few seconds.