System and method for optimising flight efficiency

A system for improving the fuel efficiency of an aircraft flight, the system comprising:

• a flight plan data store configured to store a flight plan for the aircraft flight, the flight plan comprising a scheduled route for the aircraft flight, a scheduled landing runway at a destination aerodrome, and a scheduled arrival time and date at the destination aerodrome;
• a weather data store configured to store predicted weather data corresponding to the vicinity of the airport at the scheduled arrival time and date;
• a trained machine learning prediction module that has trained based on historical aircraft flight data, the trained machine learning prediction module comprising:
• a first cluster mapping module configured to receive the scheduled route for the aircraft flight, the scheduled landing runway at a destination aerodrome, and the predicted weather data corresponding to the vicinity of the destination aerodrome at the scheduled arrival time and date;
• the first cluster mapping module configured to determine, based on the trained machine learning of the prediction module, a predicted arrival route and a predicted landing runway;
• a second cluster mapping module configured to determine, based on the predicted arrival route and the predicted landing runway, a plurality of descent flight trajectories for the aircraft flight;
• a trajectory filter configured to filter the plurality of descent flight trajectories for the aircraft flight to obtain the most probable descent flight trajectories, each ranked by a conditional probability based on one or more environmental factors;
• and
• a distance prediction module configured to determine a distance corresponding to each of the most probable descent flight trajectories;
• and
• an output configured to output, to a pilot, one or more of the most probable descent flight trajectories and the corresponding distances;
• wherein the trained machine learning prediction module is further configured to recommend a top of descent location corresponding to the distance determined for the most probable descent flight trajectory based on a desired angle of descent and windspeed data of the predicted weather data;
• and
• wherein the output is further configured to output the recommended top of descent location to the pilot or to a flight director system of the aircraft's avionics for control of an associated autopilot system.

A method for training a machine learning module to predict the most probable descent flight trajectories, the method comprising:

• receiving an indication of a planned route for a plurality of historic aircraft flights;
• receiving, for each historic aircraft flight, recorded flight data from the aircraft, the recorded flight data comprising aircraft position data and aircraft heading data, the position data forming a trajectory for each aircraft flight of the plurality of historic aircraft flights;
• receiving an indication of the magnetic bearing of each runway of a plurality of runways at a destination aerodrome;
• determining, for each historic aircraft flight, which runway was used for landing of the plurality of runways at the destination aerodrome, and what the approach direction to that runway was, based on the aircraft position data and/or the aircraft heading data;
• performing a first cluster analysis, at a first clustering module of the machine learning module, configured to group trajectories having the same determined landing runway and approach direction into a plurality of clusters;
• performing, for each of the plurality of clusters, a second cluster analysis, at a second clustering module of the machine learning module, configured to group trajectories based on the distance between respective trajectories to determine a consolidated set of trajectories;
• and
• determining, for each consolidated set of trajectories, at a regression module, a conditional probability based on one or more environmental factors.

A method for predicting the optimum parameters for a fuel efficient aircraft descent phase, the method comprising:

• training a machine learning module by:
• receiving, for each of a plurality of historic aircraft flights, recorded flight data from the aircraft, the recorded flight data comprising aircraft fuel consumption data, aircraft vertical speed data, aircraft indicated airspeed data, altitude based windspeed data, and altitude based ambient temperature data;
• dividing a descent altitude into a plurality of altitude subsections;
• determining, for each altitude subsection of each of the plurality of historic aircraft flights, the time taken and the amount of fuel consumption for the aircraft to descend through the altitude subsection based on the recorded flight data;
• and
• associating each altitude subsection of each of the plurality of historic aircraft flights with the recorded windspeed, the ambient temperature and the aircraft's indicated airspeed or Mach airspeed;
• for a future aircraft descent having a specified flight plan data and predicted altitude based weather data, iteratively stepping through the descent speeds for each altitude subsection, and performing a prediction, using the trained machine learning module, of the overall time taken and fuel consumed for each descent speed;
• and
• comparing the predictions and selecting the altitude subsection descent speeds associated with the lowest fuel consumption and within a desired flight time range.