Aircraft Performance Model for Real-Time Flight Parameter Estimation
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Solution Overview
Problem
Aircraft performance parameters, such as drag and lift, are typically calculated using time-intensive methods that are not available in real-time, limiting pilots' ability to make informed flight decisions during operations.
Innovation Solution
A system utilizing an on-board computing device with a processor, memory, and network interface that stores an aircraft performance model based on historical flight data, including a recurrent neural network layer, to generate real-time performance parameters from real-time time-series flight data received from sensors, and display them to the pilot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional time-intensive calculation methods are used to determine aircraft performance parameters, then calculation accuracy is maintained, but real-time availability during flight is lost
Solution Approach 1:
The system performs preliminary actions by training the neural network model offline using historical flight data before actual flight operations. The model learns complex aerodynamic relationships in advance, enabling real-time predictions during flight without requiring intensive calculations at that moment.
Solution Approach 2:
The patent replaces traditional mechanical calculation methods (time-intensive numerical computations) with an intelligent system using neural networks. This substitution enables real-time performance parameter determination while maintaining accuracy, as the neural network has already learned the complex aerodynamic relationships during offline training.
2Loss of information
If real-time performance parameters are provided to pilots, then situational awareness and decision-making are improved, but system complexity increases
Solution Approach 1:
The neural network model serves multiple functions: it processes various sensor inputs (aircraft state, environmental conditions), determines multiple performance parameters (drag, lift, thrust, power), and provides real-time predictions. This multi-functionality reduces the need for separate specialized systems for each parameter calculation.
Solution Approach 2:
The neural network acts as an intermediary between raw sensor data and pilot decision-making. It processes complex aerodynamic relationships and transforms multiple sensor inputs into meaningful performance parameters, simplifying the information flow while maintaining accuracy.
Data Source
AI summary
A device includes a memory, a network interface, and a processor. The memory is configured to store an aircraft performance model. The aircraft performance model is based on historical flight data of one or more aircraft. The aircraft performance model includes a recurrent neural network layer. The network interface is configured to receive real-time time-series flight data from a data bus of a first aircraft. The processor is configured to receive, via the network interface, the real-time time-series flight data. The processor is also configured to generate, based on the real-time time-series flight data and the aircraft performance model, one or more aircraft performance parameters. The processor is further configured to provide the aircraft performance parameters to a display device.