Wheel Selector

Since I’m currently a cyclist in winter (aka lots of free time), I’m trying to build a little app that shows you which wheels are actually fastest over an entire course. What am I talking about? Well there’s a lot of different depths of wheels, usually trading of aero advantage for weight.

Mostly I’m curious where that trade off point is, or even if there is one. It may be that a 90mm wheel is ALWAYS faster, or that a super shallow lightweight wheel is the way to go.

In the first iteration it’s just going to take in rider metrics, and general stats from the ride like distance, speed, and elevation gain…make a lot of assumptions, then give you an estimate of required work (kilo-jouiles) for each wheel selection.

This will be pretty crude at first, you know, no drafting, no cross winds.

So far the hardest portion of this has been trying to figure how to factor in changes in wheel drag to the overall drag of a cyclist.

To do this properly I figure you need to break the rider and wheel into your individual drag coefficient areas. This way you can do sort of a component addition of which wheel you’re using (or more specifically an addition or subtraction of CdA to your overall CdA)

    drag

drag

                drag

Like this, except with bicycles.

The difficulty is that there’s a lot of data on CdA of riders (especially with respect to hour records), but nothing that I’ve found so far on drag on individual components IN THE SYSTEM.

There’s a lot of wheel drag data out there (BS or otherwise) but the way the air flows around the wheel with a bicycle and rider present is difficult to distill.

 

 

StumbleUponRedditShare

3 Comments

  1. Yeah, Road Bike Action recently did a test between a TT bike, aero road and standard road bike. Without a rider, the aero road bike was much faster than the road bike, but when you put a rider on it, the difference was 6 watts. Seems like it comes down to rider position on the bike, so knowing the CDA of the rider in their “normal” position is important.

    Also, if you could get an iBike Newton and run a crank based power meter with it, the Newton will give you on-the-go CDA numbers; Boyd does this with his TT bike, I believe. So, you could go out with a few different wheel sets and go up and down the same stretch of road a few times and get a decent idea how only a wheel change affects the whole system.

  2. I have a Newton and G3 Powertap that I run together.
    The Newton is impressive in what it does (IOW, it does very well) but my experience with the moving CdA is less that impressive. The numbers always come out much less that reality.
    I might be doing something wrong but I sure don’t know what it could be.
    Best of luck in your project.

  3. Right there’s a lot of regression methods to obtaining CdA. I’ve watched the video of the RBA bike test. The position discipline of the rider during the test is extremely important like 6 watts is just moving your head up or down like an inch so its difficult to separate out things like that.
    Trimag did a good test of wheels in bike:
    http://triathlon.competitor.com/files/2013/02/TunnelVision.pdf
    Again no rider though, which is good and bad, you dont’ get the inconsistencies of having a rider on the bike, but you’re getting more drag effects from the rear wheel than you would in real world riding

Leave a Reply

Your email address will not be published.