Field Testing: on-road rolling resistance

I just got back from a little vacation - it's always nice to catch up with family you haven't seen in a real long time...and that whole vacation thing can sometimes be a bit re-energizing too!

Here's a pretty neat shot I managed to snap of a small cabin in the woods that we were helping to stock up in Northeastern Oregon last week:



Hey, now that I'm all re-energized and whatnot, I decided to take another look (kind of in aggregate) at the data I gathered in this BTR Forum thread:

http://biketechreview.com/forum/viewtopic.php?f=1&t=2552

All in all, thus far, I have managed to do 6 runs at 140 psi and 4 runs at 120 psi on a road that I would characterize as pretty rough. There's a picture of the road surface in the thread linked above, so you can make up your own mind, though.

As it turns out, the 140 psi runs on the road had an average Crr that was ~10% lower than the 120 psi runs.

Interestingly, I also tested the same tires on my smooth aluminum rollers in the garage just after these on-road field tests...and not too surprisingly, the 140 psi run was around 8-10% lower in it's Crr than the 120 psi run.

One might also expect that the on-road Crr values would be higher than the smooth aluminum roller case...and, indeed the on-road values were in the 35% higher ballpark. So, that'd mean that if one was using the Crr values here to estimate things/tradeoffs:

http://www.biketechreview.com/tires/AFM_tire_crr.htm

you might only have to multiply the crr of the tires by 15% or so...more work to be done on this topic, though, eh?

These preliminary results are encouraging to me, in that things seem to be consistent (trend-wise). It's good to pursue multiple, independent, lines of inquiry when exploring a topic, eh? But dang, this field testing stuff is pretty tedious and just about as much fun as watching grass grow! :-)

In Summary:

-140 psi was a wee bit faster on average than 120 psi in this comparison
-rough roads seem to have a higher Crr than smooth rollers
-field testing in order to determine Crr is difficult and boring business

Put 'em up!

This here's another wind tunnel shoot-out! ;-)

Tested a couple of fast wheels and a slew of fast tires (which just might make more of a difference than the wheel itself).

Standby for a heads up on how you can gain access to the same kind of results that manufacturers generate.

Field Testing and Wind Tunnel Testing

Over the past several years, I've had the opportunity to do some wind tunnel testing with a variety of folks. On several occasions I've also had the opportunity to try and correlate the wind tunnel data to power meter based field tests.

Here's the field testing methodologies that I've tried in the past:

-flat, regression/Lim method
-indoor velodrome, caveman method
-dip/half-pipe chung method
-dip/half-pipe work per lap bootstrap method
-short laps-flat-ish work per lap bootstrap method
-long laps-flat-ish work per lap bootstrap method

It's been my experience with the data from the San Diego wind tunnel that the indoor velodrome has produced the best results when it comes to the academic exercise of matching wind tunnel data to indirect estimations of CxA based on power meter data. In windy conditons, it seems just a bit too easy to "post hoc adjust data" using stationary wind probes without high resolution data logging capabilities, in order to make things turn out the way one wants them to turn out.

The other methods (i.e, not indoor velodrome data) seem a bit cumbersome/time consuming and un-predictable/unrepeatable, in my experience (I had some pretty good calm Lim method data taken over many successive days that on average correlated very well with lswt.com data, but this process took something like a week of early morning test sessions, IIRC - man, I don't have the patience for that ;-) ).

It's my current perception (which is, of course, subject to change based on new reliable information) that many folks who distribute information all over the internets are using math models, or field testing data reduction techniques, that don't quite capture exactly what is going on - which I feel can lead to inaccurate results from an aerodynamic body axis coordinate system perspective.

So, basically, just as I mentioned in this forum thread:

"often times, it's helpful to pursue multiple, independent lines of inquiry when attacking a problem. Field testing is but one way to gain insight, and hopefully, this methodology (referring to the work per lap bootstrap/chung approach) doesn't steer folks unknowingly down a wrong path. That would be a bummer!"

Power Meter Field Testing - WLB Method

Over the past few months, I've been slowly generating data on a variety of field test venues with my SRM power meter.


Any of the variety of field testing methods out there can produce a number. The thing that has always made me curious about those #'s that are generated is "how reliable are they".


The best I've been able to do with the various field testing I've done is +/- several percent on aero props and Crr props using a tedious regression technique that takes about 45 minutes to generate data on a couple setups.


I spent some time and whipped up a script that incorporated a suggestion (thanks Adam!) - where one uses the work per lap of a loop course to generate estimates for cxa and crr -> more discussion here:


http://forum.biketechreview.com/viewtopic.php?f=1&t=2307



the last step for me was to use these estimates and assume that cxa doesn't change over the laps (reasonable!) and calculate the crr on a per lap basis. If one did 6 laps, they'd have 6 independent estimates of crr assuming a consant cxa generated from Adam's work per lap regression method. Similarly, one can use constant crr values across laps to come up with 6 independent cxa estimates (1 per lap).


We can then bootstrap these data sets to get an idea about how reliable the final estimates for cxa and crr are.


Here's a sample plot that shows the output from one of my field tests:







pretty crafty, I reckon -> Adam is a pretty smart dood to figure that one out!


So, yeah, I reckon I'll call this compilation of data reduction techniques the "WLB" method -> y'know, Work per Lap Bootstrap... d'oh!


Here's the wordle version of the data reduction script:





holy cow, that's pretty nerdy, huh!?