Ferrari, Project 676: why F1 car predictability is hindered by external conditions. The lack of predictability is one of the causes of the "decline" of the F1-75 in the second half of 2022.

The final simulation step available to Ferrari technicians, and especially to the drivers, involves the driving simulator. While Maranello’s wind tunnel has seen its years, the new “spider” Ferrari, built in collaboration with Dynisma, is a flagship for the development and preparation of the race car. The new setup abandons the old icosahedral shape, opting for a monocoque fixed on 6 hydraulic jacks and nestled inside a 360° screen.

This new tool, in the possession of drivers and technicians of the Modena-based team since the end of 2021, is the link between what the track conveys and the virtual simulations we discussed earlier. The installed computing power can perfectly replicate all the data studied in simulations and ensures latency, the time between when the driver provides input and the system’s response, of less than one hundredth of a second.

Ferrari has, therefore, succeeded in improving its driving simulation system, achieving good correlation with data derived from the track, showing setups that are almost always up to par in the last races of the season. Finding good correlation between the computer and reality has been a significant challenge for the Italian team during the past regulatory era, and the reasons were the boundary conditions.

In the simulation phase or in the wind tunnel, the airflow and external temperature interacting with the car can be controlled and kept constant. In this way, at every slight change in setup, performance variations of one hundredth of a second can be highlighted. If you want to try the same modification on the track, you will find a discrepancy on the order of one-tenth of a second, and the reasons for this difference are multiple.

Firstly, the driver is not a machine; they can get tired and continuously change their interaction with the car. Secondly, track and air temperatures constantly change, as does wind speed or humidity. Even a tire, from the moment it emerges unscathed from the tire warmers until it wears during laps, is a performance variation element.

Pick-up, graining, blistering, and all the streaks that form on the compound due to degradation involve a variation in the detachment point of the airflow from the compounds, continuously changing the aerodynamic map. In conclusion, we can say that the most significant simulation step on a race car is to make it predictable. Drivers, to perform at their best, need a car that behaves consistently and is perfectly balanced.

A predictable race car allows for taking a corner or a turn in the same way lap after lap or going on the throttle at the same moment consistently. The driver has an enormous ability to encode what they did in one lap and repeat it the next lap. If we consider two identical corners in terms of angle and radius, one to the right and one to the left, you cannot have a speed difference of 10/15 km/h; this implies that the car is unbalanced in one direction.

In such a situation, the driver will choose to navigate both corners at the minimum speed between the two. An unpredictable race car also implies continuous corrections, loss of confidence for the driver, mistakes, and lap times that keep rising. The lack of predictability is one of the causes of the “decline” of the F1-75 in the second half of the 2022 season. The same issue resurfaced in 2023, and the changes already deliberated in Maranello for Project 676 converge in an attempt to put a predictable car in the hands of the drivers.

They are seeking a race car capable of responding to and following their inputs without unexpected behaviors. The simulations carried out during the winter and the positive sensations emerging from the Ferrari Racing Division in recent weeks await confirmation in Bahrain. A more forgiving car on the tires and more predictable entry into turns is expected, capable of alleviating the issues of tire degradation and overheating in long runs, weaknesses of the red cars for several Formula One seasons.

Source: Alessandro Arcari and Leonardo Pasqual for FUnoanalisitecnica