Auto racing isn’t always a display of precision engineering.
“When we built the car 20 years ago, we discovered that part of the body was going to have a wishbone. When we drilled the hole, we found the axle was in the wrong place,” Williams CEO James said. Bolles spoke on CNBC’s “Inside Track.”
However, the modern F1 season is packed with 24 races and there are limits to how much money can be spent competing, so even the smallest mistake can cost you the title. To get around them, teams are increasingly relying on digital tools. The car design is created virtually before being uploaded to a program that simulates the airflow around it. Other software systems, on the other hand, stress test each nut and bolt in a variety of weather conditions to ensure the design can withstand a full season.
“Once we think we’ve found a high-performing design, we build a 60% version and put it in the wind tunnel,” Dan Keyworth, McLaren’s director of business technology, told CNBC. It added that sensors have been installed. This allows software engineers to simulate the car’s performance in different scenarios.
Unlike fully assembled cars, which cannot be flown around the world for testing, these “digital twins” allow teams to model the conditions required for a real car to perform. can be converted into You can also adapt the car to each driver. Red Bull driver Max Verstappen said: “There’s a lot to analyze and work on before we get to the race track, including spending time in the simulator.” “As soon as the car hits the ground, we try to optimize the car.”
Race winner Lewis Hamilton (UK) with Mercedes and 2nd place Max Verstappen (Netherlands) celebrate at the parc fermé of the British F1 Grand Prix held at Silverstone Circuit in Northampton, England on July 7, 2024. Oracle Red Bull Racing.
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Doing this with a digital twin allows teams to develop strategies for different circuits. Cars designed for high-speed circuits like Britain’s Silverstone lack the grip and downforce needed in places like Monaco, where drivers need to use the full width of the track. With 24 races on the schedule and only a few days between them, predicting how the track will perform is important to ensure engineers are ready to make the necessary changes.
It also allows you to adapt your strategy in real time. On race day, each team is allowed up to 60 operational staff to be on the track, but each team remains in contact with analysts at headquarters. “Data is passed directly from the racetrack to the mission control room in real time,” said Ben Waterhouse, Red Bull’s head of performance engineering. “Everyone there looks at their computers and feeds back recommendations to the race track engineers that can be applied to the race car.”
These recommendations became more accurate as the team moved from stopwatches and hand-held engine temperature gauges to onboard sensors that generate 1.1 million data points per second. “AI and machine learning are very powerful in this space because they can react much faster than humans,” Vowles said. “But there are also areas where human heuristics are needed. For example, if there is a collision, a human can quickly look at it and determine if there are any red flags.”
With cost caps limiting the budget to $135 million per year, automating repetitive tasks is critical for teams to use resources efficiently. Engineers are already handing over the jobs of predicting inventory costs and organizing transport to the machines, and analysts are using pattern recognition algorithms to determine when other teams are likely to pit during a race. I am.
The pit lane is crowded during the F1 Crypto.com Miami Grand Prix 2024 held in Miami, USA on May 1, 2024. (Photo: Alessio Morgese/NurPhoto via Getty Images)
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If it works well, regulation will allow teams to find new ways to use these technologies.
“In the past, you could take pretty big risks in terms of whether or not you would get a performance benefit from doing this,” Waterhouse said. “However, the latest regulations have forced us to look at innovation differently, becoming very cost-conscious and more focused on efficiency.”
As AI and other technologies advance, finding ways to implement them will define the F1 championship. Technology companies have rushed to lend a hand to the team, signing numerous new sponsorship deals in the past two years alone. Unlike the tobacco companies that sponsored previous generations of F1 teams, technology companies are getting in on the game. Cloud computing giant Oracle is reportedly paying $300 million to be Red Bull’s title sponsor, as well as providing access to cloud infrastructure and AI expertise. Google’s partnership with McLaren is built on similar principles, offering technology and expertise in exchange for a global platform that allows businesses to test against their competitors.
This is a new era, not just for F1, but for the sport as well. Your biggest competitor is no longer the best athlete or the best strategist, but the most innovative person.
