Kenny-Heitritter/openap-top

Open flight trajectory optimizer built with non-linear optimal control method

OpenAP Trajectory Optimizer

This repository contains the flight trajectory optimizer module of OpenAP.

A more detailed user guide can be found on: https://openap.dev/top.

Install

OpenAP.top uses cfgrib for integrating wind data, cartopy for plotting, and a few other libraries. I recommend using conda to install these dependencies. Following is an example how I set it up on my computer for testing.

1. Create a new conda environmental (openap), which avoids messing up the base conda environment:

conda create -n openap python=3.10 -c conda-forge

2. Use the openap environment

conda activate openap

3. Install dependent libraries:

conda install -c conda-forge cfgrib cartopy

4. Install the most recent version of openap:

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

5. Install the most recent version of openap-top:

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

Quick start

Example code to generate a fuel optimal flight between two airports:

import openap.top as otop

optimizer = otop.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)

flight = optimizer.trajectory(objective="fuel")

You can specify different objective functions as:

flight = optimizer.trajectory(objective="ci:30")
flight = optimizer.trajectory(objective="gwp100")
flight = optimizer.trajectory(objective="gtp100")

The final flight object is a pandas DataFrame.

Use wind data

To enable wind in your optimizer, you must first download meteorological data in grib format from ECMWF, for example, the ERA5 data at https://doi.org/10.24381/cds.bd0915c6.

Then enable the wind for the defined optimizer.

Example code:

import openap.top as otop

optimizer = otop.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)

fgrib = "path_to_the_wind_data.grib"
windfield = otop.wind.read_grib(fgrib)

optimizer.enable_wind(windfield)

flight = optimizer.trajectory(objective="fuel")

If your grib file includes multiple timestamps, make sure to filter the correct time in the previous windfield object (pandas DataFrame).

Example of an optimal flight: