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Data-driven techniques for the nonlinear dynamics of mechanical structures
Citation Link: https://doi.org/10.15480/882.3055
Publikationstyp
Doctoral Thesis
Date Issued
2020-11
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2020-10-26
Institut
TORE-DOI
TORE-URI
Citation
Technische Universität Hamburg (2020)
In structural vibrations, several modeling approaches have helped to develop better, i.e. safer, less-vibrating, and more controllable designs for machines and structures. However, complex nonlinear vibrations of multi-physics, multi-component, and multi-scale systems still represent challenges to today’s identification and vibration prediction approaches. Particularly, damping and friction can play a crucial role for vibration mitigation while being inherently difficult to characterize, quantify, or even approximate. At the same time, the data sciences have become omnipresent not only in different fields of science but also in society. This thesis introduces a rigorous framework and discusses chances and limitations for using machine learning in complex structural dynamics. Special focus is put on the physical peculiarities of the vibration signals and physics-informed learning. In the context of case studies, new methodologies are presented for several systems ranging from
single-degree-of-freedom oscillators to complete automotive disk brake systems.
single-degree-of-freedom oscillators to complete automotive disk brake systems.
Subjects
Data Science
Nonlinear dynamics
Friction-induced vibrations
Chaos
Signal processing
Vibrations
DDC Class
620: Ingenieurwissenschaften
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Name
2020_11_04_Dissertation_Stender_final.pdf
Size
13.58 MB
Format
Adobe PDF