Aircraft Takeoff Trajectory Optimization Using Genetic Algorithms
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Solution Overview
Problem
Current methods for determining auxiliary takeoff trajectories for aircraft are labor-intensive, expensive, and do not account for engine breakdowns occurring at speeds higher than the decision speed, leading to potential collision risks with obstacles.
Innovation Solution
An automated method and device that generate initial data for standard and auxiliary takeoff trajectories, optimizing the auxiliary trajectory to consider various takeoff conditions, including non-nominal weather and aerodynamic configurations, and engine breakdowns at different speeds, using genetic algorithms to determine an optimum trajectory that maximizes takeoff weight while ensuring obstacle clearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an automated method using genetic algorithms is used to determine auxiliary takeoff trajectories, then operator workload is reduced and computation time is decreased, but the complexity of the determination system increases
Solution Approach 1:
The patent replaces the manual mechanical process of trajectory determination with an automated computer-based system using genetic algorithms. The processing means automatically generate and optimize takeoff trajectories by simulating evolutionary processes, substituting human operators with an algorithmic system that can evaluate multiple trajectories simultaneously and identify optimal solutions without manual intervention.
Solution Approach 2:
The determination system performs self-optimization through the genetic algorithm, which automatically generates, evaluates, and refines trajectory solutions without external human input. The system serves itself by using the performance criteria (obstacle clearance, takeoff weight maximization) to automatically select and combine successful trajectory characteristics across generations, eliminating the need for continuous human guidance.
2Reliability
If the auxiliary takeoff trajectory is optimized only for engine breakdown at decision speed, then the determination process is simpler, but safety is compromised for breakdowns occurring at higher speeds
Solution Approach 1:
The patent changes the optimization parameters to include multiple breakdown scenarios at different speeds rather than a single decision speed. The genetic algorithm evaluates trajectories under varying speed conditions, adjusting the performance criteria to account for different engine failure moments. This allows the system to generate trajectories that satisfy safety requirements across the full range of possible breakdown speeds, not just the nominal decision speed.
Solution Approach 2:
The system performs preliminary optimization for multiple breakdown scenarios simultaneously during the trajectory generation phase. By anticipating various engine failure conditions and incorporating them into the optimization criteria from the start, the system pre-calculates trajectories that are robust against speed variations. This preliminary multi-scenario optimization eliminates the need for separate deterministic calculations for each breakdown case.
3Weight of moving object
If a higher maximum takeoff weight is achieved using auxiliary trajectory, then operational flexibility is improved, but the risk of collision with obstacles increases if breakdown occurs after divergence point
Solution Approach 1:
The patent implements feedback loops where the genetic algorithm continuously evaluates trajectory performance against obstacle clearance criteria at multiple points along the flight path. The system monitors whether trajectories generated for maximum weight also satisfy safety margins at all locations, including after the divergence point. If collisions are detected, the algorithm adjusts the trajectory parameters in subsequent generations, creating a feedback mechanism that simultaneously optimizes weight and safety.
Solution Approach 2:
The system dynamically adjusts trajectory characteristics based on the breakdown scenario being evaluated. Rather than using a fixed trajectory, the genetic algorithm generates adaptive trajectories that modify their lateral and vertical profiles according to the specific engine failure condition. This dynamic optimization ensures that the trajectory provides adequate obstacle clearance for the actual breakdown speed and location while still enabling maximum takeoff weight operation.
Data Source
AI summary
A trajectory analysis device automatically determines an auxiliary takeoff trajectory including a curvilinear lateral profile, which allows to maximize the takeoff weight of the aircraft. To this end, the device includes an initial data generation device, an auxiliary takeoff trajectory determination device, and a display device. The crew of the aircraft may then review the optimized auxiliary takeoff trajectory.