Tech Explained: Formula 1 Tyre Model Development

Tyres are essential components of a race car, as they are the only direct link between the vehicle and the road. The viscoelastic characteristics of tyre compounds create an optimal window of operating conditions under which the driver can extract maximum performance, and race after race has demonstrated that achieving and maintaining this window is a critical differentiating factor between victory and defeat.

All-new 18-inch Pirelli tyres are part of a comprehensive technical regulations overhaul for Formula 1 in 2022. This step-change reverses years’ worth of collected tyre data and cultivated knowledge, and those that can get on top of the new tyres quickly could prevail with an immense competitive advantage.

The teams are well-equipped to tackle this challenge and utilise various tools and methodologies to develop their understanding of tyre performance. Below we will discuss some of these tools and the considerations involved in unlocking tyre comprehension.

Mario Isola, Head of F1 and Car Racing for Pirelli, sat down with Racecar Engineering to provide insight on tyre characterisation from the supplier’s perspective. He stressed the importance of developing models as design tools in an era of simulation-driven development, saying, ‘One of the most important transfers of technology between Formula 1 and road tyres is the ability to virtualise the tyre and accelerate the development process using simulation. Once you know how to work around certain parameters, you can develop a virtual model that works across several applications.’

Tyre modelling can take several different approaches. A theoretical tyre model attempts to capture the physical behaviour of the tyre based on its construction and basic mechanical principles. Although these models capture a high level of detail, they are incredibly complex, computationally expensive, and require comprehensive knowledge of material properties. On the other hand, empirical models use collected data to construct a mathematical relationship or interpolation table. These models can achieve high accuracy without prior knowledge of the tyre design but require a large amount of data and provide no insight into fundamental tyre behaviour. Figure 1 shows a summary of these trade-offs.  

Figure 1: There are several trade-offs to consider when selecting a modelling technique. Source: Hans B. Pacejka

Selecting the suitable tyre model is a matter of compromise and prioritisation. It is helpful to develop several different models, then choose the appropriate one based on the requirements of a specific design task. One industry favourite is the ‘Pacejka Model’ or ‘Magic Tyre Formula’. This mathematical equation uses four primary coefficients to describe the force generation capacity of a tyre.

Figure 2 demonstrates a tyre force curve generated using the Magic Formula, with annotations highlighting the influence of each coefficient. Various versions of the model exist, and the coefficients can be defined as a function of pressure, camber angle, tyre load, temperature, and more based on a designer’s requirements.

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Figure 2: Graphical demonstration of the Magic Tyre Formula

Developing complex tyre models is time-consuming, but thankfully, the teams don’t need to start from scratch. Isola reveals, ‘We provide a finite element model to the teams, as well as a thermo-mechanical model. The technology we use is confidential, so we provide this [thermo-mechanical] model as a black box for them.’ A finite element model discretises the tyre construction into individual elements that are simpler to analyse. Teams stitch these back together to predict the overall behaviour of the tyre as a structure, as shown in Figure 3. These models help evaluate the strength, stiffness, and deflection properties of the tyre.

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Figure 3: The tyre splits into a mesh grid of individual elements for structural or thermal analysis in a finite element model. Source: https://projectchrono.org/

 

Formula 1 tyres attain peak performance within a very narrow temperature band, so it is critical to understand and control heat generation. Thermo-mechanical models can capture these effects and are often combined with other models such as the Magic Formula using a grip correction factor, such as the example in Figure 4.

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Figure 4: This plot demonstrates the importance of maintaining tyre temperatures within the optimal window. Source: A.J. Tremlett & D.J. Limebeer

Any new engineering model that develops must be validated to ensure its outputs are accurate—often using an iterative loop of testing, data processing, and model refinement. Isola explains how Pirelli works with all ten Formula 1 teams to drive the improvement of models and the advancement of knowledge.

