Not long after Jodi Christie clinched the Mopar Canadian Superbike Championship at Canadian Tire Motorsport Park in August last year, John Sharrard – who runs the Accelerated Technologies/Honda Canada team and is Jodi’s crew chief – and I were busy plotting Jodi’s title defence for this year. History shows that it’s often more difficult to retain a No. 1 plate than it is to win in the first place, as you have that big target on your back.
Jodi will again be riding for Honda Canada, and John already has bikes in his shop. Last year Jodi rode the standard CBR1000RR in the Superbike class, but this year will be aboard the SP model, which does offer some benefits. The SP’s upgrades in production form include Ohlins suspension compared to the standard Showa bits, Brembo brake calipers, and hand-picked pistons and connecting rods. While some of these parts will be lost in the transition to Superbike trim (for instance, the Ohlins shock will still be replaced with a more familiar Elka unit), there are some definite advantages to using the SP.
For my part, I have been busy going through the 2.5 gigabytes of data we collected last year and trying to pinpoint areas of improvement. One of the aspects of competing in the Superbike class that we have known for some time is that those improvements come in very small increments, and as the result of very small changes. At Jodi’s level, you will not find a setup change that will produce a one-second drop in the lap time; we are dealing more with one or two tenths of a second – if that – and making sure those tenths apply over the entire race distance rather than just a single lap. Stack up all those little gains, and that’s when you start to make some headway.
How do you find those tenths? As we have been finding out, analyzing data is a lot more than just looking at squiggly lines on the computer screen; we have to use mathematical analysis and combine multiple channels to find the details we need to see in order to make these small changes. The more accurate the data you begin with, and the more detailed the manipulation of that data is, the better the results are in terms of tracking down those improvements and making changes accordingly.
The example I like to use is this: One aspect of setup we look at concerns dynamic geometry and how rake and trail change as the motorcycle goes around the track – something I’ve discussed in a previous blog. Dynamic rake and trail depend on a number of variables, however. One part of the accuracy we need is taking into account as many of these variables as possible when determining dynamic geometry, which may require adding more sensors or deriving the necessary information from existing channels.
The other part is being able to determine with some degree of confidence how a setup change will affect each of those variables and, in turn, affect dynamic rake and trail. Effectively, this means being able to predict how the motorcycle behaves in theory, given the most basic data and information. When you consider that Jodi can feel a change of just a few millimetres in the motorcycle’s attitude at a given point on the track, it makes sense that this much detail and accuracy is necessary.
The 2015 season begins at Calabogie Motorsport Park on June 12-14, where we will continue to put all this theory to the practical test.
{fcomment}
<
Discussion about this post