A Bayesian statistical inference-based flutter boundary prediction method under turbulence excitation

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (12) : 283-289.

WANG Ruochan1, YAN Gang1, LI Yang2, SHI Yuan1, ZHOU Daheng1

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Abstract

This paper proposes to extend Bayesian statistical inference method to flutter analysis for aircraft structures under excitation of atmospheric turbulence to consider the uncertainties in modal parameter identification and flutter boundary prediction. After the free decay responses are extracted from the structural responses under the excitation of atmospheric turbulence by natural excitation technique (NExT), based on Bayesian statistical inference, the posterior probability density functions of structural modal parameters are sampled and identified by Markov chain Monte Carlo (MCMC) algorithm. Then the probability density distribution for flutter velocity is obtained by Z-W flutter margin method to predict the flutter boundary and analyze the associated uncertainties. Numerical simulation studies are carried out to analyze the structural response data excited by atmospheric turbulence, and the effectiveness of the proposed method is verified.

Key words

Flutter boundary prediction / turbulence excitation / flutter margin method / Bayesian statistical inference / Markov chain Monte Carlo algorithm

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WANG Ruochan1, YAN Gang1, LI Yang2, SHI Yuan1, ZHOU Daheng1. A Bayesian statistical inference-based flutter boundary prediction method under turbulence excitation[J]. Journal of Vibration and Shock, 2023, 42(12): 283-289

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