https://github.com/junzis/openap-top
easy-peasy optimal flight trajectory
https://github.com/junzis/openap-top
casadi contrail fuel optimization wind
Last synced: 4 months ago
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easy-peasy optimal flight trajectory
- Host: GitHub
- URL: https://github.com/junzis/openap-top
- Owner: junzis
- License: lgpl-3.0
- Created: 2022-04-04T09:27:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-06-11T21:36:43.000Z (4 months ago)
- Last Synced: 2025-06-11T22:41:05.638Z (4 months ago)
- Topics: casadi, contrail, fuel, optimization, wind
- Language: Python
- Homepage: https://openap.dev/optimize
- Size: 8.14 MB
- Stars: 31
- Watchers: 5
- Forks: 9
- 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 based on the [OpenAP](https://github.com/junzis/openap) package.
This tool uses non-linear optimal control direct collocation algorithms from the `casadi` library. It provides simple interfaces to generate different optimal trajectories. For example, this tool can generate any of the following trajectories (or combinations thereof):
- Complete flight trajectories or flight segments at different flight phase
- Fuel-optimal trajectories
- Wind-optimal trajectories
- Cost index optimized trajectories
- Trajectories optimized using customized 4D cost functions (contrails, weather)
- Flight trajectories with constraint altitude, constant mach number, etc.
What's more, you can also design your own objective functions and constraints to optimize the flight trajectory.
## 🕮 User Guide
A more detailed user guide is available in the OpenAP handbook: .
## Install
1. Install from PyPI:
```sh
pip install --upgrade openap-top
```
2. Install the development branch from GitHub (also ensures the development branch of `openap`):
```sh
pip install --upgrade git+https://github.com/junzis/openap
pip install --upgrade git+https://github.com/junzis/openap-top
```
The `top` package is an extension of `openap` and will be placed in the `openap` namespace.
## Quick Start
### A simple optimal flight
The following is a piece of example code that generates a fuel-optimal flight between two airports, with a take-off mass of 85% of MTOW:
```python
from openap import top
optimizer = top.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)
flight = optimizer.trajectory(objective="fuel")
```
Other predefined objective functions are available, for example:
```python
# Cost index 30 (out of max 100)
flight = optimizer.trajectory(objective="ci:30")
# Global warming potential over 100 years
flight = optimizer.trajectory(objective="gwp100")
# Global temperature potential over 100 years
flight = optimizer.trajectory(objective="gtp100")
```
The final `flight` object is a Pandas DataFrame. Here is an example:
### Using Wind Data
To include wind in our optimization, first download meteorological data in `grib` format from ECMWF, such as the ERA5 reanalysis data.
Once grid files are ready, we can read and enable wind for our optimizer with this example code:
```python
from openap import top
fgrib = "path_to_the_wind_data.grib"
windfield = top.tools.read_grids(fgrib)
optimizer = top.CompleteFlight("A320", "EHAM", "LGAV", m0=0.85)
optimizer.enable_wind(windfield)
flight = optimizer.trajectory() # default objective is fuel
```
Next, we can visualize the trajectory with wind barbs:
```
top.vis.trajectory(flight, windfield=windfield, barb_steps=15)
```
