Non-stationary response autocorrelation function of nonlinear oscillators endowed with fractional derivative elements
Citation:
Vasileios Fragkoulis, Ioannis Kougioumtzoglou, Athanasios Pantelous, Michael Beer, Non-stationary response autocorrelation function of nonlinear oscillators endowed with fractional derivative elements, 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), Dublin, Ireland, 2023.Download Item:

Abstract:
An approximate analytical technique is developed for determining the non-stationary response autocorrelation function of nonlinear oscillators endowed with fractional derivative elements. Specifically, first, a stochastic averaging/linearization treatment is employed [1] for deriving an input-output relationship in the joint time-frequency domain. The derived relationship between the excitation and the response evolutionary power spectra (EPS) can be construed as an extension of earlier results in the literature [2] to account for fractional derivative elements in the oscillator equation of motion. Further, the non-stationary response autocorrelation function can be readily determined based on an approximate approach proposed in [3]. A nonlinear oscillator with fractional derivative elements and subjected to non-stationary stochastic excitation with an EPS is considered as a numerical example. The reliability of the developed technique is demonstrated by comparisons with pertinent Monte Carlo simulation data.
REFERENCES
[1] Fragkoulis, V. C., Kougioumtzoglou, I. A., Pantelous, A. A. and Beer, M., 2019. Non-stationary response statistics of nonlinear oscillators with fractional derivative elements under evolutionary stochastic excitation. Nonlinear Dynamics, 97(4), 2291-2303.
[2] Kougioumtzoglou, I. A., 2013. Stochastic joint timeヨfrequency response analysis of nonlinear structural systems. Journal of sound and Vibration, 332, 7153-7173.
[3] Benowitz, B. A., Shields, M. D., Deodatis, G., 2015. Determining evolutionary spectra from non-stationary autocorrelation functions. Probabilistic Engineering Mechanics, 41, 73-78.
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