Closed Form Shape Fit for Flutter Test Data Analysis
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Solution Overview
Problem
Current methods for analyzing flutter test data in aircraft structures are computationally inefficient, requiring extensive time and resources, which can disrupt flight tests and hinder real-time analysis of aeroelastic stability margins.
Innovation Solution
The implementation of a system identification method using a closed form shape fit and nonlinear optimization, which breaks down the analysis into smaller, independent problems for each sensor, allowing for faster computation of mode shapes and frequency damping, and incorporates a Fast Fourier Transform to optimize mode shape terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used to analyze flutter test data, then analysis accuracy is maintained, but analysis time is excessively long causing flight test interruptions
Solution Approach 1:
The patent divides the computational analysis into two independent stages: (1) frequency and damping identification using a subset of sensor data, and (2) mode shape determination using the identified parameters and remaining sensor data. This segmentation allows frequency-damping analysis to be completed quickly, with mode shape analysis performed subsequently, significantly reducing overall analysis time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary identification of frequency and damping parameters using a selected subset of sensor data before conducting the full mode shape analysis. This preliminary action establishes key modal parameters that are then used to guide the subsequent complete analysis, reducing the computational burden of the full analysis while preserving accuracy.
2Loss of information
If comprehensive sensor data from all sensors is processed simultaneously, then complete mode shape information is obtained, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the sensor data processing by first selecting a subset of sensors for frequency-damping identification, then using those results to analyze mode shapes across all sensors. This segmentation reduces the computational complexity of simultaneous multi-sensor analysis while ensuring complete mode shape information is obtained through the two-stage process.
Solution Approach 2:
The patent introduces identified frequency and damping parameters as intermediaries that connect the first stage of analysis (using selected sensors) to the second stage (complete mode shape analysis). These intermediary parameters enable the decomposition of the complex simultaneous analysis into manageable sequential steps.
Data Source
AI summary
System identification from free response time decay history data of a dynamic system employs a new closed form shape fit for solving the dynamic system free response decay equation. The closed form shape fit allows for the treatment of the mode shapes as linear coefficients and allows the mode shapes for each sensor to be computed independently, thereby reducing computation time. The closed form shape fit efficiently provides mode shapes for a large set of sensors based on a fit from a small set of sensors. The closed form shape fit combined with a non-linear fit of frequency and damping characteristics efficiently estimates the optimal solution, thereby reducing the time to completion of non-linear optimization. A nonlinear optimization can incorporate the closed form shape fit into determining the Jacobian matrix of sensitivities and evaluating the residuals, thereby reducing the number of parameters and reducing the time to completion of the non-linear optimization.