Deep Learning Aircraft Noise Prediction System
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Solution Overview
Problem
Current methods for predicting maximum sound pressure levels (LAmax) from aircraft noise are either numerically efficient but lack accuracy in capturing weather and configuration effects, or accurate but time-consuming and lacking robust validation, failing to provide real-time, precise noise predictions necessary for compliance with noise regulations.
Innovation Solution
The use of deep learning models trained with historical aircraft sensor data, atmospheric data, and sound data from microphones along flight paths to predict sequential maximum sound pressure levels in real-time, allowing for accurate noise forecasting and adjustment of flight paths to avoid exceeding noise thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional numerically efficient methods are used to predict maximum sound pressure levels, then computational speed is improved, but prediction accuracy deteriorates due to inability to capture weather and configuration effects
Solution Approach 1:
The patent replaces traditional physics-based numerical models with a data-driven deep learning model. The neural network learns complex nonlinear relationships between aircraft parameters, atmospheric conditions, and noise levels from historical data, eliminating the need for computationally intensive physics calculations while achieving superior prediction accuracy that captures weather and configuration effects.
Solution Approach 2:
The patent transforms the prediction approach by changing from fixed physics-based parameters to adaptive learned parameters. The deep learning model dynamically adjusts its internal parameters (weights and biases) based on training data, allowing it to capture complex interactions between atmospheric conditions, aircraft configurations, and noise generation that traditional models miss.
2Measurement precision
If accurate physics-based models are used to predict maximum sound pressure levels, then prediction accuracy is improved, but computational time increases making real-time prediction impossible
Solution Approach 1:
The patent performs preliminary action by training the deep learning model offline using extensive historical data before deployment. During real-time operation, the pre-trained model provides rapid predictions without requiring computationally intensive calculations, thus achieving both high accuracy and real-time performance.
Solution Approach 2:
The patent substitutes complex physics-based computational models with a lightweight neural network that has learned the underlying patterns from data. This substitution maintains prediction accuracy while reducing computational complexity to enable real-time processing.
3Productivity
If simple noise prediction models are used, then computational efficiency is improved, but reliability deteriorates due to lack of robust validation
Solution Approach 1:
The patent implements comprehensive feedback mechanisms during model training and validation. Multiple validation datasets, cross-validation techniques, and performance metrics provide continuous feedback to assess model reliability. The model is rigorously tested against held-out data and compared against traditional models to ensure robust validation before deployment.
Data Source
AI summary
A method, apparatus, system, and computer program product for predicting sequential maximum sound pressure levels generated by an aircraft. A first set of sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along a flight path during a flight of the aircraft using the flight path is identified. A second set of sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location is predicted. Predicting the second set of sequential maximum sound pressure levels using the set of deep learning models after training the set of deep learning models using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location.