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With the 2022 rule change on the horizon bargeboards will no longer be a feature of F1 cars, but what are they, how do they work, and will F1’s designers be able to replicate their function in 2022?
The show cars are based on FOMs working models so they're effective - teams will produce more downforce though.
You can have an 'eagle' beak type nose. What they can't do is put any sort of winglets on the nose because they prescribe no concave surfaces.BlueCheetah66 wrote: ↑01 Jan 2022, 19:43For the noses next year, are teams still allowed to have a sharp change in the width of the nose, similar to what Mercedes have had for a while or has that been closed off in these regulations?
And all of them are out. Curious.jjn9128 wrote: ↑01 Jan 2022, 21:59Tombazis is FIA. Symonds group is FOM, Jason Somerville led the aero design group under Symonds.aleks_ader wrote: ↑01 Jan 2022, 20:51Tombazis aero group with Symonds.Hoffman900 wrote: ↑01 Jan 2022, 20:49Who did the design of the FOM cars?
There seems to be at least some thought put into them.
The FOM technical Hierarchy is:
1) Brawn
2) Symonds
3) Somerville (aero), Wilson (vehicle performance)
4) 3/4 aeros and surfacers + a couple of suspension and gearbox engineers
FOM created the car(s) and hand that off to Tombazis and the FIA who then drafted the rules with the teams. There was some crossover with the FIA and FOM but that's how it worked.
They are doing it to an extent (and that is controlled by the wind tunnel/CFD usage regulations. It is only a matter of time before limits are put on general data usage in other areas (simulation, FEA - also very effective used iteratively - and during race weekends)
They already had the right idea back in 2018's outwashing front wing. The move to curved diffuser from the boxy designs of 2017-2018 vintage shows that geometry works. IIRC Haas was the first team to try the curved diffuser.
CFD is controlled by TFLOPS. That can be sidestepped by analog co-processors that are much, much faster at the Navier Stokes computations.
I think what you're thinking of are called evolutionary algorithms. But they certainly don't explore "everything", because that's impossible, it would be practically infinite.
By definition. Having a minimum and a maximum sets boundaries that are finite.
Changing the ruler doesn't mean the object changes with your description. It still is limited by real constraints. Digital to Analog nit-picking aside.BorisTheBlade wrote: ↑03 Jan 2022, 20:31By which definition? You do realise that even between the borders of 0 and 1 there is an infinite amount of values in-between?
Do you have any data to suggest that the venturi's can come anywhere near choke flow?godlameroso wrote: ↑04 Jan 2022, 01:59More importantly, the tunnels aren't straight, there will be compression waves when airflow encounters a concave surface, and there will be expansion waves when air travels on a convex surface. Compression waves slow down air and increase drag, while expansion waves speed up air and slightly decrease drag locally. The geometry to optimize these phenomena will require many studies and iterations. I don't think machine learning will be that valuable until the meat sacks get some learnin' done.
Yes there will be sonic flow and choking effects, exploiting that will be key to getting the most performance from the floor.
A venturi tunnel speeds up air, if the car is going 33 meters per second, the local speed at the venturi tunnel can be as high as 100 m/s 50% increase in speed, and you're somewhere in the sonic regime ballpark, ie choked flow. The only way to increase mass flow at this point is to increase the pressure of the inlet, or air temperature, as ambient temperature affects the Mach number of air. If you achieve choked flow, the pressure downstream will drop like a neutron hammer. The airflow is literally choked at the inlet, like sand grains passing through an hourglass. Such a sharp pressure reduction will summon any surrounding air to fill that "void".