This project focuses on the development of OpenAP, a comprehensive Open Aircraft Performance and Emission Model. OpenAP aims to provide a fully open-source platform for modeling critical aircraft performance metrics, including kinematic properties, thrust, drag, and fuel flow rates. Built on the principle of transparency and accessibility, OpenAP is founded on open data and designed to advance air transport research. The repository houses a rich collection of OpenAP databases, along with a Python implementation that streamlines data access and facilitates complex aircraft performance computations. Overall, OpenAP serves as a transparent and accessible tool for fostering rigorous and collaborative research in air transport.

Presenting a fully open-source trajectory optimizer, providing researchers with seamless access to the sophisticated yet efficient direct collocation optimization approach. The optimizer is capable of adjusting to meteorological conditions and can be utilized for distinct flight phases independently or in combination. It encompasses conventional fuel and cost index objectives, as well as climate metrics-based objectives utilizing global warming or temperature potential.

Fast Meteo is a Python package that retrieves meteorological data for flight trajectories. It uses Analysis-Ready, Cloud Optimized (ARCO) ERA5 data from Google’s Public datasets, which are derived from Copernicus ERA5. The package provides a fast and efficient way to obtain meteorological parameters such as wind, temperature, humidity, and others. It can be used in both local mode and server-client mode, and allows for customization of meteorological features and pressure levels. The package also includes a pre-sync command for downloading data and a client-server architecture for distributed processing.

PyModeS is a Python library for decoding and encoding Mode S (including ADS-B) messages. It is an open-source project that receives great support and contributions from the aviation community. This python package can be imported into an existing python project, and it also is used as a standalone tool to view and save live traffic data.

This is an open-source project implements contrail segmentation neural network models in PyTorch. The models are built using augmented transfer learning, where I applied several image augmentation on a pre-train ResUNet model. This way, the model can be quickly fine tuned with a handful of labelled satellite images. With the invention of a new loss function, SR Loss, I can further optimizes the contrail detection using contrail information in Hough space.

A Python library is introduced for wind field estimation based on the Meteo-Particle model. By employing ADS-B and Mode S data from aircraft, indirect wind and temperature measurements can be derived initially. These measurements are often unevenly distributed in the airspace. The Meteo-Particle model allows the assimilation of wind and temperature fields using these measurements, thereby overcoming the challenge posed by the distribution of the data.

This is an open-access book that provides researchers, engineers, and students a practical guide to decoding ADS-B and other types of common Mode S messages. The first part of this book presents the knowledge required to get started with decoding these signals. It includes background information on primary radar, secondary radar, Mode A/C, Mode S, and ADS-B, as well as the hardware and software setups necessary to gather radio signals. After that, the 17 core chapters of the book investigate the details of all types of ADS-B signals and commonly used Mode S signals. Throughout these chapters, examples and sample Python code are used extensively to explain and demonstrate the decoding process.

Construct a database to search for aircraft IDs (such as ICAO address, and registrations) and related information. It is built in Python / Flask and with data from Flight Radar 24.

A python library to process and analyze ADS-B flight data. It can reconstruct flight trajectories from ADS-B data and identify flight phases.