‘There are several areas where we work together with Formula 1 teams to anticipate as much as possible before going on track,’ notes Isola. ‘We provide virtual models to the teams, who, in turn, fine-tune their virtual model of the car and come back with suggestions on how to improve the virtual model of the tyre. After several loops, we converge on an accurate performance prediction.’

The active collaboration and data sharing between Formula 1 teams and Pirelli bring mutual benefits to all participants. ‘We effectively have ten different models of the car and ten sources of feedback,’ highlights Isola. ‘This helps us understand if the models are accurate and how well they align. Thanks to in-car telemetry data, we can also fine-tune our internal tests to represent on-track conditions, allowing us to develop our models even further than before.’

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Figure 5: Lewis Hamilton testing a prototype 2022 tyre at Imola. Source: F1

Collecting high-quality, relevant data is vital to achieving good model correlation, and there are various tools used on and off the track to characterise tyre behaviour. The most common indoor testing device is the flat track test, in Figure 6, which simulates the road surface using a rolling belt. A specialised machine applies a prescribed load and orientation to the spinning tyre and measures the resulting forces. This enables engineers to move efficiently through various operating conditions and create a complete characterisation of the tyre.

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Figure 6: The flat track allows engineers to simulate a realistic driving cycle in a lab environment. Source: Calspan

Teams often use a shaker rig test to evaluate the ride performance of the suspension. Here, all four tyres are on platforms that oscillate at prescribed frequencies and amplitudes. The resulting accelerations measured at the wheels and chassis can provide detailed information about the ride and handling performance for a given suspension configuration. Placing pressure-sensitive mats on these platforms allows engineers to measure the pressure distribution across the tyre in real-time, helping ensure full utilisation of the contact patch.

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Figure 7: Pressure-mapping sensor maps can help visualise the distribution of vertical loads across the tyre contact patch. Source: Tekscan

While lab testing allows engineers to control operating conditions and test plans, there is no substitute for on-track data to capture the real-world performance of the tyres. All Formula 1 cars are fitted with a wide range of sensors to measure the temperature distribution, pressure, humidity, and acceleration at each corner.

The more data one can collect and understand, the more information can be applied to modelling and characterising the tyre. Teams are constantly pushing the limits to gain more insights. During 2021 Winter Testing in Bahrain, several technicians were spotted using a mysterious new ‘gun’ (pictured below in Figure 8) to perform measurements on tyres after coming in from the track. This tool can measure the viscoelastic damping characteristics of the rubber, offering greater insight into the evolution of the tyre compound after the heat and wear cycles. Moments like these provide a fascinating glimpse into the ever-escalating arms race essential to success in Formula 1.

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Figure 8: The MegaRide VESevo is a new tool capable of measuring different viscoelastic properties of the tyre. Source: MegaRide

No amount of modelling will be able to predict performance in all operating conditions perfectly. These challenges become especially prevalent when tyre suppliers introduce a new compound or when Formula 1 visits a new track. Despite this, Isola explains, ‘We can employ many strategies to prepare for new scenarios like these. First, we consider the circuit layout, using lap simulation to predict how much energy goes into each tyre for a single lap. We simulate both qualifying and race laps, as each has different conditions such as fuel load, ERS usage and DRS availability.’

‘We also measure the tarmac roughness of each track, considering both macro roughness and microroughness. Using one table [Figure 9], we can summarise all the circuits on the calendar along these two axes.’ By collecting information such as this, engineers can compare with earlier data to find instances with similar conditions and make the best predictions possible.

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Figure 9: This graph illustrates one method of classifying the roughness of a track surface. Source: Michelin

In conclusion, tyres are incredibly complex pieces of engineering, and the behind the scenes work to model and understand them is a time and resource-intensive process. The performance sensitivity of the Pirelli Formula 1 tyres makes this an important challenge for Formula 1 teams to achieve success in the sport. The 2022 season will be an exciting opportunity to shake up the competitive order, as we see which teams will be the fastest at getting on top of an all-new generation of the tyre!