The LCC model breaks total operating cost into two buckets — fixed (independent of how much you fly) and variable (scale with flight hours) — then integrates them over the service life to produce life cycle totals and revenue metrics.
Cost taxonomy
Total Annual Cost
├── Fixed Costs
│ ├── Financing & Asset
│ │ ├── Annual Amortization (loan repayment)
│ │ ├── Annual Depreciation (straight-line residual loss)
│ │ └── Landing Site Support (vertiport fees × hours)
│ ├── Insurance
│ │ ├── Hull Insurance
│ │ └── Liability Insurance (+10% for autonomous aircraft)
│ ├── Facilities & Admin
│ │ ├── Maintenance Software
│ │ ├── Miscellaneous Service
│ │ ├── Property Tax
│ │ └── Hangar & Office Expenses
│ └── Personnel
│ ├── Pilot Salaries
│ ├── Staff Salaries
│ ├── Personnel Benefits
│ └── Annual Training Cost
└── Variable Costs (per flight hour × annual hours)
├── Fuel Cost
├── Maintenance Labor
├── Scheduled Parts
├── Midlife Inspection Reserve
├── Propeller Allowance
├── Engine Overhaul Reserve
├── Modernisation Reserve
├── Paint Reserve
├── Refurbishing Reserve
├── Battery Replacement Reserve (electric/hybrid only)
├── Miscellaneous Trip Expenses
└── Landing Fees
Fixed cost calculations
Financing
The model uses a standard amortizing loan formula:
Loan amount = Purchase price × Finance_Percent
Monthly rate (r) = Interest_Rate / 12
n = Life_Cycle_Time × 12 (number of monthly payments)
Monthly payment = Loan × r(1+r)^n / ((1+r)^n − 1)
Annual amortization = Monthly payment × 12
Depreciation
Straight-line over the service life:
Resale value = Purchase price × Resale_Percent
Annual depreciation = (Purchase price − Resale value) / Life_Cycle_Time
Autonomous aircraft adjustments
When Number_of_Pilots = 0:
Automation_Cost_Base is added to the purchase price- Liability insurance is multiplied by 1.10 (10% surcharge)
- Pilot salaries and pilot training are zero
Personnel
Pilots per aircraft = Number_of_Pilots × crews (from lookup)
Pilot salaries = Annual_Pilot_Salary × Pilots_per_Aircraft
Benefits = Pilot_Salary × Percent_Salaries_to_Benefits × Pilots_per_Aircraft
Training = Maintenance_Training_Cost + Pilot_Training_Cost × Pilots_per_Aircraft
Variable cost calculations
All variable costs are expressed per flight hour and then multiplied by Flight_Hours_per_Year to get annual totals.
Amortised reserve costs
Replacement/overhaul costs are spread across the interval between events:
Engine overhaul reserve = (Engine_Overhaul_Cost × Number_of_Engines) / Engine_Overhaul_Interval
Modernisation reserve = Modernisation_Cost / Modernisation_Interval
Paint reserve = Paint_Cost / Paint_Interval
Refurbishing reserve = Refurbishing_Cost / Refurbishing_Interval
Battery reserve = Battery_Cost / Battery_Life
Propeller allowance = Propeller_Cost / Propeller_Life
Landing fees
Flights per hour = 1 / Flight_Time_Hours
= Aircraft_Speed / Mission_Stage_Length
Landing fees/hour = Flights_per_Hour × Landing_Fee
Life cycle simulation
Annual costs are constant (no inflation model by default), so the ODE right-hand side is a constant vector. The state vector tracks ten cumulative quantities:
| Index | State variable |
|---|
| 0 | Cumulative amortization |
| 1 | Cumulative total costs |
| 2 | Cumulative fixed costs |
| 3 | Reserved |
| 4 | Reserved |
| 5 | Cumulative personnel costs |
| 6 | Cumulative training costs |
| 7 | Cumulative variable costs |
| 8 | Total flight hours |
| 9 | Cumulative landing fees |
Life_Cycle_Time using SciPy’s solve_ivp with RK45 (relative tolerance 1e-6, absolute tolerance 1e-8).
Revenue metrics
After simulation, calculate_metrics() derives two revenue-facing metrics:
Revenue CASM
Cost per available seat-nautical mile — the standard airline profitability metric:
Revenue hours = Flight_Hours_per_Year × (1 − Percent_Repositioning_Flight_Hours)
Revenue nm/year = Revenue_hours × Aircraft_Speed (statute miles → nm via 1.151 factor)
Seat-nm/year = Revenue nm/year × Aircraft_PAX_Seats
Revenue CASM = Life_Cycle_Total_Cost × (1 + Profit_Margin) / (Seat-nm over life cycle)
Fare per passenger-nautical mile
Fare per pax-nm = Revenue_CASM / 5
The /5 factor normalises CASM to a per-passenger basis assuming an average load factor. This is a planning-level estimate — adjust Aircraft_PAX_Seats and your profit margin to model different load scenarios.
Comparison outputs
run_comparison() returns a DataFrame with one row per aircraft, sorted by Revenue CASM:
| Column | Description |
|---|
Aircraft | Display name parsed from filename |
Revenue_CASM | Revenue cost per available seat-nm |
Fare_per_Pax_NM | Required fare per passenger-nm |
Total_Annual_Costs | Annual fixed + variable costs |
Life_Cycle_Total_Cost | Cumulative cost over service life |
Annual_Fixed_Costs | Fixed cost subtotal |
Annual_Variable_Costs | Variable cost subtotal |
Aircraft_Purchase_Price | Acquisition cost |
Variable_Cost_per_Hour | Total variable cost per flight hour |
Aircraft_Speed | Average cruise speed (knots) |
Pilots_per_Aircraft | Effective pilot count |
PAX_Seats | Passenger seat count |