Is this man Yamaha’s secret weapon in MotoGP?

These days riders spend more time looking at computer monitors than they do riding their motorcycles, because all those squiggly lines contain the secret to going faster.

It’s therefore no exaggeration to say that O’Kane’s work on data acquisition and chassis dynamics revolutionised motorcycle racing all those years ago and since then he’s never stopped pushing forward to learn more about motorcycle dynamics to help make bikes work better and go faster around racetracks.

Back in the early 1990s when I worked for Team Roberts, ‘King’ Kenny allowed me to examine and then publish the first data traces I’d ever seen, of a Rainey lap of Donington Park.

The data was a revelation: the squiggly lines told you so much about how Rainey rode, most impressively the detail of his throttle technique exiting corners: the throttle trace rising, then dipping briefly as he feathered the throttle to control a slide, then rising again and dipping slightly as he controlled another slide and then finally to full throttle when he finally found enough grip. The data belonged to Team Roberts, not Yamaha, but even so Yamaha wasn’t happy when I published the data. No one ever told ‘King’ Kenny what to do!

A couple of years later I was sat in the Team Roberts truck with O’Kane and the team’s lead engineers, Mike Sinclair and Warren Willing, to examine the first computer simulation of a motorcycle going around a racetrack. This was basically a stick man aboard a stick motorcycle, but you could see everything working – the suspension compressing, the geometry changing and so on. To me, it seemed like going to the moon, but MotoGP has come much, much further in the last three decades.

A decade or so ago O’Kane started working on his thesis – High-fidelity modelling of motorcycle dynamics – for a PhD in the Faculty of Science & Engineering at the National University of Ireland, near Dublin. The thesis represents a major advance in this area of performance research. It was published in 2018 and gained O’Kane a doctorate of philosophy.

It’s a momentous piece of work, which reads like Double Dutch to any normal mortal. I’ve asked several other experienced MotoGP electronics engineers to read the 85,000-word thesis and even they are astounded by its depth, reach and complexity.

This is what O’Kane writes in his introductory remarks: “This thesis is an attempt to understand what makes motorcycles and tyres behave the way they do when pushed to the limit of their performance, while laying the foundation for the engineering tools needed to exploit that knowledge”.

All those years ago I can remember ‘King’ Kenny telling me that motorcycle racing isn’t rocket science, but recent advances by O’Kane and Ducati in particular are making it so.

I do not have anything like enough brainpower to understand the thesis, so all I can do is offer you a tiny glimpse of the many equations that are its backbone to give you an inkling of the level of thought, intelligence and knowledge now involved in MotoGP.

This is what’s popularly known as the rocket equation, used in early problem-solving strategies of rocket propulsion, derived by Soviet physicist Konstantin Tsiolkovsky…

And this is O’Kane doing some tyre modelling for tyre slip and deformation…

So while the rider is charging around the race track, risking life and limb, and you’re sat on the sofa, beer in hand, these are the kind of equations and calculations being made in the back of the garages of the more advanced factory team garages, every second of every session and every race – electronics engineers, terabytes of data and hugely complex equations, deciding who finds a hundredth of a second here and loses a hundredth of a second there.

O’Kane undoubtedly helped Yamaha win its three titles with Rainey, in 1990, 1991 and 1992, when the YZR500 was the best-handling bike on the grid. When Roberts quit Yamaha to build his own MotoGP bikes, O’Kane played a hugely important role in the process, helping to create the Proton KR3, which was known for its razor-sharp handling and became the last two-stroke to score a MotoGP pole position. The same went for Suzuki’s GSX-RR, winner of the 2020 MotoGP championship, which was the most rider-friendly bike on the grid during the last few seasons.

“One of the most comprehensive pieces of work undertaken to understand the physics of a motorcycle”

Surely O’Kane’s knowhow gave Suzuki a particular advantage during MotoGP’s 2020 Covid season, when most races were run at unusual times of year, when anyone who could model motorcycle and tyre performance in unfamiliar conditions would have a real advantage.

Yamaha certainly realises O’Kane’s brilliance, and at a time when computer power is just about as important as horsepower, the Irishman’s return to the factory for the first time since the late 1990s could give 2021 champion Fabio Quartararo and team-mate Franco Morbidelli a real boost in all-round performance.

One of the many illustrations in O’Kane’s thesis, which, to those who don’t understand the details (like me), at least gives you a hint of the complexity of modern motorcycle racing

The crux of O’Kane’s thesis is mathematically constructing a multi-dimensional model of the motorcycle to evaluate the dynamics and forces going into the motorcycle.

Multibody dynamics is the science of studying the motion of complex mechanical systems under the application of mechanical forces. In other words it’s a very clever way to predict how a motorcycle and its tyres will behave in every situation. You simulate performance to optimise performance.

This, of course, is what Ducati has been doing so successfully over the last few seasons and there’s no doubt that the company’s MotoGP engineers will have read O’Kane’s thesis, which is publicly available, to help their push into this radical new area of MotoGP tech.

According to Paul Treacy, who worked with O’Kane at Team Roberts, the thesis is a “remarkable piece of work, and in my opinion one of the most comprehensive pieces of work undertaken to understand the physics of a motorcycle under racing conditions”.

The problem in MotoGP, which is inheriting the basics of this technology from Formula 1 cars, is twofold: motorcycles are much more complicated than cars and there’s much less money in bikes, so budgets are bound to be an issue.

O’Kane also faces a particular challenge at Yamaha. Computer modelling relies absolutely upon data inputs and the more data you have the better chance you have of correctly predicting machine behaviour to improve performance. But Yamaha has only two YZR-M1s on the grid this year, so he’ll have just one quarter of the data that Ducati engineers get from their eight Desmosedici riders.

Can O’Kane help Yamaha regain the MotoGP title from Ducati? Maybe.