FLO Gravel Wheel Design Journey Part 8 – Collecting Data – How Part 1

How We Got To Measuring Crr On Road Reliably

Part 7 of this journey described Why we wanted to collect on road data. This article begins to describe How we went about data collection.

Step 1 – Finding a Course and Accurate Elevation Data

If you remember from Part 6 of the Gravel Wheel Design Journey, I had spoken with Robert Chung about using the Chung Method to simultaneously measure CdA and Crr. The first thing I needed was a course and accurate elevation data. Below is a Google Earth profile of the course I selected.

For this method, we needed accurate elevation, which I figured could be pulled from an iPhone or Garmin. However, I learned that GPS elevation data on a devices like these are typically accurate within 3 to 40 meters, depending on your access to satellites. This would not work.

So, I rented a Trimble R10 GNSS System which has an accuracy of +/- 5cm when measuring elevations.

The Trimble R10 allowed us to collect the data we needed. The picture below shows one of the elevations pulled from the course.

Step 2 – Finding a Computer To Collect The Data

We’ve been collecting data on road since 2015. I built a sensor that allowed us to collect yaw angle measurements for our optimization algorithm. The picture below shows the computer we used which is now jokingly referred to as, “the blender.”

The blender, while advanced for it’s time, was not going to cut it. Our goal was to find a computer that allowed us to determine Crr. We explored options, which were few and far between, and reached out to Red Is Faster, a company out of the UK. They had a beta version sensor available for collecting data so we decided to try it out.

Step 3 – The Initial Tests & Data Processing

To begin, we tested two different scenarios, pressure and internal rim width, to determine if we could measure rolling resistance on road.  

    • Pressure difference: 80 psi vs. 120 psi
    • Internal rim width difference: 17mm vs. 21mm

Below is a picture taken during the initial tests.

The data collection was a breeze, however, the post processing was challenging and extremely time consuming. Each test run took us about four hours to process. I reached out to Ryan Cooper, one of the industries best mathematicians, to see if he could help out. Ryan was able to determine that different pressures and rim widths had an affect on rolling resistance.

The ease of collecting the data with Red Is Faster was great but unfortunately, the post processing time was not ideal for the amount of data we needed to collect. We decided to continue to look for a sensor that would work better for our specific needs.

Stay tuned for Part 9 of the FLO Gravel Wheel Design Journey where we continue talking about how we collected the on road data we needed.

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