Overview
A competitive Formula racing team needed to maximize aerodynamic performance for the upcoming season. The target was aggressive: 15% more downforce without increasing drag, to achieve faster cornering speeds while maintaining straight-line performance.
The Challenge
The team faced significant constraints that made this a complex optimization problem:
- •Tight timeline before season start with limited development cycles
- •Complex interactions between front wing, floor, and rear wing aerodynamics
- •Strict regulations limiting geometric modifications
- •Variable conditions requiring performance across different ride heights and yaw angles
- •Limited wind tunnel time available for validation
"Every tenth of a second matters in racing. We needed simulation-driven development to maximize our limited physical testing."
Our Approach
1. CFD Methodology Validation
Established high-fidelity simulation methodology:
- •Correlated CFD results with existing wind tunnel data
- •Achieved <2% deviation from experimental results
- •Validated across multiple configurations
2. Parametric Studies
Conducted systematic investigations of:
- •Front wing endplate geometry variations
- •Floor edge detail modifications
- •Diffuser strake configurations
- •Rear wing element positioning
3. Adjoint Optimization
Used sensitivity analysis to:
- •Identify highest-impact surface regions
- •Optimize within regulatory constraints
- •Reduce design iterations by 60%
4. Response Surface Modeling
Developed performance maps showing:
- •Sensitivity to ride height changes
- •Yaw angle performance
- •Setup optimization guidelines
Technical Details
| Simulation Aspect | Specification |
|---|---|
| Mesh Size | 120+ million cells |
| Turbulence Model | k-ω SST with DES |
| Software | ANSYS Fluent, Star-CCM+ |
| Optimization | HEEDS, Python scripting |
| Validation | Wind tunnel correlation |
Results
The CFD-driven development exceeded targets:
- •18% increase in total downforce (exceeded 15% target)
- •3% reduction in drag coefficient
- •4 km/h improvement in top speed
- •2.1 seconds faster lap time at benchmark circuit
- •Optimized balance for improved driver confidence
Aerodynamic Balance
Front Downforce: +16%
Rear Downforce: +20%
Drag Reduction: -3%
L/D Improvement: +22%
Key Takeaways
- •High-fidelity CFD enables rapid exploration of design space
- •Adjoint optimization dramatically reduces development cycles
- •Validated methodology is essential for confident decision-making
- •Understanding sensitivities helps optimize for real-world conditions
- •Simulation-driven development maximizes limited physical testing resources
