OpenAP: Open Aircraft Performance Model and Toolkit

Open-source aircraft performance model and toolkit for Python. Calculate aircraft performance, fuel consumption, and emissions for air transportation studies and simulations.

Requirements: Python 3.11+

🕮 User Guide

The OpenAP handbook is available at openap.dev.

Installation

Install the latest stable release from PyPI:

pip install --upgrade openap

Install the development branch from GitHub (may not be stable):

pip install --upgrade git+https://github.com/junzis/openap

Plotting is optional. Install the plotting dependencies when needed:

pip install "openap[plot]"

Aircraft models, flight-phase identification, filtering, and statistical fitting work without Matplotlib. Plotting functions load Matplotlib only when called. The openap[all] extra includes plotting and both optional backends.

Content

Model Data

Data in this repository includes:

• Aircraft data: Collected from open literature.
• Engine data: Primarily from the ICAO emission data-bank, including fuel flow and emissions.
• Drag polar model data: Exclusively derived from open data (reference).
• Fuel model data: Polynomial models derived from the acropole model by @JarryGabriel.
• Kinematic data: The kinematic model describes speed, altitude, and vertical rate (reference).
• Navigation data: Airport and waypoints obtained from X-Plane.

Python Packages

The OpenAP Python library includes the following packages:

• prop: Module for accessing aircraft and engine properties.
• aero: Module for common aeronautical conversions.
• nav: Module for accessing navigation information.
• thrust: Module provides Thrust() class for computing aircraft thrust.
• drag: Module provides Drag() class for computing aircraft drag.
• fuel: Module provides FuelFlow() class for computing fuel consumption.
• emission: Module provides Emission() class for computing aircraft emissions.
• kinematic: Module provides WRAP() class for accessing kinematic performance data.
• phase: Module provides FlightPhase() class for determining flight phases.
• gen: Module provides FlightGenerator() class for trajectory generation.

Example:

import openap

# Get aircraft properties
aircraft = openap.prop.aircraft("A320")
print(aircraft["mtow"])  # max takeoff weight: 78000 (kg)

# Calculate fuel flow during cruise
fuelflow = openap.FuelFlow("A320")
ff = fuelflow.enroute(mass=60000, tas=250, alt=30000)
print(ff)  # fuel flow: 0.92 (kg/s)

Units: Input parameters can be scalar, list, or ndarray. Speeds are in knots, altitudes in feet, vertical rates in feet/min. Mass is in kilograms (SI).

Add-ons

The OpenAP library can also be used to interact with BADA performance models if you have access to the BADA data from EUROCONTROL. You can use the following code:

from openap.addon import bada4

fuelflow = bada4.FuelFlow()

The methods and attributes of openap.addon.bada4.FuelFlow() are the same as those of openap.FuelFlow().

Alternative Backends: CasADi and JAX

OpenAP supports multiple computational backends beyond NumPy:

• CasADi: For symbolic computations and optimization
• JAX: For automatic differentiation and GPU acceleration

Installation

Install with optional backend support:

pip install openap[casadi]  # CasADi backend
pip install openap[jax]     # JAX backend
pip install openap[all]     # Both backends and plotting

Usage

# CasADi backend
import openap.casadi as oc

fuelflow = oc.FuelFlow("A320")
fuelflow.enroute(mass, tas, alt)  # works with CasADi DM, SX, or MX types

# JAX backend
import openap.jax as oj

fuelflow = oj.FuelFlow("A320")
fuelflow.enroute(mass, tas, alt)  # works with JAX arrays

The API is identical to the standard openap module. The backends are implemented using a protocol-based architecture in openap/backends/.

Citing OpenAP

@article{sun2020openap,
  title={OpenAP: An open-source aircraft performance model for air transportation studies and simulations},
  author={Sun, Junzi and Hoekstra, Jacco M and Ellerbroek, Joost},
  journal={Aerospace},
  volume={7},
  number={8},
  pages={104},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}