Predictive Flight Management System for Fuel Cost Optimization
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Solution Overview
Problem
Conventional flight management systems are reactive and rely on simplifying assumptions, leading to suboptimal fuel savings and increased direct operating costs, as they typically determine flight plans based on averaged data and past aircraft states rather than real-time, specific aircraft performance.
Innovation Solution
Implementing a predictive flight management system that uses accurate mathematical models and real-time data to generate cost-optimal control inputs, adjusting for actual aircraft characteristics and future performance predictions to minimize direct operating costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional flight management systems use simplifying assumptions and averaged data, then the system complexity is reduced and ease of operation is improved, but fuel optimization performance deteriorates and direct operating costs increase
Solution Approach 1:
The system performs preliminary actions by pre-computing optimal flight paths and parameters using accurate aircraft performance models before flight execution. This allows the system to provide optimized guidance without requiring complex real-time calculations during flight, maintaining ease of operation while achieving superior fuel optimization compared to conventional reactive systems.
Solution Approach 2:
The system creates a digital copy or model of the specific aircraft's performance characteristics through accurate mathematical modeling. This copy allows the system to simulate and optimize flight paths based on the actual aircraft's unique performance traits without requiring physical trial-and-error, thereby reducing fuel consumption while keeping the operational system relatively simple.
2Device complexity
If conventional flight management systems react to current aircraft states, then the system complexity is reduced, but the optimization performance deteriorates and fuel savings are compromised
Solution Approach 1:
The system performs preliminary optimization calculations using accurate aircraft performance models to determine optimal flight paths before execution. This proactive approach allows the system to minimize fuel consumption without requiring complex real-time reactive adjustments during flight, thus maintaining manageable device complexity while achieving superior energy efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms that compare actual aircraft performance with predicted performance from the accurate mathematical model. This feedback allows the system to iteratively refine flight path optimization based on real-world deviations, achieving better fuel savings while keeping the overall system complexity manageable through structured feedback loops.
3Ease of manufacture
If conventional flight management systems use simplifying assumptions for flight path determination, then the ease of manufacture and implementation is improved, but manufacturing precision and optimization accuracy deteriorate
Solution Approach 1:
The system performs preliminary computations using accurate aircraft performance models to determine optimal flight paths before actual flight execution. This approach allows the system to achieve high optimization accuracy by incorporating specific aircraft characteristics without requiring complex real-time adjustments, thereby maintaining ease of manufacture and implementation while improving precision.
Solution Approach 2:
The system creates an accurate digital model or copy of the specific aircraft's performance characteristics through mathematical modeling. This copy enables precise optimization calculations that account for the actual aircraft's unique traits, achieving manufacturing precision in terms of optimization accuracy while keeping the physical system relatively simple to manufacture and implement.
Data Source
AI summary
A system, computer-readable medium, and a method including obtaining flight data for a specific aircraft for a prescribed flight; obtaining current sample measurements of at least one state or output of the specific aircraft; performing based on the obtained flight data, the current measurements or outputs, and a mathematical model accurately representing an actual operational performance of the specific aircraft and providing a predictive indication of a future performance of the specific aircraft, a control optimization to determine a cost-optimal control input for the prescribed flight; adjusting, in response to a consideration of actual operational characteristics of the specific aircraft, the optimized control input; and transmitting the adjusted optimized control input to the specific aircraft to operate the specific aircraft to minimize the direct operating cost for the prescribed flight.