Stochastic Characterization of Flutter Using Historical Wind Tunnel Data
Author(s) -
Jennifer Heeg
Publication year - 2007
Publication title -
54th aiaa/asme/asce/ahs/asc structures, structural dynamics, and materials conference
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2007-1769
Subject(s) - flutter , aeroelasticity , modal , margin (machine learning) , wind tunnel , computer science , modal analysis , work (physics) , control theory (sociology) , vibration , engineering , acoustics , aerodynamics , aerospace engineering , machine learning , artificial intelligence , physics , mechanical engineering , chemistry , control (management) , polymer chemistry
Methods for predicting the onset of flutter during an experiment are traditionally applied treating the data as deterministic values. Uncertainty and variation in the data is often glossed over by using best-fit curves to represent the information. This paper applies stochastic treatments to wind tunnel data obtained for the Piezoelectric Aeroelastic Response Tailoring Investigation model. These methods include modal amplitude tracking, modal frequency tracking and several applications of the flutter margin method. The flutter margin method was developed by Zimmerman and Weissenburger, and extended by Poirel, Dunn and Porter to incorporate uncertainty. Much of the current work follows the future work recommendations of Poirel, Dunn and Porter.
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