Aircraft Wind Load Estimation Using Neural Networks
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Solution Overview
Problem
Current methods for identifying wind loads on aircraft are either costly due to sensor installation, weight-increasing hardware, or inaccurately rely on statistical models that may lead to unnecessary maintenance, as they cannot definitively confirm wind encounters during flight.
Innovation Solution
A load estimation system utilizing a wind load analyzer that receives aircraft parameter data from flight recorders to estimate wind loads on aerodynamic structures through pattern recognition and neural network analysis, eliminating the need for sensors and improving accuracy by directly correlating flight data with load patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed to directly measure wind loads on aircraft, then measurement precision is improved, but device complexity and weight increase
Solution Approach 1:
The patent creates a computational model that copies the physical behavior of wind loads on aircraft by using neural networks trained on flight data. Instead of physically installing sensors to measure loads, the system creates a virtual copy of the load estimation process through software algorithms that analyze existing flight recorder data to predict wind load conditions.
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with an information-processing system. Rather than using physical sensors to detect wind loads, the invention uses neural networks and flight data analysis to computationally estimate loads, substituting mechanical detection with algorithmic prediction.
2Device complexity
If statistical models are used to estimate wind loads, then device complexity is reduced, but measurement precision deteriorates leading to unnecessary maintenance
Solution Approach 1:
The patent transforms the input parameters for load estimation by using actual flight recorder data (accelerations, attitudes, flight conditions) rather than relying on statistical assumptions. The neural network learns optimal parameter relationships from training data, dynamically adjusting how flight parameters are weighted and combined to estimate wind loads with higher precision.
Solution Approach 2:
The system implements feedback through neural network training where the model continuously improves its load estimation accuracy by learning from historical flight data. The trained network provides feedback mechanisms that refine parameter relationships, allowing the system to adapt and improve measurement precision while maintaining computational efficiency.
3Weight of moving object
If flight data is analyzed without sensors to estimate wind loads, then weight and cost are reduced, but reliability of load identification deteriorates
Solution Approach 1:
The patent makes existing flight recorder data serve multiple functions. The same data that records flight operations for other purposes is also utilized for wind load estimation, eliminating the need for dedicated sensors. This multi-functional use of existing data infrastructure maintains reliability while avoiding additional weight and cost.
Solution Approach 2:
The system enables flight data to serve itself by using the aircraft's own operational data to estimate loads it encountered. The flight recorder data inherently contains information about aircraft response to wind conditions, and the neural network extracts this self-contained information without requiring external measurement devices.
Data Source
AI summary
A method and apparatus for identifying loads caused by wind. Information for a group of aircraft parameters recorded by an information recorder is received in an aircraft during operation of the aircraft. A number of loads on an aerodynamic structure of the aircraft is estimated using the information for the group of aircraft parameters.