Don't get your shorts in a twist.

"Ponderation" is just a transliteration from French. It's just a weighting function, or basically the histogram of the yaw experienced. (In French, a weighting function is "fonction de ponderation".

Thanks...

Any thoughts on the rest of it?

My mind is not so brilliant, but I'll answer anyway...


I've used the Mavic weighting for my combined aero+Crr calculations...but mostly because that's all there was when I started doing it. I can send you the "binning" values I've used later.

I think I'd just average the Mavic (ponderation) and the Flo weightings and call it a day.

BTW, download the freeware "Engauge Digitizer" to grab (digitize) data points off of plots :-)

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http://bikeblather.blogspot.com/

I buy what I can afford, considering how it looks first and how aero it is second. After that I simply ride my bike based on how I feel and what my power meter says and I don't worry about any of the aero shit at all.

I'm an Applied Mathematics major that works as an engineer, so I've come to understand that numbers don't lie, but people how publish them often do, and life is too short to worry about who is lying to me about CDa.

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"...the street finds its own uses for things"

I'm on the same page as Tom A. Just by looking at a couple of datapoints, the Mavic and Flo curves appear to mostly envelope the others. So, the AZ and HI curves might allow a design to be optimized to either specific locale...I don't think that what *I* want as a consumer. I want a bike that is as good as can be in as many different situations as possible.

The AZ and HI curves might allow you to say "this bike is best for Kona" and this bike is best for IMAZ. But, who buys specific bikes for specific events? Not me. Maybe if you give me flight controls that allow me to configure the vanes optimally...no, I'm can't afford these bikes, let alone an articulated version.

-tch

Not claiming any brilliance but I can think of one element that can HIGHLY confound the data collection. If I recall correctly, mavic mounted some or all of their instrumentation on the fork. And I think some of the other parties might have mounted some or all of their equipment on the frame, but with the instrumentation still located well ahead of the bike (so still in clean air).

Because even if the environmental conditions are identical, even the rider takes an identical path at an identical speed, and even if the instrumentation is set up in exactly the same place in space in clean air, just the change of fork vs frame mounting will give hugely different yaw data. When I saw that mavic did this, I wondered why in the world they would do this, especially if the point is to evaluate the whole bike + rider system ...

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Yaw depends not only on the wind speed and its direction but also on your speed, so even on the same course with the same wind a slow rider and a fast rider will see different yaw distributions. So if you really care about such things (and not everyone does) it's better to see the entire drag vs. yaw relationship than to reduce it down to one summary statistic, even if that summary statistic is based on a standardized weighting function.

Interesing (or ODD). I wondered that very thing when I first saw the Trek setup--which I thought invalidated the whole data set. The pictures are kind of grainy, but I've convinced myself that it is somehow mounted to the head-tubes on the various test bikes and not connected to the steerer/fork (ie, articulated with the front wheel, as you described the Mavic config).

We've always done frame mounted, in such a way as to put the business end of the sensor on the centerline of the frame and far out enough to be in clean air, as DSW noted.

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Carl Matson

Yeah, I planned to integrate this into the model as well. But that's not the purpose of this thread.

Quantifying that metric is important because nobody seems to do it. There's this general understanding "if i'm slow high yaw is more important whereas if I'm fast low yaw is more important". But nobody does a very good job of framing those differences with maths. Maybe you're the guy to help do that?

I have the mavic data digitised, happy to share. I used it to validate and improve the modelling I've been doing for a long time.

I don't consider it worthwhile to try to isolate one curve that is representative, consider how different this modelled distribution for a particular ride in Florida is to the Kona data.


Most people are trying to qualify for Kona in far narrower yaw distributions than they will encounter on race day.
Two different years of IMNZ


For myself, I'm most interested in sub 5deg.

But a summary statistic is nice because it's hard (even for people who care and have some familiarity with such things) to look at a series of drag vs. yaw plots and decide which one would be optimal for a specific person at a specific type of conditions. You can ballpark, particularly for extreme conditions (if very low wind and you're fast, it's an easy call).

