We propose a hybrid approach based on meta-modelling techniques and machine-learning algorithms to determine the best car configuration for each circuit. By a specific interpolation model, we obtain an accurate estimation of the car’s speed as a function of the front wing configuration and the bend curvature. Some high-fidelity fluid dynamic simulations train the model and extend it to the entire design space. These data are then used as input for a simplified car dynamics model, providing an accurate estimate of the ideal lap time. Comparison with actual telemetry data confirms that the resulting tool is reliable, fast and easy to use.
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