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complete test (meaning installing the bike in the tunnel and then
testing it from 0 degrees of yaw (straight headwind) out to approximately 30 degrees; the maximum allowed by the tunnel. The
goal was to establish a standard measurement protocol—an “open
source testing” process—that everyone in the industry could use
and abide by, or to determine at least whether such a standard was
even feasible. The short answer is that it is not. Cote learned that
because of nuances in tunnel design, “noise” in the testing process,
and a host of other factors, results from various wind tunnels were
The engineering side of this conundrum explains, to a large
degree, the shift within the industry to CFD, which uses computers to run virtual wind tunnel tests—which are obviously incredibly repeatable since they are virtual—on bikes, wheels, etc. This,
of course, has opened its own can of worms, as the fascinating
multicolored pictures generated by CFD are a marketer’s dream.
Furthermore, CFD is incredibly reliant on the quality of the model.
While wind-tunnel testing is also dependent on the quality of the
tunnel, no cycling company can afford to build its own tunnel (
unlike Formula 1), and so there is at least some check on the quality
of the data a tunnel produces.
But with CFD, Poertner emphasized that the quality of the data
is massively dependent on the initial assumptions that you make
about the model. If you start with a bad assumption, CFD can very
often reinforce that notion. As with most computer programs, garbage in gets you garbage out, but it doesn’t always flag it as garbage. Thankfully, the wind tunnel will tell you if you put garbage
in, but as Cote saw, sometimes the tunnel itself adds a fair bit of
garbage to the process, meaning you might get garbage out even
if you put good stuff in. None of this is particularly problematic
to engineers; in fact, some might argue that this is fundamentally
what science and engineering are all about. But it does present
problems for marketers, who want to sell the products that are getting built in order to actually fund the aforementioned science and
engineering. And it presents problems to consumers who, at the
end of the day, really only want to know what will make them faster.
In science and engineering, explaining what you do not know is
generally the most important part of a technical document. But explaining what you don’t know doesn’t sell a product. And, ultimately, sales are necessary. Which brings up yet another conundrum:
cost. I’ve never seen cost discussed as a factor on any white paper.
Cost is a very real factor, but often goes unmentioned because it’s
even harder to pin a dollar figure on the benefit of a given feature
than it is to pin an aerodynamic benefit on that feature.
In addition to the dollar cost associated with development of
new technology, there is also the time cost, which has to be divided between new product development and explaining those
new products internally as well as abroad. All the time that Cote or
Poertner spend answering questions is time that is not spent elsewhere. It’s further complicated by the fact that marketing departments often want to take advantage of the engineering advances,
but lack the true understanding to explain the advantages to consumers. Cote said it’s not fair to use the graphs without explanation,
but the graphs are the easy takeaway, and the explanation often
gets left behind. Poertner emphasized that there are vastly more