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  Transitions in a noisy birhythmic vibro-impact oscillator with improved memory damping regime

Wang, D., Pei, H., Xu, W., Yao, J., Hellmann, F., Kurths, J. (2022): Transitions in a noisy birhythmic vibro-impact oscillator with improved memory damping regime. - Nonlinear Dynamics, 108, 2, 1045-1070.
https://doi.org/10.1007/s11071-022-07261-5

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 Creators:
Wang, Deli1, Author
Pei, Haiqing1, Author
Xu, Wei1, Author
Yao, Jitao1, Author
Hellmann, Frank2, Author              
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: We study the critical transition problem for a stochastically forced birhythmic vibro-impact (BVI) model with improved memory damping regime (IMDR) effect. This paper then reports a detailed probabilistic description on the random distribution of the oscillator response. Analytical criteria for critical transitions are obtained successively relying on the amplitude probability, the most probable amplitude (MPA), the joint probability, its cross-sectional view, and the contour projection, which can complement each other. Besides, numerical simulations are carried out to check the theoretical evaluation results. Detect the control parameter source inducing the transition between monorhythmicity and birhythmicity, also indicating the tipping interval of stochastic P-bifurcation and the critical evolution of the half-shaped stochastic attractor mode determined by the vibro-impact constraint, which can also be visually displayed by the intermittent behavior of time history responses. The alternation of feedback gains design of IMDR can subvert the utility direction of transitions caused by some control parameters. Shannon entropy measure is added to display a quantitative indication of the reference interval for the parameters triggering bifurcations, so as to lock effectively in the rhythm mode required by engineering application issues.

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Language(s): eng - English
 Dates: 2022-02-142022-04
 Publication Status: Finally published
 Pages: 26
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-022-07261-5
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 108 (2) Sequence Number: - Start / End Page: 1045 - 1070 Identifier: Other: 1573-269X
ISSN: 0924-090X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer