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Old 12-24-2020, 02:08 AM   #1 (permalink)
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Thesis on wake behaviour on square back vehicles

Adrian Gaylard (head of Jaguar Land Rover aero) has released his PhD thesis.

http://wrap.warwick.ac.uk/143065/1/W...ylard_2019.pdf

It's a really excellent thesis, especially (for me) in:

(a) describing wake behaviour of squareback vehicles via really clear graphics

(b) correlating that behaviour with what can be observed on different squareback vehicles simply by looking at dirt deposition patterns.

It's a pretty intense read but key lessons (at least for me) can be gained largely by looking at the 3D graphics (eg page 83).

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Old 12-28-2020, 05:33 PM   #2 (permalink)
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Thanks for posting the link to this study. It is pretty heavy on math and on the soiling of the back of a square back vehicle by water spray. However, there are some great color coded diagrams of flow patterns. I would suggest pages 100 and 101 among others.

It sure helps me understand why my blue car is completely white from the rear after I drive on unplowed roads after a fresh snow. It is a square back with a flat floor and minimal ground clearance. The worst.
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Originally Posted by freebeard View Post
The power needed to push an object through a fluid increases as the cube of the velocity. Mechanical friction increases as the square, so increasing speed requires progressively more power.

Last edited by COcyclist; 12-30-2020 at 12:05 PM..
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Old 12-28-2020, 06:28 PM   #3 (permalink)
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Page 9: "However, it is not feasible to consider the interactions between individual
molecules in an analysis of air flow around an object of that scale. " — Page 9

So they use a voxel-based approach. According to
Quote:
How Many Molecules of Air Are in The Atmosphere?
theweatherprediction.com/habyhints3/976/
The molar mass of air is around 29 grams / mole (5.27 * 10^21 grams) * (1 mole / 29 grams) = 1.81 * 10^20 moles Finally, multiply by Avogadro 's number to convert moles to molecules (1.81 * 10^20 moles) * (6.02214179*10^23 molecules/mole) = 1.09 * 10^44 molecules
The difference is 44 orders of magnitude. Maybe the best approach is to use flocking algorithms to model group behavior of molecules to get it down to 10 orders of magnitude and let advancing software algorithms and hardware make up the difference.

It's good to see Adrian Gaylard's stuff available again.

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