It might be nice to have a kind of heat map. Where, say, the x-axis is your expected average speed and the y-axis is a sort of ponderation index (in some order of monotonically increasing yaw). The heat values would be the NDRV numbers for a particular piece of equipment. Then you could identify the sweet spots pretty easily.

Brilliant. I'll PM you

That 'metric' isn't...

The yaw angle distribution is meaningless without three other measurements and can't be applied without three (or more) rider properties. That's why nobody 'does it'.

If you're only going to report one number, I would lean toward using something that emphasizes low yaw -- so Flo I guess. High yaw effects are specialized and can be large. I would never choose a bike because of its high yaw performance, yet using the Mavic weighting will result in that.

Another option would be to report two numbers: a low yaw weighting and a high yaw weighting. The Mavic seems good for the high yaw one. For the low yaw one I'd use something even narrower than Flo, that puts weight only on -5 to 5.

Yeah, I know this. But I think that's garbage. Very simple for a company to provide an excel file with dynamic inputs so the customer can contextualize the data for them. The maths aren't that difficult. Talking about it is, unless you stipulate, like cyclenutz has in his data, that these figures apply to a 5 hour rider who has x, y, and z.

You can't have it both ways, coming here asking for help with the 'yaw thing' in your 'model', and then refute my hint that it's very much more complicated than a distribution weighting with "the maths aren't that difficult".

edit:spelling

Trek doesn't use a simple distribution weighting formula.


They have all the data required to do this and they indicate how non-trivial it is to properly quantify what's 'fastest'.

Mavic and Flo might have all the data but they don't (?) indicate that they use it properly, they do however give you some 'yaw angle distribution' which you seemingly want to run with...

RChung told you what to do. Just present the drag coefficient vs yaw angle. Corrected for beta-squared of course...

"mavic mounted some or all of their instrumentation on the fork."

i can absolutely see why trek mounted theirs on the frame (they make frames). i can see why mavic mounted theirs on the fork (they make wheels).

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Dan Empfield
aka Slowman

I've thought reasonably little about yaw sweeps (and don't have a personal investment in it at all), but as a part-time statistician (Psychology professor, I teach stats) here is how I would model it:

Yaw is composed of 3 factors: Wind direction, wind speed, and rider speed. We need to treat each of these separately, determining the appropriate distribution type and the parameters of these distributions and then compute average across those distributions.

I would run a few different models for different conditions (and then average them for an overall winner if you want). The first would be based entirely on you Kiley, since you are the rider this test was centered on (your position, your bikes, etc.). The second would be on an average pro rider and the third on an average MOP age group rider. For each of those situations I would do the following:

I would treat wind speed and rider speed as gaussian distributions. Wind speed would be the same across all three models and be as global as possible. Find some meteorological database on wind-speeds during daylight hours at varied populated land sites around the world and calculate Mean and SD wind speed. Generate a Gaussian (normal) distribution with those parameters.

Rider speed is individual depending on the model. For Kiley's data, I want Mean speed and SD of speed across a few 70.3's (I think that is your primary distance?). That SD is of frequently sampled speed data that you should be able to get off most good cycling computers. It is NOT the Standard Error of the Mean (SEM) that you would calculate by just comparing Average bike speeds from a bunch of different races as that would drastically underestimate the actual moment-to-moment variations in speed that matter for yaw. If you can't pull out the moment-to-moment speeds, you could estimate SD from SEM using the simple formula of SD = SEM * sqrt(N) since SEM can be estimated from SD and N using SEM = SD/sqrt(N). You'll probably have to do something like that transform anyway for the other estimates of MOP and Pro since you are unlikely to have access to moment-by-moment data from other riders, but you will have access to mean speeds across different individuals from different race results. I would actually bet you can do a damn good job of estimating the SD for these riders since SD is likely to scale linearly with average speed, so you could use your own SD/M ratio (assuming you can calculate it from raw moment-to-moment data) and apply it to the Means of the other data sets for a pretty good SD estimate.

Wind direction is the easy one: Model it as a rectangular/flat distribution across all possible angles. That is of course entirely not true (wind has a funny habit of coming from the west usually), but since roads seem to go all sorts of directions (yes they are weighted to run N/S or E/W), its fine to treat wind direction as flat or unpredictable because this needs to generalize across any race.

