🏎️ Equations, Models and Car Performance
Equations to consider when designing models to predict cat performance. More physics and automobile.
🏎️ - F = ma (Force = mass * acceleration)
This clearly is the first equation that you need to keep in mind. It is the basis of all the other equations. It is also the basis of Newton's second law of motion. In designing your ML model to predict car acceleration, you need to keep this in mind.
🏎️ - F = mv^2/r (Centripetal force = mass * velocity^2 / radius of turn)
This is the equation for centripetal force. It is the force that keeps the car in a circular path. It is the basis of the equation for cornering speed. Each F1 car has a cornering speed that it can take a corner at. This is the speed at which the centripetal force is equal to the force of friction. Any faster and the car will skid off the track. Any slower and the car will be able to take the corner but will be slower than the competition. Your model can predict the speed of a complete lap by taking into account the cornering speed of the car.
🏎️ - F = 1/2 * Cd * A * rho * v^2 (Drag force = 1/2 * drag coefficient * frontal area * air density * velocity^2)
This is the equation for drag force. It is the force that opposes the motion of the car. It is the basis of the equation for top speed. C is the drag coefficient, A is the frontal area of the car, rho is the air density and v is the velocity of the car.
Keep in mind, there is a threshold, Aerodynamics start to have a more noticeable affect on a vehicle at around 50 mph.
Your model can predict the drag force the car may experience by taking into account the expected top speed. Drag force rises with the square of the speed, so doubling the speed requires four times the power to overcome air resistance. This is why F1 cars have such high power outputs.
FINAL NOTES
The relationship between power and force is given by the equation P = F * v (Power = Force * velocity). Hence the more mass it has, the more power it could produce. But remember, there is always a tradeoff whether obvious or not, and there are always thresholds to consider too. Always aim to find these making use of your ML model, making the right analysis with the right features.