Define "Aircraft Sizing" (Fuel Calculations) Logic

[michaelweinold]

In the process of setting vehicle parameters, the carculator attempts to design optimally sized cars from scratch. \ It does so using an iterative approach:

while diff > 0.00001:
  old_driving_mass = self["driving mass"].sum().values
  self.set_vehicle_mass()
  self.set_power_parameters()
  (...)
  diff = (self["driving mass"].sum().values - old_driving_mass) / self[
      "driving mass"
  ].sum()

The iterative approach is required because (highly simplified) vehicle parameters are interdependent:

weight(power) and power(weight)

For each vehicle size class, it assumes a starting value for weight/power is provided. The interdependent vehicle parameters are then solved for iteratively.

In contrast, "sizing" aircraft from scratch is unlikely to be a viable approach. Sizing aircraft is a complex undertaking that needs to account for many business parameters that we could not hope to capture. For instance, while an Airbus A380 in all-economy seating configuration would have excellent fuel efficiency/pax, industry now considers the very concept a failure.

It would therefore be prudent to use aircraft parameters from existing proposals (such as the MIT D8 Concept), where all these considerations have already been considered.

@julienmctighe found similar approaches in literature (email from 19.01.2024):

After a quick review of studies with sensitivity analysis, I agree it may be difficult to size aircraft up or down in seating capacity. Most treat seating capacity as fixed and report sensitivity to technology assumptions (LH2 tank efficiency, FC energy density) or design decisions (tank location, material, vent pressure). I have included two examples below.

Onorato 2022 - https://doi.org/10.1007/s13272-022-00601-6 Design decisions (see Section 4 Results)

Karpuk 2023 - https://doi.org/10.3390/aerospace10060566 Technology sensitivity (see Section 4.2.3. Technology Sensitivity of Hydrogen BWB Aircraft)

It therefore seems that we need to have not a "sizing" function, but a "weight and balance" calculation function. This function would compute the fuel required per trip:

def calculate_fuel(distance: int, payload: int, mtow: int, maxfuelweight: int) -> float:
   (...)
   return fuel_weight

[julienmctighe]

As it is still a little unclear how we will get the aircraft size from values reported in literature, I have split out the following into two steps (aircraft sizing for design mission, fuel requirement calculation for off-design mission), with a few options for step 1:

1.a) for design mission (simplest)

• lookup closest conceptual aircraft design (for size and configuration) found in literature
• common metrics provided: design range, design payload, design mission fuel consumption including reserves, MTOW
• apply assumptions from existing aircraft: flight phase energy requirements vs MTOW, range (currently looking into whether this is possible)

1.b) for design mission (current method)

• basic sizing using MTOW/Seat and MJ/Seat-KM correlations from conceptual designs in literature
• apply assumptions from existing aircraft: flight phase energy requirements vs MTOW, range (currently looking into whether this is possible)

1.c) for design mission (similar to GAM)

• input: technology assumptions (component efficiencies and power densities), hybridization method per flight phase (% battery, % kerosene, % H2)
• calculate: propulsion system mass and volume, aircraft mass and wetted area (iterative), energy requirements per flight phase
• make use of existing conceptual aircraft design tools for (re-)sizing

2.) for off-design mission(s)

• input: mission range, mission payload, MTOW, design mission range, design mission payload, design mission energy per flight phase
• scale LTO energy consumption with payload only (TBD)
• scale CCD energy consumption with payload and mission length (TBD)

The energy calculation for 1c) and scaling for 2) could be based on some combination of:

• modified Breguet range equation (to account for difference in aircraft mass delta as fuel or battery energy consumed)
• AEM reported fuel consuption for existing aircraft, varying mission length and payload (if possible)
• other mission performance tools, see DLR tool AirLYFE fuel burn calculation (10.3390/aerospace8070187 section 3.3.5) based on PianoX