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Title: Bayesian analysis of the flutter margin method in aeroelasticity

A Bayesian statistical framework is presented for Zimmerman and Weissenburger flutter margin method which considers the uncertainties in aeroelastic modal parameters. The proposed methodology overcomes the limitations of the previously developed least-square based estimation technique which relies on the Gaussian approximation of the flutter margin probability density function (pdf). Using the measured free-decay responses at subcritical (preflutter) airspeeds, the joint non-Gaussain posterior pdf of the modal parameters is sampled using the Metropolis–Hastings (MH) Markov chain Monte Carlo (MCMC) algorithm. The posterior MCMC samples of the modal parameters are then used to obtain the flutter margin pdfs and finally the flutter speed pdf. The usefulness of the Bayesian flutter margin method is demonstrated using synthetic data generated from a two-degree-of-freedom pitch-plunge aeroelastic model. The robustness of the statistical framework is demonstrated using different sets of measurement data. In conclusion, it will be shown that the probabilistic (Bayesian) approach reduces the number of test points required in providing a flutter speed estimate for a given accuracy and precision.
 [1] ;  [2] ;  [3]
  1. Carleton Univ., Ottawa, ON (Canada); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Royal Military College of Canada, Kingston, ON (Canada)
  3. Carleton Univ., Ottawa, ON (Canada)
Publication Date:
Report Number(s):
Journal ID: ISSN 0022-460X; 640508; TRN: US1700147
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Sound and Vibration
Additional Journal Information:
Journal Name: Journal of Sound and Vibration; Journal ID: ISSN 0022-460X
Research Org:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; aeroelasticity; coalescence flutter; Bayesian estimation; Metropolis-Hastings algorithm; non-Gaussian; parameter estimation
OSTI Identifier: