A Formula 1 Car CFD simulation is the most important tool that modern F1 teams use to make their cars faster. In Formula 1, the race is won and lost in the corners, and the speed a car can carry through a corner is controlled almost entirely by aerodynamics. An Aerodynamic CFD Study of a Formula 1 Car allows engineers to see this invisible force. The goal is simple but very difficult: create as much downforce as possible while creating as little drag as possible. Downforce is a vertical force that pushes the car down onto the track, giving the tires massive grip. Drag is the resistance of the air that tries to slow the car down. A Formula 1 Car Fluent simulation using powerful software like ANSYS Fluent is the key to finding the perfect balance.

An F1 car CFD study is a virtual wind tunnel that is often faster, cheaper, and provides much more detail than a real one. Engineers can test hundreds of different designs for wings, the underbody, and other small parts on the computer to see how they affect the airflow. A good F1 car fluent simulation can accurately predict complex physics like ground effect, where the bottom of the car works like an upside-down wing to suck the car to the track. It can also capture the very messy, turbulent air that comes off the rotating wheels, which can ruin the performance of other parts of the car if it is not managed correctly. Every single surface on an F1 car is shaped to control the air. This detailed CFD analysis is how teams find the small advantages, measured in thousandths of a second, that lead to victory on the track.

• Reference [1]: Guerrero, Alex, and Robert Castilla. “Aerodynamic study of the wake effects on a formula 1 car.” Energies19 (2020): 5183.

Figure 1: Ferrari SF70H, a 2017 spec. car

Simulation process: Building the F1 Car geometry and Race Environment

The simulation process for this Formula 1 Car CFD study began with the most difficult step: creating a high-quality mesh for the extremely complex F1 car geometry. A mesh is a grid of small cells that fills the space around the car, and the computer solves the airflow equations in each cell. The final mesh for this car contained 15,288,842 poly-hexcore cells. This huge number of cells is necessary because every single part of an F1 car—the multi-element front and rear wings, the sidepods, the diffuser, and the wheel assemblies—is aerodynamically important and needs to be covered in a very fine grid to get accurate results.

With the mesh complete, the virtual race environment was set up inside ANSYS Fluent to match realistic conditions. The air was given a speed of 50 m/s (180 km/h) at the inlet, which is a typical speed where aerodynamic forces are very important. To correctly simulate the physics, three crucial moving parts were defined. First, the ground was set as a moving wall also traveling at 50 m/s. This is essential to correctly simulate ground effect, the powerful suction created under the car. Second, the four wheels were also set as moving walls, but with a rotational speed of 151 rad/s. This is critical because rotating wheels create a large amount of drag and a very messy, turbulent wake that has a huge effect on the car’s total performance. The car body itself was set as a stationary, no-slip wall. The main goal of the simulation was to run the calculation until the two most important forces, lift (downforce) and drag, stopped changing, which would tell us the final, stable aerodynamic performance of the car.

Figure 2: Mesh generation setup displaying the polyhedral mesh around the F1 car with fine cell resolution near surfaces

Post-processing: CFD Aerodynamic analysis of F1 car

The simulation is complete, and the data is in. We will now conduct an engineering debrief, like a team of race engineers, to understand the car’s performance. We will start with the main numbers, then use the contours as evidence to investigate where the performance comes from—both the good and the bad. The first step is to look at the main results from the force monitors, which tell us the overall story.

The verdict from this data is immediate and clear. The car is generating 459.6 Newtons of downforce (the negative sign in the lift force means the force is pushing downwards, which is what we want). However, this comes at the cost of 998.6 Newtons of drag. This gives a downforce-to-drag ratio of only 0.46. From an engineering standpoint, this is a very poor efficiency ratio for a Formula 1 car, which would typically aim for a value between 2.5 and 4.0. Our job now is to use the other contours to understand why.

To understand where the 459.6 N of downforce is coming from, we look at the pressure contour in Figure 4. Downforce is created by low pressure, or suction, pulling the car down. This contour clearly shows large areas of dark blue on the car’s surfaces, which represent strong low-pressure zones (-1500 to -2500 Pascals). We can see this suction in two main places:

1. On the Wings: The top surfaces of the front and rear wings are blue, which is expected. As air accelerates over their curved shape, the pressure drops, creating downforce.
2. Under the Car: Most importantly, there is a very large blue region on the car’s underbody. This is the powerful ground effect at work. The moving ground and the car’s floor create a narrow channel that squeezes the air, making it speed up dramatically. The velocity streamlines in Figure 5 shows this acceleration. This high-speed air creates a massive low-pressure zone that sucks the car to the ground. This is the biggest source of downforce on a modern F1 car.

Figure 3: Velocity magnitude contour displaying flow acceleration over the F1 car with high-speed

Table 1: Aerodynamic Performance Results from F1 Car CFD Analysis

To understand why the drag force is so high at nearly 1000 N, we again look at the pressure contour (Figure 4), but this time we look for high pressure. Drag is caused by high pressure pushing against the front of the car and by the messy, low-energy wake behind the car. The evidence is clear:

1. Pressure Drag: The contour shows large areas of bright red on the front-facing surfaces, representing high pressure (1500 to 2450 Pascals). This is where the air crashes into the car and stops. We see these red zones on the nose, the front of the sidepods, and especially on the front faces of the four wheels. The wheels act like big, blunt walls hitting the air, which creates a huge amount of pressure drag.
2. Wake Drag: The velocity contour in Figure 3 shows a very large, blue, low-velocity region behind the car. This is the wake. A big, messy wake is a sign of high drag because it means the air has separated from the car’s body, creating a low-pressure area at the back that pulls the car backward. The streamlines in Figure 5 show that the rotating wheels are a major cause of this messy wake. They throw off very chaotic, turbulent air that damages the performance of the rear wing and diffuser and contributes significantly to the total drag.

Figure 4: Pressure distribution on F1 car surfaces from CFD simulation showing high-pressure stagnation zones

Figure 5: Velocity streamlines from Formula 1 aerodynamic CFD analysis in Ansys Fluent

The most important achievement of this simulation is that it has not only quantified the car’s poor aerodynamic efficiency (a ratio of 0.46) but has also pinpointed exactly where the problems are.

For a Formula 1 designer, this simulation is like a perfect diagnostic report. It tells them that while the car’s concept for generating downforce from the underbody is working, it is being severely handicapped by the massive drag created primarily by the wheels. Based on this data, the design team would immediately:

1. Focus on Managing the Wheel Wakes: They would use this simulation to design and test new “bargeboards” and other small wings next to the wheels. The goal of these parts is to push the turbulent air from the wheels outwards, away from the sensitive underbody of the car.
2. Optimize Wing and Body Shapes: They would make small changes to the front wing and nose cone to try and reduce the size of the red high-pressure zone, which would directly lower the drag.
3. Run More Simulations: They would use this validated simulation as a baseline. They could now test dozens of new ideas on the computer to see if they can increase the downforce while decreasing the drag, aiming to get that critical efficiency ratio up into the competitive 2.5-4.0 range. This virtual testing process is what allows F1 teams to improve their cars so quickly throughout a season.