So now you have three distributions, two of which are actually interesting, and you simply apply the combined probabilities to whatever yaw data you have (-10, 0, 10 if I remember correctly) and weight the data accordingly.

Again, this is how I would approach it as a statistical problem that is blind to all the nuances of cycling and racing and just treats each component as a piece of numerical data. All of that is probably worth $.02.

A good point.

And, yes, it makes some sense if one is only interested in front wheel drag/yaw. But for rear wheel drag/yaw and/or total system drag/yaw, then it is far far less useful. Also, I believe they published the yaw ranges that they measured on the Kona course and kind of represented them as such. The problem is, they were not really the real yaw ranges of a rider on the Kona course. They were the yaw ranges experience by only the front wheel on the Kona course ... which is sort of a different thing.

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Advanced Aero TopTube Storage for Road, Gravel, & Tri...ZeroSlip & Direct-mount, made in the USA.
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one fact well established in the literature is that i am an ignorant slut. that being stipulated to, this is why i have been saying for some years now that field testing, while lacking precision, brings some accuracy to the equation because there is no such thing as a statistical representation of the yaw of a bike. there is the yaw of everything behind the steering axis and the yaw of everything in front of it (or attached to it).

not only do wind tunnels not test for this, they don't examine what the wind does when the wheel changes tack at the velocity that change occurs while riding the bike.

for all that, tho, if i was a wheel maker i would want to know how my (front) wheel performs in the yaws it sees versus the yaws the bike sees. the very act of countersteering (true countersteering) is what keeps the bike up, and that by itself means everything attached to the fork sees a bigger yaw that everything attached to the frame.

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Dan Empfield
aka Slowman

Yeah so maybe the maths aren't that easy.

Actually, not much of what I laid out is that complicated math wise. Especially because I think you might have made it easier on yourself by having limited data. Didn't you just run -10, 0, 10 degrees, or do you have data at lots of different angles? If it is the former, then you really just need a proportion that is derived from the probability distribution function with the M and SD as inputs. The harder part is probably gathering the raw data to figure out what those parameters look like for you or some other theoretical cyclist. If there is no rush, I could do it for you once my semester ends (mid May). It would be "fun".

You could also go with the Wisdom of the Crowds approach: Pull Mean and SD from any data set you have available (Mavic, Flo, etc.) average them, and call it a day (well call it a back-of-a-large-napkin calculation).

seems drag should be displayed as a 3D surface map relative to yaw (x), wind speed (y) and wind direction (z). To display in 2D for comparative sake, create a slider tool from 15 to 30mph rider speed, a second slider tool for wind speed, and a third slider tool for wind direction. have the drag at yaw change accordingly per bike... or have rider time on a slider tool... and/or have a slider that combines wind speed and wind direction in a single slider tool with hashes on the slider with course names for each hash, using actual course data.

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wovebike.com | Wove on instagram

Funny that's exactly what I was thinking re wisdom of crowds. Just take everyone's and put them all together.

We tested 7 yaw points 9 times for each run -- well, one run was abbreviated to three angles because it was to validate a somewhat surprising result for one run.

On your proposal, hell yeah. It is pretty fun, actually. Fun with numbers. Maths you can apply.

I do generally agree with the outline of the approach, but Gaussian assumptions are often bad in the natural environment. I wouldn't treat them as Gaussian distributions without taking a good look at whether it was remotely justifiable; distributions of speeds are constrained to be positive, for starters. It's worth a quick look at distributions of measured wind speed at a couple of locations, but a log-normal might be more appropriate (truncated normals might be fine too). Similar checks should be done for rider speed.

If you aren't using normal distributions, then the standard deviations may not be telling you what you think. You're suggesting it as a measure of spread, but something like an appropriately scaled median absolute deviation, or another robust measure of spread (like the Sn estimator) may well be more appropriate than a standard deviation. The full rider speed distributions should be easy to pull out of a set of Garmin files from races. I'd be happy to provide some of mine (20k and 40k TTs; some 70.3s) if kileyay needs a giant stack of race files for building distributions of rider speeds.

