Abstract

This article explores an integrated approach combining high-fidelity Computational Fluid Dynamics (CFD) data with modern machine learning techniques to predict Formula 1 front wing aerodynamic performance. By training gradient boosting models (XGBoost) on simulation-derived data, we demonstrate how aerodynamic coefficients can be accurately predicted for novel wing configurations without running computationally expensive simulations. The results show excellent predictive performance for drag coefficient estimation and promising outcomes for lift coefficient prediction, potentially accelerating the design iteration process in Formula 1 aerodynamic development.

1. Introduction

Formula 1 racing is an arena where aerodynamics is paramount. Not only does the front wing generate crucial downforce, but it also manipulates airflow to optimize performance and balance the car. In recent years, advanced Computational Fluid Dynamics (CFD) simulations have provided detailed insights into the aerodynamic behavior of various wing configurations. However, running these simulations for every possible design permutation can be computationally expensive and time-consuming.

This article explores an integrated approach that combines high-fidelity CFD data with modern machine learning techniques. We detail how a gradient boosting model (XGBoost) is trained on simulation-derived data to predict lift and drag coefficients. The goal is to accelerate design screening and optimization without sacrificing accuracy.

2. Aerodynamic Analysis of Front Wings in F1

2.1 Overview of CFD Simulations

The analysis provides an extensive examination of single-, double-, and triple-element front wings using ANSYS Fluent. The study examined:

Boundary Conditions and Domain Setup:
A uniform inflow velocity of 30 m/s, pressure outlets, and no-slip conditions on the wing surfaces were used.

Key Parameters:

• Angle of Attack (AoA): Simulations conducted at 0°, 5°, and 9° for different elements.
• Ground Clearance: Ride heights of 30 mm and 40 mm were considered to capture ground effect.
• Mesh Quality and Turbulence Models: Both structured (with inflation layers) and unstructured polyhedral meshes were employed. The conventional k–ω SST model was mainly used to capture turbulent effects accurately.

2.2 Simulation Results

The CFD results revealed how the aerodynamics of the wing vary with configuration. For example:

• Single Element: A baseline configuration where an inverted airfoil generates downforce via Bernoulli's principle.
• Double Element: Flap interactions were observed, with notable increases in downforce at specific angles.
• Triple Element: More complex interactions were documented, including a sinusoidal variation in drag with changing AoA.

Numerical values of the aerodynamic coefficients were extracted for each simulation case. An example dataset entry is:

Case: Double-element wing at 0° AoA with 30 mm ground clearance
CL: 1.405, CD: 0.1674

This data was structured and used to train the machine learning model.

3. Integrating CFD Data with Machine Learning

To effectively predict aerodynamic performance, CFD simulation data was structured into a dataset featuring:

3.1 Enhanced Dataset Example

The following table summarizes the key simulation data extracted from the dissertation and related documentation:

AirfoilType	AoA1	AoA2	AoA3	Ground Clearance (mm)	Velocity (m/s)	MeshType	CL	CD
Single	0	0	0	30	30	Structured	1.079	0.1475
Double	0	15.5	0	30	30	Structured	1.405	0.1674
Double	5	10.5	0	30	30	Structured	1.380	0.1650
Double	9	10.5	0	30	30	Structured	1.200	0.1500
Triple	0	10.5	10.5	30	30	Unstructured	1.350	0.1600
Triple	5	10.5	10.5	30	30	Unstructured	1.420	0.1680
Triple	9	10.5	10.5	30	30	Unstructured	1.300	0.1620
Double	0	15.5	0	40	30	Structured	1.250	0.1400
Triple	0	10.5	10.5	40	30	Unstructured	1.180	0.1350

4. Machine Learning Model

4.1 Model Choice: XGBoost Regressor

The XGBoost regressor was selected for its ability to handle nonlinear relationships and perform robustly on relatively small datasets. This approach helps capture the complex interactions between aerodynamic variables such as AoA and ground clearance.

4.2 Performance Summary

The model was trained on 70% of the data and tested on the remaining 30%. Two separate regressors were used for lift and drag:

5. Discussion

The integration of CFD simulation data and ML modeling presents a compelling strategy for aerodynamic optimization in Formula 1:

• Rapid Predictions: With the trained ML model, new airfoil configurations can be evaluated quickly without rerunning extensive CFD simulations.
• Design Trade-offs: The model highlights that while drag characteristics are predicted with high accuracy, the prediction for lift can be refined further—suggesting that additional data or more complex models may be beneficial.
• Optimization Potential: A prediction engine based on this framework could serve as a front-line tool for design engineers, facilitating rapid exploration of design spaces and helping identify promising configurations for further analysis.

6. Future Work

Enhancements to this study might include:

• Expanding the Dataset: Extract more points using techniques such as digital extraction from figures or additional simulation runs.
• Advanced Modeling Techniques: Experiment with ensemble methods or neural network models that may capture non-linearities better.
• Physics-Informed Features: Augment the inputs with physics-based quantities (e.g., Reynolds number, aspect ratio) to improve the lift predictions.
• Real-Time Prediction Interface: Develop an interactive tool where engineers can input design parameters to instantly obtain predicted CL and CD values.

7. Conclusion

By merging CFD analysis with machine learning, this study demonstrates a pathway to significantly accelerate aerodynamic design in Formula 1. The XGBoost model's promising results—especially in predicting drag—affirm the potential of ML to supplement traditional simulation methods. As aerodynamic demands in motorsports continue to evolve, such integrated approaches will be vital in the quest for competitive performance and innovation.

References

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