Aeroelastic Flutter Prediction Using In-Flight Sensor Data
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Solution Overview
Problem
Current methods for predicting aeroelastic flutter in aircraft are impractical and inaccurate, particularly for configurable aircraft, as they rely on wind tunnel testing and finite element analysis, which are not feasible for numerous configurations and can cause wing damage, and do not effectively utilize actual flight data.
Innovation Solution
A method involving flying an aircraft with various payload configurations, acquiring sensor data, training a machine learning predictive model, and using it to predict aeroelastic flutter in new configurations, with a system that includes a processing unit and warning mechanism to alert pilots of impending flutter conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wind tunnel testing is used to determine flutter conditions, then measurement precision is improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent replaces the mechanical wind tunnel testing system with an in-flight sensor-based measurement system. Sensors mounted on the aircraft directly measure vibrational responses during actual flight operations, eliminating the need for complex ground-based wind tunnel infrastructure while maintaining measurement capability.
Solution Approach 2:
The patent uses sensors to capture and record flight data that replicates the information previously obtained only through wind tunnel testing. By copying the essential measurement function to in-flight sensors, the system avoids the complexity of physical wind tunnel facilities while preserving the ability to detect flutter conditions.
2Measurement precision
If wind tunnel testing is conducted for multiple configurations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent enables continuous data collection across multiple configurations during normal flight operations. Instead of discrete, time-consuming wind tunnel tests for each configuration, the system continuously monitors vibrational responses as configurations change in flight, accumulating data efficiently over time.
Solution Approach 2:
The patent collects and stores flight data from multiple configurations during routine operations, preparing the dataset in advance for later analysis. This preliminary data collection during normal flights eliminates the need for dedicated testing time for each configuration.
3Productivity
If finite element analysis is used to predict flutter, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where actual flight measurement data is used to validate and refine the predictive model. Sensors provide real-world vibrational response data that feeds back into the system, allowing the model to be continuously improved and calibrated against actual observations, thereby increasing accuracy while maintaining computational efficiency.
4Adaptability or versatility
If aircraft are tested in various configurations, then adaptability is improved, but object-generated harmful factors increase
Solution Approach 1:
The patent identifies and flags potential flutter conditions before they can cause damage. By using sensors to detect early signs of unstable oscillations and alerting pilots in advance, the system prevents the development of full-blown flutter that could damage wings, thereby protecting the aircraft while enabling configuration testing.
Data Source
AI summary
Methods and systems for analyzing and predicting aeroelastic flutter on configurable aircraft are disclosed herein. The method may include the steps of: a) flying a known aircraft type above ground, wherein the aircraft has a payload in a known configuration; b) acquiring data from at least one sensor on the aircraft while flying above ground; c) repeating steps a) and b) with a different payload configuration; d) training a machine learning predictive model for the aircraft type for aeroelastic flutter using the collected data; and e) using the predictive model to predict when aeroelastic flutter may occur on the aircraft type when the aircraft has a payload in a new configuration for which data from sensors was not previously collected with the aircraft in flight.