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http://ironvision.blogspot.com ; @drSteve1663

Dan Connelly did some work on this a few years back on his blog: http://djconnel.blogspot.com/...-wind-speed-and.html

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http://bikeblather.blogspot.com/

Yes, very well said.

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Advanced Aero TopTube Storage for Road, Gravel, & Tri...ZeroSlip & Direct-mount, made in the USA.
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It seems to me that if you really wanted to understand the time that a particular rider spends at x yaw, what you would do in an ideal world is run 1000s of riders on 1000s of courses, rather than have it based on a few runs on a particular course.

Until you get a data set like that and then compare it against something like rider power (CDA would be too hard - power is an easier proxy and likely readily available) you're working on generalizations on a very specific rider or handful of riders - and your data set would be dominated by the conditions of that course on that particular day rather than a true reflection of what a rider would experience,

My guess is that the only way to get that type data would be to talk to Argon-18 in a few years.

Neither wind nor rider speed are well-modeled by gaussian distributions. Wind is better modeled with something like a Weibull distribution, and because the aero component of drag varies with the square of speed (so the power component of aero drag varies with the cube, while the rolling resistance varies linearly), speed distributions tend to be skew.

All-in-all, it's simpler to assume symmetry across negative and positive yaw, then report the drag at a handful of yaws; like, maybe, four or five. Then people can do their own weighting. Let them ponder on their own ponderation.

Data looks pointless to me. Just give me drag as a function of yaw angle and I'll do the rest. Wind direction, speed, gust conditions, and heading are too variable in real life to pigeon hole everything into some Kona model. But just the drag vs yaw is one of the golden inputs.

This reminds me of my second day in my first statistics course.
On day one the lecturer presented a set of numbers, then a formula and hey presto, one number came out the end. That number was deemed to represent the whole set of numbers.
On day 2 the lecturer presented the same set of numbers and a (different formula) and lo and behold, a different single number popped out the end. This number was also deemed by the lecturer to represent the entire set of numbers!
I piped up and said to the lecturer that it appeared he was using whatever formula he needed, to get the answer he wanted. His response was "You sir, will do very well in this course". He followed that up with the famous statistics line "There's lies, damned lies, and then there's statistics"

My point is that your goal of getting one number to represent the aero-ness (drag?) of each bike is laudible, but as this relies on statistics, it's flawed to believe you will ever get the right number.

While your experimentation seems to be fairly robust, repeatable etc, you are still hamstrung by needing to find a single number to represent the entire data set for each bike. Without being argumentative, you simply cannot succeed in your quest.

Your latest quandary, the subject of this thread, shows the cascading affect of using multiple factors in determining the final (un)representative number. I'd suggest that rather than trying to decide which method of determining yaw probability is 'right', that you simply choose one of them and move on to the next problem. When it comes down to it, the ST brains trust will find fault with whichever one you choose, so just accept defeat and move on.

And to start the non-sensical argument, it's agreed (I hope) that the aero impact on the front wheel is the most important. (this may be why Mavic chose their fork mount system, to evaluate the yaw on the front wheel, while Trek, primarily a bike manufacturer, had a head tube mounted rig, to more accurately test the bike/whole rig). For me, since you're testing bikes with riders, and are more interested in testing that, I'd suggest using the head tube mounted rig information.

And then there's the whole discussion of which course is most representative of wind direction (yaw) experienced by a rider on a bike. We all wanna race Kona, but even Kona varies from year to year so who the hell knows what course is best.

To try and actually be helpful, pick one, use it for all the bikes (of course) and begin formulating your counter-arguments before you publish your results :-)

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TriDork

"Happiness is a myth. All you can hope for is to get laid once in a while, drunk once in a while and to eat chocolate every day"

And this is exactly why I just buy the nicest looking bike.

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--Chris

With all due respect, your stats prof sounds terrible. The first day in my class, I explain that statistics is NOT a field of taking some numbers in and popping out some other numbers on the other side. Statistics is a way to tell a story of what data means in the real world. Heck, in my classes we don't even do manual calculations of any statistics because computers are good at that part.

