Mercedes always believed that its 2024 Formula 1 car – the W15 – would be quick, and were surprised to have failed to realise its potential at the start of the campaign.
The German manufacturer entered the F1 summer break with a record of three wins from the last four races, with George Russell triumphing in the Austrian Grand Prix and Lewis Hamilton adding victories in Great Britain and Belgium.
The success came after a series of updates, which arrived in conjunction with the Monaco Grand Prix, helped unlock the car’s improved balance characteristics that the team had been chasing.
Trackside Technical Director Andrew Shovlin believes the key to the team’s progress was simply a gradual progression in understanding how to unlock the potential they knew the car had.
“It wasn’t a watershed moment,” he said. “I think we always thought this car, in its day, was fast.”
“But to be able to do that all weekend was a challenge for us in the first part of the year. Now it’s more usable.”
Andrew Shovlin, Trackside Engineering Director, Mercedes-AMG
Photo by: Erik Junius
“This is not just one development. There are many things we have done to try to solve these problems.”
“As I said, we were surprised that we weren’t quicker off the line. We thought we had made a good car and underneath it was a good car. It just had a few issues that we had to fix. Now we are seeing the result of all that hard work.”
Shovlin believes the car’s progress accelerated once Mercedes understood what was needed to make it fast.
“It’s just a feature of the organization working well,” he said. “A lot of the success in F1 is based on learning and ideas.”
“So far, the learning rate has been high this year and the idea generation has been good. Ultimately, this is where all the new parts and new developments come from.”
Mercedes was also helped by a better correlation between wind tunnel and simulation predictions and what happens in the real world.
“The better the models, the better you can develop offline,” he added. “As we said, we have a lot of different models and the correlation is never perfect, but it’s an area where we’ve definitely seen some improvement.”
“The ability to model what the car will do is one of the best ways to develop nowadays, when you don’t have endless amounts of testing in the tunnel or on the track.”
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