why is understanding turbulence such a great mystery of science?

>can't even triple integral

File: istock-exploding-head.jpg (51KB, 600x450px) Image search: [Google]

51KB, 600x450px

>>8476949
what?

>>8476945
Shit is hard.
People in fluids are not the brightest.

because you can't easily model the fluid as the sum of its parts, ie. write kinetic equations for each particle that makes up the fluid and track each particle's motion with time (this is basically molecular dynamics, which is used to simulate radiation damage in solids for example, but a simulation that spans picoseconds could take a long ass time to run and only show you a single damage site (volumes on the order of cubic microns and less)

>>8476980
you forgot to close one of your parentheses

>>8476945
We are basically a collection of turbulent fluids.
Think about how fucking hard it is to understand yourself, then turn that into a set of equations of three or more degrees, (derivatives in multiple dimensions including time). Throw in some chaos, if you want, and try to model that shit.

You ain't no model.
Showing no integral understanding.
Very chaotic.
Too much variables.

If you were able to understand it, you'd be a model, and earn enough money to not go on 4chan asking questions like this.

Because most scientists are cis, white and male and therefore can't grasp the concept of fluidity

Do you understand statistical mechanics at all?
Modelling turbulence has the same issues as modeling exactly what the weather will do.
>>8477774
Underrated

File: Ragemcgee.jpg (119KB, 1023x328px) Image search: [Google]

119KB, 1023x328px

>>8477774

Great point. We have a lot of work to do as a community overcoming these issues.

>>8477845

This is true though.

>>8477218

You forgot to use "." at the end of your sentence.

>>8476945
There is no mystery to turbulence, we just cant make long term predictions for any realistic problem because the cost of running the full scale model is incredibly expensive.

Using surrogate models and reduced order approximations also struggles because since the system is highly nonlinear and sensitive to it's initial conditions, any epistemic uncertainties (which are implicit with metamodels) will quickly cause the solution to diverge.

Stochastically, we understand turbulence very well, hence the popularity of RANS models in making steady state predictions (sufficient in most cases).

It's a computational problem.