As has been pointed out by others, there are better distributions to use than Gaussians. They are supported by what the raw data actually looks like. As I said, I have never worked on yaw, wind speed, bike speed, or anything related using statistics, so Normal was my default distribution suggestion. Using a more appropriate and data supported distribution that can be parameterized is obviously better and more accurate. It is definitely NOT arbitrary here, it is supported by the data of how these variables actually are distributed. As with any parametric statistic, the output is only as good as the fit to the assumptions. If you use Mean and SD and assume a Gaussian distribution but actually have a skewed or otherwise non-normal distribution, then your statistics will spit out flawed conclusions. The closer the data is to the shape of the actual distribution, the closer the output will be to the real world. The only reason you pretty much have to use a distribution in cases like this is that you need to parameterize the data to ease the calculations and because you generally lack sufficient data for all the situations you are trying to model. It obviously incurs errors, but statistics is all about limiting the amount of error and knowing where it will be and how big it will be. Kiley already knows that since he was already planning on using error bars (i.e. measures of errors in statistical estimates) on his graphs. Oh, and he never asked, but I vote for 95%CI error bars rather than something like SE error bars.

One other note. The debate over Yaw-at-front-wheel and Yaw-behind-steering-axis is probably irrelevant here for modeling the wind tunnel data. In the tunnel, I assume that the wheel is kept straight with the rest of the frame, so all data that is coming out of the tunnel (and is the input to these statistical equations) is stuck in a single wheel-frame relationship. I don't think you can model how the drag would change in other conditions unless you already had 2 other pieces of information:

1) How drag changes in a tunnel when the wheel and frame are not perfectly aligned
2) What the distribution of wheel-frame relationships are out on the road

But, again, if you had both of those pieces of information, you could these factors to your estimates and come up with an even better "real world" estimate of drag in the real world for a given set of wind-tunnel data.

"Me thinks the Lady doth protest too much" :-)
While I said that statistics is chosing the formula to get the number you want, I was being somewhat facetious of course. However, what I insinuated was choice by the statistician, you (to paraphrase) "fit to the assumptions". I guess I'm contending that statistics is an art, when it appears to be a science. Regardless of what your data set represents, there are a multitude of ways it can be interpreted and the validity of those ways can be argued by statisticians until the cows come home!
In the case of selecting bicycle for triathlon, we here at ST understand that the aeroness (drag) is a very important factor when considering what bike to purchase. We look to the testers for answers on which bike is most aero. the vast majority of us are looking for one number to tell us which bike is best. Most of us don't look at yaw angle performance and compare that to the wind direction (and apparent yaw we will experience on our bikes), we just want to know which bike is best. For that, we need one number. While statistics has the ability to pop out a single number at the end of the formula, it has the ability to pump out lots of single numbers that all represent the data set depending on your interpretation.
In this particular case I think the headtube mounted sampling apparatus gives the most accurate wind yaw information, I can also see the validity of the fork mounted apparatus since we wobble our way down the road, even in still air! I will leave it to others to argue the validity of each case, because it's almost beer O'clock here in New Zealand, but just like we anticipate that the bikes will all end up being dangerously close to each other in drag, which testing protocol results in the right answer will also be dangerously close. Potato, Potato :-)

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TriDork

"Happiness is a myth. All you can hope for is to get laid once in a while, drunk once in a while and to eat chocolate every day"

I would go with FLO's distribution. They've been open in their processes and had little criticism of either their methodology or results.

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'It never gets easier, you just get crazier.'

In general I like what Flo have done, and Trek's stuff is pretty good too.

A few observations I have on this:
Wind speed and direction is very noisy outside a wind tunnel. You are not in a stable environment when riding. And the bike is not fixed vertically.

When there is enough wind to generate larger wind angles (> 8° say) the data is very very noisy. You may be more interested in handling than drag at this point. I am quite happy for wheel designers to take high/varying wind angles into consideration.

8° isn't a very large angle. Measuring this is fun.

Using meteorological data is not straightforward due to gradient and the impact of objects at ground level.

Oh, and of course, bikes don't yaw unless things are going very wrong ;)

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Developing aero, fit and other fun stuff at Red is Faster

I would also like to underline this. Turbulence intensity in a wind tunnel is low. A highly turbulent flow will behave quite differently in a number of situations, especially in case of adverse pressure gradients. Then there is the dynamic effects, hysteresis of flow separation and so on... So for lager angles of attack, a wind tunnel measurement might not be a useful representation of what happens in the real world for that angle of attack.

