Aircraft Flight Parameter Optimization for Multi-Objective Cost Control
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Solution Overview
Problem
Current cost index systems in aircraft flight management are inadequate for modern airline objectives, failing to integrate costs such as environmental impact, noise, and uncertainties like delays or weather, and do not allow for global optimization across flight networks, schedules, or fleet allocation.
Innovation Solution
A method for managing aircraft flight parameters using a 'Smart Index' that considers multiple cost factors, including financial, environmental, and risk costs, to optimize flight profiles dynamically, recalculating and reprogramming flight parameters in response to in-flight events to achieve overall cost objectives across multiple flights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the Cost Index is used to optimize flight parameters, then fuel consumption can be minimized, but it cannot incorporate multiple cost factors such as environmental impact, noise, delays, and weather uncertainties
Solution Approach 1:
The patent segments the overall cost function into multiple independent cost factors (fuel consumption, environmental impact, noise, delays, weather uncertainties). Each factor is calculated and optimized separately, then aggregated to form the comprehensive cost function F. This allows the system to consider diverse cost elements without compromising the ability to minimize fuel consumption.
Solution Approach 2:
The patent creates a universal cost optimization framework that can handle multiple types of cost factors simultaneously. The system is designed to be multi-functional, accommodating not only traditional fuel costs but also environmental, operational, and risk-related costs, making it adaptable to modern airline objectives beyond simple fuel efficiency.
2Productivity
If the Cost Index is applied at the mission level, then flight parameters can be optimized for individual trajectories, but global optimization at the network level, scheduling level, or fleet allocation level cannot be achieved
Solution Approach 1:
The patent implements a nested optimization architecture where the cost function F calculated for individual missions is integrated into larger optimization loops at the network, scheduling, and fleet allocation levels. Each level nests within the previous one, allowing global optimization to build upon local mission-level optimizations without requiring complete system redesign.
Solution Approach 2:
The patent extends the optimization from a single mission dimension to multiple dimensions by incorporating network-level, scheduling-level, and fleet allocation-level considerations. This multi-dimensional approach allows the system to optimize across different operational scales simultaneously, transforming the problem from one-dimensional trajectory optimization to multi-dimensional operational optimization.
3Adaptability or versatility
If flight parameters are optimized based on predefined cost objectives, then initial flight profiles can be generated, but dynamic adaptation to in-flight events requiring parameter revision cannot be performed
Solution Approach 1:
The patent performs preliminary optimization of flight parameters before departure based on available information. When in-flight events occur, the system only needs to recalculate the affected portions of the cost function F and adjust parameters incrementally, rather than performing complete optimization from scratch. This preliminary action reduces the time penalty associated with dynamic adaptations.
Solution Approach 2:
The patent implements a dynamic optimization system that continuously monitors flight conditions and automatically recalculates parameters when events occur. The system transitions from static pre-flight optimization to dynamic in-flight optimization, allowing real-time adaptation while managing computational burden through event-triggered recalculation rather than continuous optimization.
Data Source
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AI summary
An aircraft flight parameter management system is configured with overall flight cost objectives. For a first flight, first-flight cost function parameters are determined (103) with respect to various cost factors. Flight parameter optimization is performed (104) to minimize the cost function. The avionics of an aircraft performing the first flight are programmed with these flight parameters. Upon detection of an in-flight event requiring a revision of the flight parameters, the cost function parameters are recalculated, along with the flight parameters, and the avionics are reprogrammed accordingly. This process is repeated for at least a second flight, taking into account the actual contribution of the first flight to the overall objectives, and so on. Thus, an airline can perform overall multi-objective optimization on its flights.