Flight Plan Optimization Using Dynamic Route Learning
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Solution Overview
Problem
Pilots lack tools to optimize flight plans in real-time based on evolving meteorological conditions, leading to potential fuel overconsumption and arrival time variability due to unpredictable flight parameters.
Innovation Solution
A method that learns a network of air routes specific to a fleet of aircraft using machine learning, collecting and analyzing flight data to determine optimal routes that adapt to current conditions, including meteorological data, to minimize fuel consumption and arrival time deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If pilots use traditional flight planning software with forecast air routes, then flight safety is ensured through predetermined routes, but fuel consumption increases and arrival time varies due to inability to adapt to changing meteorological conditions
Solution Approach 1:
The system dynamically adjusts the air route during flight based on real-time meteorological conditions. The originally static forecast air route is replaced with a dynamic optimized route that adapts to changing wind, temperature, and pressure conditions, allowing the aircraft to maintain optimal fuel consumption while responding to actual environmental factors.
Solution Approach 2:
The system continuously receives feedback from meteorological sensors and flight parameters to recalculate and optimize the air route. By monitoring actual flight conditions and comparing them with forecast data, the system provides real-time feedback that enables continuous route optimization, reducing fuel consumption while maintaining safety margins.
2Productivity
If pilots rely on experience to make flight decisions, then flexibility in responding to conditions is achieved, but objective optimization of fuel consumption and arrival time is lost
Solution Approach 1:
The system acts as an intermediary between the pilot and flight decision-making by processing meteorological data and flight parameters through objective algorithms. This intermediary layer provides data-driven route optimization recommendations that complement pilot experience, ensuring objective analysis of fuel consumption and arrival time predictions while maintaining pilot authority over final decisions.
Solution Approach 2:
The system replaces subjective pilot judgment with an automated computational system that objectively analyzes flight data and meteorological conditions. By substituting manual experience-based decision-making with algorithmic optimization, the system eliminates biases and inconsistencies while providing quantifiable metrics for fuel consumption and arrival time prediction.
3Loss of time
If flight plans are prepared in advance with forecast conditions, then preparation time is sufficient, but real-time adaptation to actual flight conditions is delayed
Solution Approach 1:
The system performs preliminary calculations of multiple potential optimized routes based on forecast conditions before departure. By pre-computing alternative routes and their associated fuel consumption and arrival time predictions, the system reduces real-time computational burden and enables rapid adaptation when actual conditions diverge from forecasts, minimizing arrival time variability without requiring complex real-time processing.
Data Source
AI summary
The invention relates to a method for optimising a flight plan consisting of an air route for an aircraft of a fleet of aircraft, each aircraft being designed to record flight data, the method comprising a preliminary step of learning a network of air routes specific to the fleet of aircraft, the method comprising a step of determining a fuel consumption model specific to the aircraft based on the flight data, the method comprising the subsequent steps of: —collecting meteorological data associated with the aircraft environment, and —determining an optimised flight plan for reaching the destination waypoint, based on a current position of the aircraft, the flight conditions of the aircraft, the predefined air route, the consumption model of the aircraft, the meteorological conditions and the previously defined optimal air routes.