I can empathise with your dilemma. You do not want to overwhelm people with a plethora of data, which they cannot usefully interpret; but at the same time, you must avoid 'molesting' the data too much for the sake of simplicity.

It's received wisdom that some set-ups will be good low-yaw performers & others will be better high(er)-yaw performers. I think what you ultimately want to do, is highlight which of these set-ups is best at either, or just a good all-round bike. No cherry-picking as it were. What's good for Starky, isn't necessarily good for an MOPer with $10k to drop. Maybe I've over simplified it.

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29 years and counting

+1

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29 years and counting

This is not directly answering your query about which data set to use but rather querying the validity of all 3 and consideration of wind impact altogether.

I have experience of wind tunnel testing but not in the field of cycling. Perhaps this has all been well discussed and there are generally accepted test practices I'm not aware of. However, since I don't know that to be the case, and I'm pretty skeptical of cycling aerodynamic testing robustness in general, I think it's worth questioning.

As I'm sure everyone's already aware, wind has an airspeed gradient such that airflow in contact with the ground is zero and it increases as you rise above it. However, the bike moves forward as one object so the speed and yaw of airflow impinging on the bike will NOT be uniform except in static air.

Let me give some examples to illustrate this....
(I'm ignoring the fact that rotating/reciprocating parts, i.e. wheels, cranks and legs don't have a constant airspeed)

Case 1: Still air
The bike and rider encounters an impinging airflow with a constant velocity from ground level all the way up to the top of the riders head. A wind tunnel test for this case would require a uniform airflow across the tunnel cross section or, if a significant gradient exists in the tunnel, compensation would be required.

Case 2: Headwind
The impinging airflow velocity at the base of the bike will be in the same direction but lower speed than the top of the bike/rider. In this case the rider position, helmet and aerobars will be of greater significance than in the still air case but the wheels and frame will be comparatively less relevant. An accurate wind tunnel test for this case would require a representative airspeed gradient across the tunnel section such that airspeed increases as you move higher in the tunnel.

Case 3: Tailwind
The impinging airflow velocity at the base of the bike will be higher than the top of the bike. Like the headwind case, the entire bike is not effected equally. The lower part of the bike will experience higher airspeeds than the upper parts of the bike and the rider. So, for example, the helmet and aerobars will be less relevant while the frame and wheels would be more relevant compared to the still air case. An accurate wind tunnel test for this case would require a representative airspeed gradient across the tunnel section such that airspeed is higher at the floor of the tunnel and decreases as you move higher in the tunnel. I've never seen this configuration.

Case 4: Crosswind (at right angles to direction of travel)
The lateral component of the airflow velocity will be lower at the base of the bike than the top of the bike/rider while the component in the direction of travel will be constant. Thus, the bottom of the bike will be at zero yaw and this angle will increase higher on the bike/rider as the lateral component becomes more significant. Bike yaw, as measured at handlebar level will not be representative of the yaw at the height of most parts of the frame or wheels for example. An accurate wind tunnel test for this case would be very difficult, probably impossible, to produce. A truly representative test would require a complex impinging flow, not only that but it would have to be adjustable to mimic different wind/speed permutations, which makes an accurate static test utterly unfeasible.

Case 5: Everything else (there is some wind, direction not exactly in line with or perpendicular to direction of travel)
There are infinite combinations of the above cases and the overall airspeed/yaw profile will vary widely depending on wind speed, wind direction and bike speed. As with Case 4, anything involving a crosswind is likely untestable in a static wind tunnel.


