Aircraft Flight Path Planning Using Predicted Noise Waypoints
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Solution Overview
Problem
Aircraft noise remains a significant issue for communities near airports due to improved navigation techniques leading to increased overflight frequencies, despite regulatory efforts to control noise levels.
Innovation Solution
A method for generating aircraft flight paths that predicts noise levels at multiple waypoints and uses these predictions to optimize flight paths, reducing ground-level noise through the use of a computing system and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If navigation techniques are improved to increase flight efficiency, then aircraft can overfly communities with increased frequency, but ground-level noise increases
Solution Approach 1:
The system dynamically adjusts flight path parameters (altitude, lateral position, speed) to minimize noise impact on ground communities while maintaining flight efficiency. By changing these parameters in real-time based on predicted noise levels, the system resolves the contradiction between efficient routing and noise reduction.
Solution Approach 2:
The system uses predicted noise level feedback from machine learning models to continuously optimize flight paths. The noise predictions are fed back into the path planning algorithm to adjust subsequent routing decisions, creating a closed-loop system that balances efficiency and noise reduction.
2Object-generated harmful factors
If flight paths are optimized to reduce noise, then community disturbance decreases, but flight path complexity increases
Solution Approach 1:
The system performs preliminary noise level predictions at multiple waypoints before finalizing the flight path. By pre-calculating noise impacts and identifying optimal routing options in advance, the system reduces community disturbance without requiring complex real-time adjustments during flight.
Solution Approach 2:
The flight path is divided into multiple segments between waypoints, with noise optimization applied to each segment independently. This segmentation allows the system to manage complexity by breaking down the overall path optimization into smaller, more manageable decisions at each waypoint.
3Measurement precision
If noise predictions are calculated at multiple waypoints, then flight path optimization accuracy improves, but computational requirements increase
Solution Approach 1:
The system calculates noise predictions at a selected subset of critical waypoints rather than all possible points along the flight path. This partial action approach maintains sufficient prediction accuracy for effective optimization while significantly reducing the computational energy required compared to exhaustive calculations.
Data Source
AI summary
A method for aircraft flight path generation includes, at a computing system, receiving, for a plurality of waypoints in a geographic area, predicted aircraft noise levels for an aircraft at each waypoint of the plurality of waypoints, the predicted aircraft noise levels predicted based at least in part on a plurality of flight parameters for the aircraft. The predicted aircraft noise levels are input to a flight path prediction system configured to generate a candidate flight path for the aircraft through the geographic area based at least in part on the predicted aircraft noise levels. The candidate flight path is output from the flight path prediction system, wherein the candidate flight path is predicted to result in less ground-level noise when followed by the aircraft as compared to an alternate flight path through the geographic area.