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Excitation and Analysis Technique for Flight Flutter Tests

Excitation and Analysis Technique for Flight Flutter Tests
Author: G. Haidl
Publisher:
Total Pages: 0
Release: 1979
Genre: Aeroelasticity
ISBN:
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This paper presents a survey of excitation methods applied recently for flight flutter testing. Examples of excitation by frequency sweep, pseudo-random, harmonic oscillation and control feedback technique are given and their effectiveness and adaption to digital processing is discussed. Experience with generating aerodynamic forces by control-surfaces or additional vanes is presented. The second part of the paper deals with the digital analysis of flight flutter test data. Recommendations for selection of analysis parameters and how to avoid errors due to digital processing are given. For data evaluation in flight flutter tests the autopower-spectrum and transfer- and coherence function are used. Errors and effects of digital blockwise computation and analysis procedures like block overlapping, windowing, averaging or curve fitting are demonstrated. The filter correlation - and the modal analysis technique are applied for mode separation and damping evaluation based on the above mentioned functions. Practical experiences and examples from wind tunnel, flight and laboratory tests are discussed. In addition an on-line computer program is presented for realtime calculation of resonance frequencies and damping factors. (Author).

Advances in Flight Flutter Testing Techniques

Advances in Flight Flutter Testing Techniques
Author: Asim Abbasi
Publisher: LAP Lambert Academic Publishing
Total Pages: 248
Release: 2011-04
Genre:
ISBN: 9783844330786
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Flight flutter testing is mandatory for aircraft certification; however it is a risky, lengthy and costly process with ever-growing demand for shorter and cheaper as well as online testing tools. This research has dealt with two key issues and the methods developed have been validated on simulated aeroelastic data sets. Firstly, the flutter boundaries are evaluated using statistical confidence criteria based on Least Squares statistics and eigenvalue perturbation theory rooting from noise statistics in the response data from a single flight test outdoing the need for Monte Carlo simulations or repeated tests. The approach is employed on three different flutter prediction tools: Damping Extrapolation method, Flutter Margin method and Envelope Function method. Results from the first two methods showed widening of the confidence bounds with increasing noise whilst the third method remained robust to it. The other research problem involves the development of an online flutter prediction tool using the Envelope Function method. The original approach is extended by making direct use of the chirp response to predict the flutter speed and is applicable to both offline and online versions.

Application of Advanced Parameter Identification Methods for Flight Flutter Data Analysis with Comparisons to Current Techniques

Application of Advanced Parameter Identification Methods for Flight Flutter Data Analysis with Comparisons to Current Techniques
Author: H. J. Perangelo
Publisher:
Total Pages: 29
Release: 1984
Genre:
ISBN:
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Grumman has been pursuing the implementation and evaluation of advanced parameter identification software for use in flutter test data processing operations as its Automated Telemetry Station. They have been motivated by aircraft design tending toward thin, lightweight aircraft structures, which make it difficult to use authoritative shaker systems, and the continuing development of high-sped digital computer technology. This development activity is aimed at establishing an on-line processing capability, in the 1985 time frame, that will initially use the maximum likelihood parameter identification algorithm in conjunction with a detailed physical aeroelastic aircraft model to perform optimal flutter test data analysis. Extended Kalman filtering is being considered for eventual use as a second advanced parameter identification method. A mathematical description of the advanced parameter identification approach and Grumman's current least-squares flutter analysis procedures are presented. A comparison between this current analysis capability and prototype code for the maximum likelihood parameter identification algorithm on response data excited randomly (via atmospheric turbulence) and by swept frequency shaker inputs indicates a significant improvement in analysis results with the advanced method.