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https://github.com/junzis/openap-top
Flight trajectory optimizer with non-linear optimal control
https://github.com/junzis/openap-top
Last synced: 3 months ago
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Flight trajectory optimizer with non-linear optimal control
- Host: GitHub
- URL: https://github.com/junzis/openap-top
- Owner: junzis
- License: lgpl-3.0
- Created: 2022-04-04T09:27:21.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-07T13:40:45.000Z (6 months ago)
- Last Synced: 2024-10-13T21:42:31.258Z (4 months ago)
- Language: Python
- Homepage: https://openap.dev/top
- Size: 8.02 MB
- Stars: 21
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-source-aviation - openap-top - Open flight trajectory optimizer built with non-linear optimal control method (Tools and Libraries)
README
# OpenAP Trajectory Optimizer
This repository contains the flight trajectory optimizer module of [OpenAP](https://github.com/junzis/openap).
A more detailed user guide can be found at: 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 environment (`openap-env`) and install the necessary packages:
```sh
mamba create -n openap-env python=3.11 -c conda-forge
mamba activate openap-env
mamba install cfgrib cartopy casadi scikit-learn -c conda-forge
```
2. Install the most recent version of `openap`:
```sh
pip install --upgrade git+https://github.com/junzis/openap
```
3. Install the most recent version of `openap-top`:
```sh
pip install --upgrade git+https://github.com/junzis/openap-top
```
## Quick start
Example code to generate a fuel-optimal flight between two airports:
```python
from openap import top
optimizer = top.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)
flight = optimizer.trajectory(objective="fuel")
```
You can specify different objective functions as:
```python
flight = optimizer.trajectory(objective="ci:30")
flight = optimizer.trajectory(objective="gwp100")
flight = optimizer.trajectory(objective="gtp100")
```
The final `flight` object is a Pandas DataFrame. The following is an example:
## 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:
```python
from openap import top
from openap.top import wind
optimizer = top.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)
fgrib = "path_to_the_wind_data.grib"
windfield = 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:
