Tuesday, January 2, 2018

Cheapest Android phone to use CdaCrr bike computer

After some research (cause people asking me when the app will be available on iOS, my answer is: sorry, never), here is the cheapest way to buy an Android phone to use the app (prices are given for December 31th, 2017) :

So for 83$*, you should have an ANT+ enabled Android phone (follow this) to run the free version of the app (classic lap mode) and aero field test on velodrome or other closed loop. There is also GPS and Bluetooth on this phone, so more CdaCrr modes could be used. Not tested yet, but I plan to buy and give a try soon.

*: You would need also an ANT+ powermeter (~450$), an ANT+ speed sensor (~35$), a phone bike mount (~10$). And a bike of course :-)

Edit 05/01/2018
I bought the Blackview A7 and it seems great for the price, but unhappily, it does not have USB host feature so we can't use an ANT+ stick :-(

Edit 14/01/2018: 
-List of ~130 Android phones with ANT+ built-in on www.thisisant.com
-List of ~460 Android phones with USB Host on www.gsmarena.com

Sunday, December 3, 2017

Virtual elevations

Last week results. The anemometer improves CdA accuracy when drafted a lot by cars. Supposed Crr at 0.004 may be too high.

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.