Question 1 - What is the airspeed velocity profile of the wind tunnel used for testing? This is a basic wind tunnel characteristic.
Question 2 - Does this allow useful/accurate analysis for a bike in still air?
Question 3 - How useful is this data once wind enters the equation (in axis of travel)?
Question 4 - How useful is this data once wind enters the equation (off axis of travel)?

great thread
this is above me but what i would suggest is less can be more
ie dont try to make it more complicated as it already is. I guess you are doing an FOP test focus on getting the numbers for that
otherwise you will have to come up which something that is even more time consuming than the task you have in hand right now.

and i think a cyclenutz has mentioned its likley that 5 degree of jaw could be the best indicator for most races ( the only consistent outlier would be kona. ( of course that is of course a goal race for many FOP s )

If you look at various photos of A2 you will see that the testing fixture for bikes sits on a splitter plate, so I guess you can safely assume a pretty constant profile.

Perhaps this will help allay your fears a bit:

https://www.academia.edu/...mech_1998_14_276-291

+1. The "whirly gig" wind speed/direction probe Jim Martin developed back in the 1990s would dance all over the place, especially when a car passed you.

That said, we know that wind tunnel-predicted and actual power agree to w/in ~1% of so, and we also know that wind tunnel drag data are not influenced by the act of pedaling per se. I'm therefore not convinced that such high-frequency components have any real impact on time-averaged data (i.e., hysteresis must be minimal).

Just an FYI: people in the "speedbike" (HPV) world have been measuring such things for at least 30 y (and Argon's probe appears to be too short to provide valid data).

Possibly not the only way.

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Developing aero, fit and other fun stuff at Red is Faster

As well, even without a splitter plate the only part of the bike that would be in the boundary layer of a decently-designed wind tunnel would be the bottom few centimeters of the wheel...and if the wheels are spinning at a speed close to the wind tunnel speed, the relative wind speed there would be minimal.

Or call it meta-analysis.

One thing to note is that Best Bike Split already does a lot of this already. It may make sense to work with Ryan Cooper and find a way to input your data into his models since they are already working. This way you could model all bikes on every 70.3 and 140.6 course to find the best in each case. Of course this would need to include the weight of each setup too.

The error here is taking CdA's from a wind tunnel and applying them outside (which I guess is the point of the test anyway, since they are ridden outside). Either way, this could be a fun section to include after your own analysis.

I agree, the more I think about it. Would like to work with him

Uh, no.

Why not? As good of a job as Kiley did holding his position, I doubt he (or anyone) could be so still outside. What does that do to the data for bikes we are expecting to be fairly close together anyway?

Oh, I don't know...maybe because it was shown about 20 y ago that wind tunnel testing is highly predictive of "real world" cycling? (Note the quotes.)

He looked down the entire test. If he had been looking up, maybe it would be closer.

I'm referring to data taken in the last year by the BBS aero analyzer of the United Healthcare team in which was trying to get real world CDAs to match wind tunnel CDAs. Some people were close and some were way off because they couldn't hold the exact position in the tunnel in which they were completely still with heads down.

Were there any formal papers published on this ?

I think both you and Coggan are correct and just talking past each other. The tunnel CdA carries over to the real world. But if your tunnel position is unrideable in the real world that's a problem with your test methodology, not a problem with the fundamentals of windtunnel-derived CdA.

I do agree that I very often see ridiculously unridable positions in wind tunnel photos and "garage trainer wind tunnel" pictures with people staring down their front fork.

https://www.academia.edu/...mech_1998_14_276-291

Bingo.

More specifically, there is no evidence that there are significant cyclist-bicycle aerodynamic interactions that would occur only outside of the wind tunnel for someone using the same basic position. The notion that the bikes tested (or any others) would behave differently in the "real world" (again note the quotes) is therefore simply a non-starter.

I think my head position was in a realistic spot because I was staring at the tracings up ahead of me. I have tremendous head discipline in races, so much that I very often can't see up ahead of me.

What I couldn't do in real life is stay that tense. Trying to hold absolutely still makes you mad tense. Does moving around more and relaxing more and a bunch of other things IRL impact your "effective" CdA? Sure. But none of that is going to change the order of these frames, which is what we were trying to isolate as a variable in this test.

Kant is an idealist joke, "categorical imperitive" what a farce hybrid of philosophy and religion after Hegels tremendous step forward to tear morality from the heavens, but idealism is only a step to materialism. Kant attempts to produce supra-class moral principle, that which is detached from the ruling class' vital interest in imposing its moral philosophy upon the exploited masses.