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Building functional networks for complex response analysis in systems of coupled nonlinear oscillators
Citation Link: https://doi.org/10.15480/882.9659
Publikationstyp
Journal Article
Date Issued
2024-05-30
Sprache
English
TORE-DOI
Journal
Volume
590
Article Number
118544
Citation
Journal of Sound and Vibration 590: 118544 (2024)
Publisher DOI
Scopus ID
Publisher
Elsevier BV
Peer Reviewed
true
Some aspects of engineering dynamics, such as nonlinearities and transient motion of many interconnected parts, remain difficult to handle today. To comply with increasing demands on resilience and safety, the dynamics of large machines need to be better understood. Complex network methods, already present in many scientific disciplines, provide a tool set complementary to conventional methods of system analysis. This work aims at providing a new, function-based view on mechanical systems by generating functional networks. To this end, a network algorithm is applied to sets of cyclically coupled Duffing oscillators as a common example of a complex nonlinear mechanical system. In the functional network, each node represents an oscillator while the direction of the network edges represents a functional coupling. Results show that the network method is capable of identifying dynamical transitions and synchronization between components, as well as determining the number of different states present within a system. Additionally, the time evolution of the component interactions, especially in response to a disturbance, is studied via a sliding-window approach. The results of this analysis might hopefully open new ways for a more efficient system analysis through optional sensor placement, and for effective countermeasures against unwanted dynamics through improved analysis of transient dynamics.
Subjects
Complex networks
Duffing oscillators
Nonlinear dynamics
Synchronization
Time series analysis
Transient analysis
DDC Class
620: Engineering
621.8: Machine Engineering
510: Mathematics
Funding Organisations
Publication version
publishedVersion
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Name
Geier2024_FunctionalNetworks_JSV.pdf
Type
Main Article
Size
2.31 MB
Format
Adobe PDF