Endurance Analytics

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Aerodynamic interaction effects in group riding

As little as 5 years ago cycling aerodynamics was still in the wild west. Very few riders had ever seen the inside of a wind tunnel, myths prevailed, and we all had much to learn. But things have moved on...serious cyclists are now routinely using aero data to optimise race preparation, and we understand not only what positions or equipment are faster but how and why. It would help if all manufacturers were forthcoming with performance data, but we digress...

If you have seen any of the recent wind tunnel videos from Specialized, or the series presented by Chris Boardman with ITV4’s Tour de France coverage, then you’ll have noticed that with the basics well understood more efforts are being focussed on the nuances of drafting and group riding. “Interaction efforts” are the big new direction, understanding them offers a lot to anyone participating in team or mass start events, or simply with some cheeky drafting opportunities, and we ought to pay attention.

But there is a problem. Wind tunnels weren’t built for groups of riders, and they certainly weren’t built to evaluate individual drag forces on multiple riders at the same time. Studies of drafting effects began in a more practical manner with power meter data, where Broker et al. (1999) revealed that “man 2” in a 4 man team pursuit (at 57-60kph) needed about 71% of man 1’s power, while men 3 and 4 needed about 64%. More recently researchers such as Blocken et al. (2013) have used CFD (Computational Fluid Dynamics) software to simulate the theoretical effects of group riding, with similar conclusions.

Though power data can be converted to drag data using the "Martin et al." mathematical model adopted by CPL there are some problems with relying on this approach to estimate changes in a more useful number, drag. It’s really difficult for a group of riders to maintain constant wheel-wheel separation in a field test, and as we know on an intuitive level this makes a big difference to drafting effects. Meanwhile CFD simulations require simplifications which may spoil their ability to reflect the real world.

Just last month the journal “Sports Engineering” featured a key article from a team (Barrey et al, 2015) working at Monash University, Australia.  Uniquely they have worked with modifications to their wind tunnel, allowing 4 riders to be evaluated individually and simultaneously. They’ve shown how, with a 12cm separation, man 1 can expect a 5% reduction in drag, man 2 gets around 45%, while men 2 and 3 can expect around 55%. These are averages of all possible combinations of the 4 riders because, crucially, optimising these things depends on the anthropometrics and baseline CdA’s of the riders. When you plug these drag reduction factors into a power model the results compare well with the power based study mentioned above.

If anyone has put more than 4 riders together in a wind tunnel then we haven't heard of it, so CFD simulations can still add some valuable insights. For example, Blocken et al have shown how, in a pace line of upto 8 riders, there is a gradual reduction in drag force through positions progressively further back in the group. This occurs due to a gradual downstream widening of the group’s wake. In groups of 5 or fewer it is the last rider who enjoys the greatest drag reduction. But in larger groups the place to be is last-but-one, since the diminishing benefits of wake are eventually offset by the reduction in pressure drag that comes with a following rider. Important lessons here for anyone involved in group riding.

What does all of this mean for ride modelling? First of all, to do a good job of modelling team pursuit or team time trial events requires a matrix of drag reduction factors. Every rider has a baseline CdA, which falls by a certain amount given their position in the group, who is ahead and behind. Team performance still hinges on the Watts/CdA of the lead rider, rather than some notion of “group average Watts/CdA”, but the drag experienced by drafting riders can be important in modelling recovery. Secondly, the insights here will allow us to do a better job of modelling mass start events. If the type and extent of drafting achievable can be factored into an Event Model then we would expect time-on-course predictions to get even closer to reality. For the mean time, when using CPL, you may wish to apply “rule of thumb” adjustments to CdA - but watch out for some more sophisticated modelling options.

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