Machine Learning for Aircraft Formation Flight Pairing
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Solution Overview
Problem
Current methods for optimizing aircraft formation flight lack efficient decision-making to pair lead and trailing aircraft, leading to suboptimal flight planning and reduced benefits from cooperative trajectories.
Innovation Solution
A system utilizing machine learning to predict and optimize formation flight segments by analyzing flight data and air traffic control requirements, enabling real-time pairing of lead and trailing aircraft for enhanced operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the lead aircraft delays or circles to allow the trailing aircraft to join formation, then the trailing aircraft can join formation without extended tail chase, but the benefit from cooperative trajectories is reduced or eliminated
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal formation flight paths and predicting favorable wind conditions before the flight occurs. Flight plans are optimized in advance to account of predicted meteorological data, allowing both aircraft to depart simultaneously while still achieving formation flight benefits without requiring delays or circling maneuvers during the flight.
Solution Approach 2:
The system dynamically adapts flight plans based on real-time and predicted meteorological conditions. By continuously monitoring and predicting wind patterns, the system can adjust departure times, altitudes, and routes dynamically to maximize formation flight opportunities while maintaining operational efficiency, rather than relying on static pre-planned maneuvers.
2Productivity
If simultaneous departure is used for lead and trailing aircraft, then airline route planning is improved, but the trailing aircraft cannot join formation without extended high-power tail chase
Solution Approach 1:
The system performs preliminary optimization of flight paths and predicts favorable meteorological conditions before departure. By pre-calculating optimal routes and predicting wind patterns, the system enables simultaneous departure while avoiding the need for high-energy tail chase maneuvers, as the formation flight is arranged to take advantage of predicted conditions rather than requiring corrective maneuvers.
Solution Approach 2:
The system incorporates feedback from predicted and actual meteorological conditions to adjust flight plans. By monitoring wind patterns and other environmental factors, the system can modify departure times or routes dynamically to create optimal formation flight conditions, allowing simultaneous departure without excessive energy consumption while still achieving formation benefits.
3Productivity
If the system analyzes historical flight data and uses machine learning to predict formation flight segments, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The system creates a simplified digital representation or copy of historical flight data and meteorological patterns to train machine learning models. Instead of processing complex real-time multi-variable problems directly, the system uses pre-processed historical data copies to train predictive models that can efficiently forecast formation flight opportunities, reducing the computational complexity of real-time decision-making while maintaining high operational efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances fuel economy and flight range by optimizing formation flight paths, reducing the need for lag turns or extended tail chases, and improving overall fleet operations.
Implementation Method 1
applying the values of the set of features to a machine learning model trained to predict the at least one segment of formation flight
Implementation Method 2
The formation of wake or wingtip vortices trailing behind an aircraft during flight is well known and documented. Generally, when wings are generating lift, air from below the wing is drawn around the wingtips into the region above the wings due to the lower pressure above the wing, which causes a respective vortex to trail from each wingtip.
Data Source
AI summary
A method is provided for optimizing a flight of an aircraft with at least one segment of formation flight. The method includes accessing flight plans for flights of a fleet, and transforming the flight plans into values of a set of features that describe segments of the flights. The values are applied to a machine learning model trained to predict the segment(s) during which the aircraft is within a region that includes at least one second aircraft of the fleet that is thereby capable of serving as a leading aircraft in the segment(s) of formation flight in which the aircraft is a trailing aircraft. A notification is sent to the aircraft of the segment(s) and the second aircraft capable of serving as the leading aircraft. And a second notification is sent to the second aircraft of the segment(s) and the aircraft capable of serving as the trailing aircraft.