Project

WINFC is funded by the European Commission under the Clean Sky JTI (Joint Technology Initiative, http://www.cleansky.eu/), call SP1-JTI-CS-2013-02, activity SGO (System for Green Operation).

 

WINFC is a 18 months project which started in March 2014. 

 

The data acquired during the flight and the updated weather and environmental conditions shall be considered by the pilot during a real aircraft flight. A tool for re-planning the optimal trajectory considering the updated condition can be useful to reduce the pilot workload and can help to chose the optimal trajectory that can reduce the emissions of pollutants and noise. To this goal, several source of information can be provided to the re-planning algorithm: data acquired by weather radar, METAR and NOTAM updates, updated weather forecasts, etc. The role of data fusion is relevant as it can affect significantly the consequent optimization phase.

Q-AI is the Quasi-Artificial Intelligence on-board trajectory optimization algorithm developed by SELEX-ES and under implementation on EFB by CNIT in the KLEAN project (Call ref. SP1-JTI-CS-2011-03 016). The main goal of Q-AI is to help the pilot in selecting the trajectory (by managing a risk map due to post processing classification of the avionic weather radar data) for the aircraft that produce reduced emissions of CO2, NOx and noise.

 

WINFC specific objectives and their progress are summarized below:

 

Ob.1 - Analysis of the information sources about un-forecasted events like weather changes, traffic congestion in present and future ATM environment

 

Ob.2 - Study of new measurements methods on board able to provide new weather real-time information

 

Ob.3 - Development of a SW tool for simulating the information data flow coming from different sources in case of unexpected weather and traffic scenarios

 

Ob.4 - Development of a data fusion tool for the adaptive integration and correlation of the information coming from different information sources

 

Ob.5 - Definition of an efficient and effective unbiased threat/importance factor(s) useful for the pilot and as input parameters to Q-AI trajectory and mission optimization algorithms.

 

Ob.6 - Validation of the data fusion tool by a test activity based on the data flow SW tool applied to selected realistic scenarios