Friends don't let friends ride Gatorskins or Tufos.

...I would be hesitant to look at non-TT position data over wide variations in speed.

Agreed, and I would expect noisier data for non-TT positions even at constant speeds. With a mannequin in the wind tunnel we've found noisier data with hands on the hoods, and cleaner data with torso closer to horizontal. Our suspicion is the flow over the rider may be more stable with the torso closer to horizontal.

Agreed, and I would expect noisier data for non-TT positions even at constant speeds. With a mannequin in the wind tunnel we've found noisier data with hands on the hoods, and cleaner data with torso closer to horizontal. Our suspicion is the flow over the rider may be more stable with the torso closer to horizontal.

Interesting...but, I have to point out, in all of those additional examples you show, the apparent changes are significantly less than what is seen on that Grappe chart originally used as an example.

Also, speaking of the TT position being less change...having done quite a number of field tests, and having been in the wind tunnel as a rider, I would be hesitant to look at non-TT position data over wide variations in speed.

First off, in my own field tests I've observed that the results are much more consistent when testing with a TT position (where the rider is more "locked" in place...with a good position, that is ;-) than when testing with a road "on the hoods" or "in the drops" position.

Second, unless the wind tunnel testing was with a mannequin, I know there's a tendency for a rider to "brace" against increasing air speed, or even make subtle movements like ducking a head more, or decreasing torso angle as the wind speed comes up. I've found myself doing it at times, and although subtle, it can affect the results, especially with road positions.

For those 2 reasons, I try to do any of my field testing in an aero position...even if it means mounting aerobar extensions on a "gravel" bike ;-)

- As the cycling industry started to use technology that generated airflow development effects which were sensitive to Reynolds number (laminar airfoil type shapes with separation and hysteresis effects, skin suits, etc.), I threw the "CdA of a cyclist is perfectly constant with Reynolds number" out the window. Is it close enough to constant over a typical range of speeds a rider would experience? In many cases, yes!

Agreed, and I would expect noisier data for non-TT positions even at constant speeds. With a mannequin in the wind tunnel we've found noisier data with hands on the hoods, and cleaner data with torso closer to horizontal. Our suspicion is the flow over the rider may be more stable with the torso closer to horizontal.

I agree with Robert: Few parameters that appear in modelling within Continuum Mechanics are fundamental constants. Commonly, we treat them as constants since such a convenient approximation leads to empirically adequate results.

. See below for a speed sweep we did on one of our track team riders last year, using a "rough" suit.

I noticed you noted that. :-)

Another consideration is to view CdA, Crr, etc., as distributions, not as specific values. Thus, we might say that--with a given probability--a<CdA<b; e.g., Fig. 11 of https://arxiv.org/pdf/2005.04229.pdf .

I've seen CdA vary with speed pretty much since I started aero testing back at university. Have a google on "drag crisis" and you'll see pretty quickly why cyclist's CdAs are sensitive to speed. I've measured it on track, as you've seen from the Cycling Weekly test data posted in here from 2017, but also in the wind tunnel. See below for a speed sweep we did on one of our track team riders last year, using a "rough" suit.

BTW: One of my coauthors, and a former postdoc, Tomasz Danek is very much a "Bayesian"

Hi guys, Dan Bigham here. I'm not a regular poster on ST but I got sent this thread and found it pretty interesting! I have a few things to throw out there that I'm sure you'll enjoy digging in to.

I've seen CdA vary with speed pretty much since I started aero testing back at university. Have a google on "drag crisis" and you'll see pretty quickly why cyclist's CdAs are sensitive to speed. I've measured it on track, as you've seen from the Cycling Weekly test data posted in here from 2017, but also in the wind tunnel. See below for a speed sweep we did on one of our track team riders last year, using a "rough" suit.



As velocity increases surface roughness (skinsuit fabric) helps to transition the boundary layer to turbulent, and therefore increases it's resistance to separation in negative pressure gradients, reducing pressure drag. This is the critical Re range, above this there is no further benefit and CdA starts to increase again as velocity increases further. Plenty to read here for those interested: https://www.sciencedirect.com/...167610520300532#fig1

It's even more clear in fabric testing. I'm not at liberty to share any of our (HUUB's) test data but luckily enough Rule28 Clothing have shared theirs publicly, which was nice of them! For full disclosure, I don't work for or have involvement with Rule28. They used to sponsor both my road and track teams (Ribble Weldtite & HUUB Wattbike), and I tested their original aero sock back in 2017 for them. Link to their testing overview here: https://www.rule28.com/...2020-product-testing and to download their fabric testing data off their website here: https://drive.google.com/...X_USiyV90mYbWSZ/view



Just one of their fabric's graphs.



Have a look through all their fabric data and you'll see how varied each fabric's performance is, even when it is just simply flipped or rotated. It's why I've always found it funny when "aero stripe" is talked about like it's one fabric that performs the same, when there is probably >40 "striped" fabrics on the market all with their own Cd-Re curves that are wildly different. Obviously, this data applies to a cylinder (arm or a leg), but the principle still stands for a cyclist that is effectively a collection of different sizes cylinders (~ish). I've tested a lot of suits that clearly "drop in" at different speeds, depending on the fabric chosen. This aligns well with fabric testing. You can run the numbers on the size of a rider's lower leg to get frontal area, multiply up by the Cd at your chosen Reynolds number (speed) and see pretty quickly how big of a CdA reduction you are likely to get.

Hope that adds a bit to the discussion, even if it presents a few headaches for those doing variable speed runs to separate out CdA and Crr. The Crr-speed sensitivity is a whole other can of worms!

I've measured it on track, as you've seen from the Cycling Weekly test data posted in here from 2017, but also in the wind tunnel. See below for a speed sweep we did on one of our track team riders last year, using a "rough" suit.

Is this method for calculate Crr any similar to GC? Or it could be more accuracy?

https://www.researchgate.net/..._on_aerodynamic_drag

"Firstly, the cyclists performed a discontinuous incremental exercise at different V to determine the rolling resistance coefficient (Cr) from the RT-V2 linear regression method (Grappe et al. 1997)"

Ok, undertand it.

If I want to control Crr (Coeficient), without calculate it, which variables I need to control? Air temperature and wheel pressure? Or perhaps wheel width also?

If all you want to do is compare CdA, just use the same tires at the same tire pressure. Measure (or estimate) air density and use a fixed constant value for Crr. That won't be exactly correct, but it will get you close. Then you'll be able to get an approximate value for CdA for that particular assumed value of Crr. You can use that to estimate speeds at different powers. That's what I did for a couple of hour record attempts: we couldn't have known what the air density was going to be on the day of the attempt, nor what power the rider would actually be able to maintain so I put together a table that said, "with these values for Crr and CdA, if the air density was *this* and you could average *that* power, then here's how far you will go." After the attempts, we knew what the air density had been, and what power the rider had produced so we could see whether the predictions were correct. Pretty damn close.