The 2022 F1 season is key to the emergence of a new car concept adapted to the latest technical regulations. Here begins a series of articles in which we will analyse different aspects of the F1 cars of the 2022 season, comparing them with cars from previous seasons to understand the potential of the new design. As a result of the many overtaking events in the 2022 F1 Grand Prix season so far, we will use this to analyse the overtaking capacity of the current cars, comparing it with the capabilities of the 2018 season car. To do this, we will explore the velocity field around the vehicle, the wake created at the rear, and the turbulence or vortices created. The principles around which we will measure overtaking capacity are:
- Lower air speed at the rear of the leading car: less ease of overtaking.
- More air turbulence: less ease of overtaking.
CFD simulations basic parameters:
- Static simulation
- Rotating wheels (tires + rims) with brakes heat transfer
- Moving ground-track
- Radiators in sidepods: loss of energy and heat transfer
- Exhaust: gas and heat transfer
- Engine air admission
- Heat transfer in the engine block
- 20 layers in the boundary layer
- Size of min mesh = 1 mm
- Size of main mesh = 1 cm
- 10 m length special mesh behind the car with an angled tail of 30º – 35º
- Speed: 55 m/s speed
- Temperature: 15 ºC and pressure: 1 Atmosphere
Below are the geometries used in this article. The setup of both cars has a pitch or rake of 1° (Figure 1-6 – F1 of 2018, Figure 7-12 – F1 of 2022):
Velocity field and turbulence
Figure 13 shows the range of colours used in the air velocity map for the CFD simulations and their meaning.Figure 13 – F1 2018 season; velocity field; colour map of velocity: blue = low velocity, red = high velocity. The section (parallel to the track ground) chosen to represent the velocity field is 5mm above the track surface (Figure 14 for 2018 vs Figure 15 for 2022).
The blue zone corresponds to a low-speed zone; when the following car encounters this air mass, its potential reduces because the slower air acts as a brake. This makes overtaking more difficult. The larger the blue zone behind the car, the more difficult it will be to overtake. Moreover, the vehicle can also maintain this low-speed blue zone rearward and the longer it remains, the more difficult it is to overtake. On the other hand, one of the existing measurables to quantify the turbulence in a particular area is the Turbulent Kinetic Energy (TKE). This value is nothing more than the sum of the squares of the velocity components. It gives a quantification of the current turbulence. As before, the blue colour indicates low turbulence, while the red colour indicates high turbulence – see Figure 16:
Let’s look at a comparison of the two seasons concerning TKE. Comparison of TKE in section to 5mm above track 2018/2022: We can see that in the 2018 season, the wake zone is more significant. This means there is more turbulence compared to the 2022 season car. As such, the ease of overtaking is greater in 2022.
Velocity field and turbulence in the wake: The wake velocity can also illustrate the ease of overtaking.
The low-speed zone (dark blue) is more significant and denser in the case of the 2018-season car. The low-speed zone also concentrates closer to the rear in the 2018 car. This makes, as stated above, overtaking more difficult in 2018.
As before, we can also represent the TKE turbulence field by comparing TKE in the symmetry plane of 2018 vs 2022 cars.
The area where turbulence exists is more significant in 2018, so it is more difficult to overtake.
Vortices rear zone
Continuing with the wake area, the vortices generated by the rear wing, which are part of the wake, can be seen below:
Comparison front view 2018 / 2022:
This comparison of the vortices created by the rear area of the car confirms the 2018 season car throws the airflow (wake) at a lower height, making overtaking more difficult.
About the Authors.
- Timoteo Briet – Aerodynamic and CFD engineer, Mathematician, Cosmologist, Online Course CFD, Aero and CFD professor. firstname.lastname@example.org Twitter: @timoteobriet https://www.linkedin.com/in/timoteobriet/
- Nacho Suárez – PhD Electronics Engineer, Vehicle Dynamics, Virtual 7-post Rig, Simulation, Autonomous Vehicles, Control, Racing, Embedded Systems; UNEX University. email@example.com https://www.linkedin.com/in/nachosuarezphd/
- Enrique Scalabroni – Formerly at Dallara Automobilli, Ferrari F1 Chassis Technical Director, Williams F1 and Lotus F1, among many others firstname.lastname@example.org – Twitter: @ScalabroniE