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Item type:Publication, Access status: with files , Towards a network perspective on the nonlinear dynamics of mechanical structures(2026); The nonlinear dynamics of mechanical structures present significant challenges in engineering today, particularly as these structures become more intricate and environmental conditions grow increasingly unpredictable. In this work, different perspectives on the dynamics of mechanical structures are explored and compared. The balance between interpretability and generalizability is studied in the context of classical, physics-based approaches, data-driven methods and new network-based techniques. Network science offers novel views by interpreting mechanical systems as networks composed of nodes and edges. This thesis focusses on the applicability and potential of network-based methods for analyzing mechanical structures.Publicationtype: Doctoral Thesis70 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Exploring localization in nonlinear oscillator systems through network-based predictionsLocalized vibrations, arising from nonlinearities or symmetry breaking, pose a challenge in engineering, as the resulting high-amplitude vibrations may result in component failure due to fatigue. During operation, the emergence of localization is difficult to predict, partly because of changing parameters over the life cycle of a system. This work proposes a novel, network-based approach to detect an imminent localized vibration. Synthetic measurement data are used to generate a functional network, which captures the dynamic interplay of the machine parts, complementary to their geometric coupling. Analysis of these functional networks reveals an impending localized vibration and its location. The method is demonstrated using a model system for a bladed disk, a ring composed of coupled nonlinear Duffing oscillators. Results indicate that the proposed method is robust against small parameter uncertainties, added measurement noise, and the length of the measurement data samples. The source code for this work is available at C. Geier [(2024). “Code for paper Exploring localization in nonlinear oscillator systems through network-based predictions,” Zenodo. https://doi.org/10.5281/zenodo.12611988].Publicationtype: Journal Article19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Building functional networks for complex response analysis in systems of coupled nonlinear oscillatorsSome 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.Publicationtype: Journal Article22 62