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)
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.