In busy arrival streams, aircraft remain safely separated to avoid encountering wake turbulence from preceding aircraft. SESAR is researching ways of optimising wake separations between consecutive arrivals on the final approach by using dynamic real-time data, big data and machine learning techniques. 

The candidate solution allows for the conditional reduction of wake separations based on wake risk predictions. Wake risk predictions are based on the real time measurements of wake risk from using a ground based lidar, metrological data and aircraft data combined with advanced big data and machine learning techniques.

The dynamic pairwise wake separations per aircraft pair are relayed to the tower controllers via a separation delivery tool. These dynamic pairwise wake separations lead to a benefit in terms of increased runway throughput capacity due to the reduced, optimised separations on the final approach, and safety is maintained. 

BENEFITS

Increased runway throughput