AeroTread Dynamics

A predictive F1 strategy tool that models how aerodynamic damage causes specific tyre failures, turning reactive analysis into proactive, race-winning intelligence.

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About the Project

Inspiration

Our inspiration stemmed from a fundamental yet often overlooked challenge in Formula 1: the intricate relationship between minor aerodynamic damage and catastrophic tyre degradation. A notable case was Lance Stroll’s 2020 Turkish Grand Prix, where post-race analysis revealed that hidden front-wing damage caused severe front-tyre graining, costing him a potential win.

This exposed a critical gap in race strategy tools. Engineers can observe the symptoms — such as tyre graining via infrared telemetry — but often lack an immediate, data-driven diagnosis of the cause. These issues are rarely triggered by dramatic events; they result from cascading failures, often initiated by something as subtle as running over a kerb.

Our goal was to bridge this gap with a predictive intelligence system that connects aerodynamic damage to tyre degradation in real-time. We envisioned a tool that could translate “Front Wing Endplate Damaged” into:

“High probability of front-tyre graining in 3–5 laps, projected stint length reduced by 8 laps.”

What It Does

The Aero-Tread Dynamics System (ATDS) is a real-time predictive and visualization framework that models the causal link between aerodynamic imbalance and tyre degradation.

ATDS converts a qualitative damage event into quantitative aerodynamic penalties and uses physical equations to simulate how this affects the car’s balance, slip angles, and ultimately tyre wear. The system predicts when and how the tyres will degrade — be it graining, blistering, or overheating — and displays these results through an intuitive engineer dashboard.

How We Built It

Module 1: The Intelligence Core

This is the brain of ATDS, powered by the Aerodynamic Damage Impact Matrix (ADIM) — a physics-based lookup system populated with CFD-derived data. For each damage type (e.g., “Floor Edge Damage”), ADIM outputs penalties to aerodynamic coefficients:

$$ C_l' = C_l - \Delta C_l, \quad C_d' = C_d + \Delta C_d $$

It also computes the new Centre of Pressure (CoP), given by:

$$ CoP_{new} = \frac{C_{l_{rear}} - C_{l_{front}}}{C_{l_{total}}} $$

This value determines how the car’s aerodynamic balance shifts after damage.

Module 2: The Predictive Engine

This is the simulation heart of ATDS. Using CoP data, it models tyre slip and heat generation. A rearward CoP shift increases the front slip angle ( \alpha_f ), leading to higher frictional energy:

$$ Q = \mu \cdot F_n \cdot v_{slip} $$

The tyre surface temperature rise over time is modeled as:

$$ \frac{dT}{dt} = \frac{Q_{in} - Q_{out}}{m \cdot c_p} $$

When the thermal gradient between surface and core temperature exceeds a threshold ( \Delta T_{crit} ):

$$ \Delta T = T_{surface} - T_{core} > \Delta T_{crit} $$

the Graining Index begins to increase non-linearly:

$$ G_i(t + \Delta t) = G_i(t) + \beta \cdot (\Delta T - \Delta T_{crit}) $$

This governs the tyre’s degradation rate and projected stint life.

Module 3: The Visualization Layer

This module translates physics into intuitive visuals:

  • A 3D model of the car displays damaged aero zones and live CoP shifts.
  • The ChromaTread dashboard shows tyre temperature and degradation via heatmaps.
  • A predictive analytics panel overlays the post-damage degradation curve:

$$ D_{post}(t) > D_{base}(t) $$

making stint-shortening effects immediately visible to race strategists.

Challenges We Faced

  1. Quantifying qualitative damage: Translating visual damage (e.g., a chipped floor) into accurate ( \Delta C_l ) and ( \Delta C_d ) values required deep research into open-source CFD datasets.
  2. Balancing complexity and speed: Tyre thermodynamics involve viscoelastic and multi-layer heat transfer. We simplified it to essential parameters to achieve real-time simulation performance.
  3. Integrating aero and tyre physics: Linking aerodynamic imbalance with tyre thermomechanics was complex. Using CoP shifts as the coupling bridge created a robust integration between both models.

Accomplishments We’re Proud Of

  • Successfully modeled the full causal chain between aerodynamic and tyre behavior:

$$ \text{Damage} \Rightarrow \text{Aero Imbalance} \Rightarrow \text{Increased Slip} \Rightarrow \text{Heat Build-up} \Rightarrow \text{Degradation} $$

  • Created the Aerodynamic Damage Impact Matrix (ADIM), encoding CFD-based aerodynamic penalties (e.g., a 17.7% downforce reduction for a front-wing endplate loss).
  • Enabled predictive analytics, allowing ATDS to project tyre degradation curves and recommend optimal pit windows.

What We Learned

This project deepened our understanding of the physics connecting aerodynamic state to tyre health. The key insight is that tyre degradation is not simply a result of downforce loss, but a shift in aerodynamic balance, expressed through the Centre of Pressure (CoP).

The mechanism can be summarized as:

  1. Damage causes imbalance: Loss of front downforce shifts ( CoP ) rearward:

$$ CoP_{shift} = CoP_{new} - CoP_{base} < 0 $$

  1. Imbalance forces sliding: The driver compensates by increasing steering angle, raising the front slip angle ( \alpha_f ).

  2. Sliding generates heat: Frictional energy increases tyre surface temperature:

$$ Q = \mu \cdot F_n \cdot v_{slip} $$

When ( \Delta T = T_{surface} - T_{core} > \Delta T_{crit} ), graining begins.

This understanding allowed us to model and predict specific tyre failure modes purely from aerodynamic data.

What’s Next for ATDS

  1. Expand the ADIM Library: Add asymmetric damage types (e.g., diffuser stall) and estimate penalties using machine learning trained on CFD data.
  2. Live Simulator Integration: Connect ATDS with telemetry APIs from F1 simulators for real-time validation.
  3. Machine Learning Refinement: Use historical race data to learn the nonlinear relationships between ( CoP ) shifts, driver inputs, and tyre wear rates.
  4. Driver Feedback Interface: Implement real-time guidance (e.g., smoother steering or brake bias adjustments) to minimize predicted wear.
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