"Big Data" has been all the rage for the last few years. But the sport of Formula 1 racing caught that bug a long time ago, certainly in the days predating that buzzword. In the past, we've taken a look at how teams like Williams Martini Racing, Renault Sport Formula One, and Caterham F1 (RIP) have handled collecting and crunching their terabytes. Today, it's Red Bull Racing's turn.
"I've worked for the team for 13 years now, and we've been doing this for ages. The complexity of what we measure and sophistication of the analytics continues to improve, but we've been doing big data for a long time," explained Matt Cadieux, Red Bull Racing's chief information officer. The data in question is collected by myriad sensors all over the team's race cars, roughly adding up to a terabyte each race weekend (500GB for each of the two cars).
"But if you look at all the other data we use—video, audio, number crunching to run through various simulations—it's a huge multiplication factor on top of that," he told Ars. Cadieux wouldn't give us an exact number for that data volume over a race weekend, lest that information prove too useful to the team's rivals in the paddock, but company-wide the team manages 8PB of data. Cadieux reckoned that 95 percent of that was related to car design and car performance—think CAD (computer-aided design) and CFD (computational fluid dynamics), but also strategy simulations and historical telemetry data from previous seasons. "We have a very data-hungry business," he said.
That data is fed back to the garage and used to inform the team's strategy during races. But it also gets fed back to the engineering department in the English town of Milton Keynes, where it's used to refine the cars throughout the season and inform the development of next year's machines.
In recent years, the FIA—the group that writes F1's rules—has been on a cost-saving kick. (It's well-meant, but the FIA ignores the fact that each team will spend every penny of its yearly budget in search of better performance at the track.) That has meant restrictions on the use of wind tunnels (which are limited to 60-percent scale) but also CFD modeling—with a maximum of 30 hours of wind tunnel time or 30 teraflops of CFD processing in a week (or a combination of both so long as the combined mix of hours and teraflops only adds up to 30). But teams are also now limited in the number of personnel they can bring to each race weekend.
Keeping HQ in the loop
Getty Images for Red Bull
Like other teams we've spoken to, Red Bull supplements its personnel with data links and enterprise software to loop in engineers back at HQ. "Sixty people have operational roles at the race track, and we supplement that with a large number here in the UK. We have an operations room—branded by AT&T—that holds more than 40 people at full capacity," Cadieux told us.
For each race weekend and test session, the team sets up an MPLS (Multi-Protocol Label Switching) connection between the track and Milton Keynes that provides the operations room real-time feeds for video, audio, and voice chat. So if the team is testing a new part from the design office—a revised front wing, for example—that part can be debugged with the expert from the design team in the ops room. "He can see how the car is operating in real-time and participate in engineering debriefs that take place after every practice session or qualifying session during a race weekend," he said.
That connection back to home base also means that if the team is nursing a particular issue on a car during the race, it can have that system's expert in the loop. "They can pretty much act as if they were at the race track themselves because of the real-time connectivity," Cadieux said. Other members of the team play a more proactive role by monitoring competitors to identify weaknesses or strengths that could inform Red Bull's strategy. The platform the team uses for this may be familiar to those of you who spend your work days in remote meetings: Citrix.
"What Citrix allows us to do is look at applications that are very graphically intensive or that have big data there that you couldn't normally interact with at a remote location," Cadieux explained. That includes telemetry from the cars as well as post-processing and analysis of that car data, a service provided by servers that the team brings to each race but which is then also piped back to home base.
And data flows in the other direction, too. Since real-world testing is now highly restricted by the rulebook, the Friday practice sessions of an F1 race weekend are used by Red Bull to validate all the simulations it runs during the week. "Guys can upload CAD big assemblies, rotate them, cut big sections through, and suggest changes for the future. With CFD, they can open up the animations and stream videos with more or less the same performance as if they were back at HQ," he told us.
Got to keep it safe
Getty Images for Red Bull
Obviously, all of this data needs to be well-protected. Cadieux pointed out that F1 is an incestuous industry, with most of the grid operating from bases within 50 miles of each other in the UK's so-called "carbon-fiber triangle." Teams have relatively flat organizational structures, and Cadieux pointed out that an easy way to get promoted is to go to another team, data in hand. (Most famously, a Ferrari engineer was caught passing documents to McLaren in 2007, but it has happened before—in 2004, Toyota was accused of making use of stolen Ferrari design plans.)
But Cadieux said that the need for data-access controls has to be balanced by a necessity not to slow the pace at which the team works or to make the work environment too unpleasant. External threats exist, too, both from other teams but also third parties looking for a ransomware payout. "Because we're high-profile, we tend to be a target," he said.
But perhaps the most fascinating aspect of Red Bull's informatics work is one that Cadieux couldn't tell us much about: machine learning and AI. "We're exploring them to make better simulations and better decisions at the races," he said. "It's early days; we're working with several partners right now, but it's the very beginning of a journey. But I've been in IT for almost 30 years, and there's more pace of change now than ever before. It's both scary and a huge opportunity."
Read the full article here by Ars Technica
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