Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 185, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2022.109782
Keywords
Aeroelastic systems; Flutter; Hybrid uncertainties; Probability density function; Interval
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This paper proposes a novel numerical method for flutter stability analysis of aeroelastic systems considering hybrid uncertain parameters. The interval-based probability density evolution method is introduced to predict the probability density function of the lower and upper bound of generalized eigenvalues, and the stability of aeroelastic systems with hybrid uncertain parameters is analyzed based on the probability distribution.
Uncertainties are usually ubiquitous and of different sources, and especially hybrid uncertainties widely exist in the aeroelastic system including external loads and material properties. In this context, a novel numerical method for flutter stability analysis of aeroelastic systems considering hybrid uncertain parameters is proposed. Uncertain parameters in aeroelastic systems are char-acterized by stochastic and interval model, and the flutter analysis model with hybrid uncertain parameters is established. The interval-based probability density evolution method is then developed to capture the probability density function of the lower and upper bound of general-ized eigenvalues. By introducing the virtual time parameter, the probability density evolution equation can be transformed into a standard form. Using the finite difference method and the total variation diminishing (TVD) scheme, the probability density function as well as the second -order statistical quantities of the lower and upper bound of the maximum real part of generalized eigenvalues are predicted efficiently. Thus, the stability of aeroelastic systems with hybrid un-certain parameters can be analyzed based on the probability distribution of the interval bound of the maximum real part of generalized eigenvalues. Two numerical examples demonstrate that the proposed method yields results consistent with Monte-Carlo simulation method and improve the computational efficiency.
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