Today marks the start of the serious climbing in the final third of the tour. The first of the remaining four proper mountain stages features four major climbs and a total of 3,800 metres of elevation gain.
As the route climbs steeply shortly after the start, breakaway riders who are strong climbers will be looking to attack early on.
We can expect the field to become completely fragmented at an early stage.
The big question for the general classification is how long the GC contenders will stay together. The field is likely to be whittled down right from the start; anyone having an off day will drop down the standings today. But where will the real attacks begin? Only on the final climb? This is one of the toughest on the Tour, with gradients briefly reaching double figures.
We’ll start by simulating an early escape and, in doing so, examine a technical question that we’ve so far left aside: What happens, in terms of timing, if the propulsion system operates just a tad more efficiently?
Result: A half percentage point improvement in drivetrain efficiency saves 45 seconds over the entire stage (applies to a breakaway rider who spends the whole day ahead of the peloton).
We know from our tests that such differences between lubricants are possible.
Aero or wax? Both!
The professionals all line up at the start with waxed drivetrains, because waxes have proved to be more efficient. That half a percentage point difference in efficiency is roughly the difference between a very good oil and a very good Chain wax. But performance also depends on other factors: have the chains been freshly hot-waxed and are they therefore particularly efficient? For the top riders, the answer is probably yes. Less privileged riders might use a drip wax on their chain, which is easier to reapply but isn’t quite as efficient as hot wax.
What is interesting, however, is the magnitude of the effects. The simulation of all bikes for the entire stage shows that, even on this mountain stage, the aero adjustment has a greater impact than the half-per-cent increase in efficiency. An aero bike saves just under six minutes compared with a bike that is only slightly lighter. In terms of time, this is an effect almost eight times greater than that achieved by optimising the lubricant.
Surprised? That might be because we humans are poor at estimating exponential effects based on gut feeling. The power required to overcome air resistance increases cubically with speed (double the speed = eight times the power required). That’s what makes the aerodynamic effect so powerful.
Number of the day: eight seconds
Today’s simulation centres on the gruelling final climb up the Col du Haag. Which general classification contender is best equipped to tackle this climb, which is very steep in places?
According to our calculations, the Cervelo S5 is the fastest bike due to its low weight. So Jona Vingegaard has the best chance – at least as far as the bike is concerned. An extra 400 grams will cost Tadej Pogacar around eight seconds.
A half-per-cent increase in efficiency on the final climb also yields a lead of eight seconds.
In a head-to-head battle between the best, every detail counts. 6.8 kilos, aerodynamic features and fresh wax on the chain are all in place to ensure not a single second is lost.
An overview of the (almost) full line-up*:
The table shows the times for the final climb to the finish line. As the climb is steep, the top three bikes are those with the lowest weight. As the gradient increases, weight becomes a more significant factor, although aerodynamics remain a consideration.
The ‘Aero-Power’ figure shown is the power measured by TOUR in the wind tunnel as required to overcome the aerodynamic drag of the bike and a dummy with moving legs at 45 km/h. For the simulation, we mathematically add the rider’s upper body and scale the drag to the actual race speed.
* Simulation calculations
Based on our own wind tunnel tests, we carry out simulation calculations for the Tour de France tech briefing. How TOUR tests: Aero road bike test in the wind tunnel.
We are investigating which wheels can offer a technical advantage in which situations. The variables we can control in the simulation include wheel weight, rider weight, the inertia of the wheels, the drag coefficient, the rolling resistance coefficient and the efficiency of the drivetrain.
To model ride times, we use realistic power outputs and weights for the riders, combine these with our wind tunnel data, and have the riders race virtually along selected sections of the route, which we extract from the official route data; the derived elevation profiles are key to this. The modelling also includes bends, which we can brake for realistically, and adjustable power profiles for different types of riders. This allows us to distinguish between hill climbs and proper final sprints. Taken together, this makes the simulation very realistic. What we cannot replicate are dynamic handling effects such as the individual behaviour of the wheels on different surfaces.
The journey times calculated for the sections of the route that are decisive for the race highlight the influence of the wheels – provided that the riders always behave in the same way in a given scenario.
Robert Kühnen
Editor