> ## Documentation Index > Fetch the complete documentation index at: https://docs.idoptlab.com/llms.txt > Use this file to discover all available pages before exploring further. # Cost Model > How the LCC model computes fixed costs, variable costs, and revenue metrics 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 | Initial conditions include the **down payment** (purchase price × (1 − Finance\_Percent)) and the **first-year training cost** as upfront charges at t = 0. Integration runs from year 0 to `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 |