Fuel Efficiency for Max Performance
...not Verstappen.
In 2010, the FIA banned refuelling of cars during pitstops. Hence, a race car is expected to have sufficient fuel to last a full race. Factors to consider in fuel selection includes environmental impact, weight and efficiency. Although the fuels utilised by F1 teams are composed of the same base fuel chemistries, the racing fuel is highly fine-tuned and optimized for use in specific F1 cars just as you will fine-tune a machine learning model for a specific use-case. So the best fuel for a Williams engine might not necessarily be the best for a Haas Ferrari engine.
The energy content of a fuel depends on its mass density.
Volumetric energy density typically measured in megajoules per liter (MJ/L) or Kilowatt-hours per liter (kWh/L). Where Evā is the volumetric energy density, E is the total energy content of the fuel, and V is the volume of the fuel.
Gravimetric energy density typically measured in megajoules per kg (MJ/kg) or kilowatt-hours per kilogram (kWh/kg). Where Egā is the gravimetric energy density, E is the total energy content of the fuel, and m is the mass of the fuel.
There are two types of fuel efficiency of a fuel mix: volumetric fuel efficiency and gravimetric fuel efficiency. The former, as the name implies measures the efficiency or energy density of a fuel as a measure of its volume whilst the later a measure in respect to its mass. Typically. the greater these values are , the more efficient a fuel is. You want sufficient fuel but as light as can be too. Remember in the last post how there it is iterated that there is no point optimising a car to only end up leading for a few laps, you need to win in the long run.
Optimizing for power with excess compromise for fuel efficiency is of no use.
You do not want to have excess fuel carried around to the end of a race, also you want to have optimized power as well as sufficient fuel to complete the race. So engineers calculate the amount of fuel they need to complete a full race. It will interest you to know, that although each race circuit has a different distance, the number of laps are adjusted to make each race roughly the same length. This often falls around 308 Km. Hence this makes it just a little easier to make the required fuel calculations. In recent years, Formula1 teams have adopted an hybrid power unit system (electric and fuel); however lets assume a simplified scenario without considering hybrid systems:
def calculate_fuel_required(distance, fuel_efficiency, energy_content):
fuel_liters = distance / fuel_efficiency
energy_required = distance * energy_content
return fuel_liters, energy_required
fuel_efficiency = 5 #Ā Fuel efficiency in km/L
energy_content = 35 # Energy content of fuel in MJ/L
distance_to_cover = 308 # Distance to be covered in Km
fuel_liters, energy_required = calculate_fuel_required(distance_to_cover, fuel_efficiency, energy_content)
print(f"Fuel required: {fuel_liters:.2f} liters")
print(f"Energy required: {energy_required:.2f} megajoules")
You can run the above code block in a python environment and replace the values as you wish. However, that is a simplified calculation of the liters of fuel and energy required for a race car to complete a race of a specific distance. Other factors including race conditions and engine come to play in a real scenario and edge computing is used to calculate these values quickly by running multiple simulations. We will discuss more on edge computing and its importance later.
Merry Christmas!