Over the winter I had pored over last year’s data fairly extensively, and had come up with some changes for Jodi's Honda CBR1000RR and different ways to analyze the various data channels. After putting together more than 50 math channels to combine or dissect raw data, this would be the first chance to put some of those channels to use.
With any big bike such as the 170-horsepower Honda CBR1000RR that Jodi rides, squat and anti-squat are usually the dominating factor in setup. I have discussed these characteristics in detail previously; squat is the tendency for the rear suspension to compress as the motorcycle accelerates and transfers weight to the rear tire. Anti-squat, working against squat, is the tendency for chain pull and the driving force from the rear wheel to extend the rear suspension under acceleration. Squat is good because it puts more weight on the tire for more traction, but at the same time anti-squat can be used to literally drive the rear tire into the ground for more load.
It can be a real headache when it comes to finding a decent setup, because there is such a narrow “sweet spot” in the suspension’s travel where the forces all balance out just right for best traction. Err too much in either direction and a good-handling machine can quickly turn into a sliding, bucking bronco.
There are a couple of ways to put numbers to anti-squat. One is to measure all the pertinent bits like tire diameter, swingarm length, the distance from the swingarm pivot to the countershaft, the ride height and so on, and calculate a theoretical value. As setup changes are made, new parameters can be input into the equations and the change in anti-squat can be calculated. The calculations are not simple, but easily managed in a spreadsheet program.
Since we have suspension potentiometers as part of the AiM data acquisition system on Jodi’s CBR1000RR, we can use math channels to look at squat directly and see how the bike reacts to setup adjustments, backing up the theoretical numbers. Perhaps more importantly, combining the suspension data with GPS and other channels, we can see when and how quickly the bike squats in each turn.
Over the course of the weekend at Shannonville, I was switching back and forth from Excel and the theoretical numbers to AiM's Race Studio 2 analysis package and the actual data from each session, making sure that squat was staying constant with each change we made, and that the data correlated to the theoretical numbers when we did want to change squat. It may seem like such a small detail, but the data was showing noticeable changes in squat with even small adjustments to ride heights and even damping.
At Shannonville, we were able to find a good setup for the 1000 that Jodi was comfortable on; he was at or near the top of the timing charts all weekend, and finished a very close second to Jordan Szoke in the final. Hopefully that’s a good indicator that we are on the right track with analyzing the data and using it to determine a setup direction.
The next event is at St-Eustache, QC, on July 4-6, where we will try out some more parts and ideas I have been working on.