Monday, August 7, 2017

Track model

According to Google Maps, the 250 meters track has straight of length 74.8 meters and turn radius of 16.0 meters.

The measured incline along the black line in the turns is ~30°, for 6° in the straights.

Friday, March 17, 2017

Velodrome session with high wind

Back to the outdoor velodrome. I wanted to see if the anemometer could improve precision with high wind conditions. The results, after three runs of 10 laps (trying to keep the same relaxed position on the hoods of my road bike) are:

We can notice that the wind, measured near the ground (the anemometer was mounted on the drops), is in the 5-15 km/h range (weather station indicated 22 km/h this day). 

Quick updated conclusions for the moment:
  • with low and constant wind conditions (0-5 km/h), data anemometer is useless as the effect is nearly cancelled (CV~1-2%)
  • with higher wind conditions (>5 km/h), anemometer is mandatory to maintain acceptable precision (CV~1.5-3%) whereas for apparent CdA (wind data is not included into the model) variability is way of.
  • variability from run to run is still an issue in my protocol (0.343, 0.353, 0.342) even if it should be lower with the use of a time trial bike.

Sunday, January 15, 2017

Outdoor velodrome with an anemometer

On an outdoor velodrome (250m length), the rider selected the "Defined lap length" mode in the app and did a first run of 28 laps, then a second run of 22 laps. The Weather Meter anemometer, which is mounted on the front of his TT bike, records axial apparent speed:

We clearly see that the wind, during the session, is not constant at all, and has a range of [1-5] km/h. The mean axial wind, on the whole session, is -0.19 km/h. It indicates that the calibration factor used in the app to take in account the stagnation effects seems slightly underestimated. It is confirmed by the computation of real CdA vs apparent CdA (air vs bike speed in the drag force): 

First run:
0.303 +/-0.006 (CV: 1.8%)
0.301 +/-0.005 (CV: 1.8%)
Second run:
0.308 +/-0.003 (CV: 0.9%)
0.304 +/-0.005 (CV: 1.7%)

The apparent CdA is lower than the real CdA during the two runs, which is unexpected. Nevertheless, even if there is a biais on the real CdA, we can notice two things:

-Variability is low for the real CdA, which is a very good point for the validation of the anemometer
-During out and back ride or velodrome testing, apparent CdA is accurate enough when wind is low (less than 5 km/h) as the effect is nearly cancelled in the drag force

Tuesday, November 22, 2016

Ride with an anemometer

A friend is currently testing intensively the CdaCrr app with the Weather Meter anemometer mounted on the bike. The air speed is now recorded every second, so the rider average CdA is more accurately computed. 
On the smartphone screen, two figures now appear on the top left, with first the real CdA (wind is used in the model), and the apparent CdA (model with no wind). The two are of interest, with the first one you can see the influence of your position changes during the ride, even with unsteady wind. By the second value, we can learn how drafting is important during a group ride, or how tail or headwind affects quantitatively the air resistance measured by the apparent CdA figure.

2 hours ride. Segments where CdA is averaged are indicated by the red marks.
During his last ride (between 11am and 1pm), the nearest airport station has reported a wind speed of 14.8 km/h and an east direction (so between ~80 and 100 degrees from the north). Meteorological station are at a standard height of 10 meters, so wind speed should be translated to a value near the ground (there is a velocity profile created by shear) with the Hellman estimation. A rule of thumb is to divide the value by two (or even three, according to this post). So, we guess a wind speed near the ground of 5-8 km/h.

CdaCrr app has recorded the following data: axial wind speed (subtracted bike sensor speed from anemometer air speed) and direction of travel (given by the phone GPS):

We can notice the sinusoidal tendency of axial wind speed with direction. Indeed, the axial wind speed seen by the rider is given by:

AxialWindSpeed = WindSpeed * cos(BikeDirection-WindDirection)

By fitting the data with a sinusoidal function (red curve below), we find out the prevailing wind direction 84°, east as indicated by the weather station. 

The fit also gives us the sinusoidal amplitude, WindSpeed in the previous formula, as 3.9 km/h with a wind in the previous graph between -10 and 10 km/h. Compared to the 5-8 km/h of the meteorological station, there is no surprise here.

The idea of using GPS data to check wind direction was read in this great article.

Monday, April 25, 2016

Monitoring apparent CdA during a race

I raced sunday on a flat course during 2 hours with the CdACrr app on my bike (a criterium with 30 turns). It was the first time I recorded data during a race, and the post analysis is interesting. The first 5 laps were not recorded (I forgot to push the "start run" button), but we can seen clearly on each lap the influence of drafting. I spent 8 laps (lap 4 to 11) alone in front of the race, with the peloton just a dozen of seconds behind me. The CdA was stable and between 0.30 and 0.31. Before and after my escape the apparent CdA was oscillating between 0.24 and 0.27 (I was generally in the first five positions). I think I spent the whole 14th lap (CdA~0.215 in the middle of the peloton) and the lap 13th in the first position, hands on the hoods as nobody wanted to ride at this moment of the race. The second picture is the average power per lap (300-310W during laps 4 to 10) and 320W during the last lap where I chased behind 5 riders in the final. I remenber also that during the 23th lap, I was always in the second/third positions of the peloton (CdA~0.225).

Monday, February 1, 2016

Velodrome test

Field testing isn't easy. 

Lesson one: one turn (250 meters) per lap is enough to compute a CdA value. Adding more turns for a lap do not reduce variability.
Lesson two: the first turns should be systematically discarded.
Lesson three: keeping a constant position on the bike should be the goal number one.