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SMART BOATING learning

SMART BOATING learning – which is basically advanced statistics – can find those patterns in a much more systematic and logical way.” “It’s about trying to fill in information where there is none,” says Mauricio. “We use this real-time data to identify causes for failures and then piece this together to get a bigger overall picture.” To maximize input and output and gain that bigger picture, the analysis stretches far beyond what happens on the boat during a single training session, Mauricio explains. “We’re looking beyond the traditional day-to-day performance analysis approach,” he says. “So we crunch not only today’s data, but compare it to what we generated a few months ago, hoping that seeing the trends over time can lead to a more informed decisionmaking process in the development of the boat.” But it’s not as simple as it sounds. “One of the fundamental things that you have to understand about sailing is that unlike car racing, the problem is very ill-defined,” says Mauricio. “The boat’s performance can potentially differ quite dramatically from day to day, even if all the variables you can control on the boat remain the same.” Indeed, these variables force the very best out of the Portsmouth team and the algorithms they use as they work to cover a growing range of scenarios and influences in their analyses; what’s defined as a 10-knot wind whipping across the sea surface can actually vary between seven and 13 knots; underwater currents constantly shift; and things tend to move fast on an America’s Cup Class boat. Combine that with the variables the racing team can control – like switching out different hydrofoils – and it becomes complicated. “If you modify or change a component on the boat, you’re trying to understand the impact of that specific change and remove the background noise,” says Jim. “The sheer wealth of information being generated by the boat’s sensors makes a huge difference. The machine learning techniques we have designed help filter through this morass of information to find and monitor elements THE 35TH AMERICA’S CUP Dates: May 26 to June 27 Location: Bermuda Number of teams: Six (one defender and five challengers) Defending Champion: ORACLE TEAM USA (Skipper: Jimmy Spithill) Boat Class: All America’s Cup boats are subject to specific design rules, the so-called ‘Class Rules’. All teams must adhere to these specific design rules and present their own boats built specially for the race. The Land Rover BAR data team is based in Portsmouth, UK, and has been created to boost performance on the waters. Pictured here are Jim Johnston (left), Richard Hopkirk (below left) and Mauricio Muñoz (below right) “THE FIGHT TO WIN that are constantly changing.” By using these ongoing miniscule THE AMERICA’S tweaks to improve the boat’s CUP IS ON A NEW, design, the team based in Portsmouth and their counterparts MORE ADVANCED, training in Bermuda have, as such, been working in harmony and FRONTIER: BIG DATA” aligning their findings with the common goal of optimizing the R1 Land Rover BAR America’s Cup Class boat as far as possible ahead of the competition. The effort comes from a relatively young team competing against opposition with decades of experience. Still, the Portsmouth crew has been able to benefit from the technologies developed by Jaguar Land Rover derived from car design and research. This helps accelerate the learning curve and, ultimately, progress. Jim adds: “It was Jaguar Land Rover’s experience in managing and analyzing data for our self-learning car technology that helped us create an expert team able to understand and develop algorithms to disseminate the data. This includes aerodynamics, control systems and of course the machine learning power, combined with work streams we developed.” “We’ve got a definitive end goal,” Richard says. “In Bermuda, we have to get everything spot on. Though there’s no way of knowing what other teams are focusing on, the large-scale analysis of performance data is potentially a world first in the racing world and it is our goal to show just how effective this can be. We are constantly looking for new ways of harnessing that all-important data to feed the algorithms and generate new insights.” “It’s all about winning and bringing the cup home,” Jim says. “That’s the key driver.” “Though that won’t be the end of it,” Richard adds. “We’ll be using the insights we are gathering all through this Cup into the next competition, and far beyond.” 44

SMART BOATING 300 CHANNELS TO HELP GATHER REAL-TIME INSIGHTS FROM THE BOAT 175 GIGABYTES OF RAW DATA GATHERED PER SIMULATION PHOTOGRAPHY: HARRY KENNEY-HERBERT (1) 45

 

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