Reduced-order model-inspired system identification of geometrically nonlinear structures: application to a nonlinear cantilever-type structure

Abstract In the field of structural dynamics, system identification usually refers to building mathematical models from an experimentally obtained data set. To build reliable models using the measurement data, the mathematical model must be representative of the structure. In this work, attention is given to robust identification of geometrically nonlinear structures, particularly those with large inertial effects. We draw inspiration from reduced-order modelling to determine a suitable model for the system identification. There are large similarities between reduced-order modelling and system identification fields, i.e. both are used to replicate the dynamics of a system using a mathematical model with low complexity. Reduced-order models (ROMs) can accurately capture the physics of a system with a low number of degrees of freedom; thus, in system identification, a model based on the form of a ROM is potentially more robust. Nonlinear system identification of a structure is presented, where inspiration is taken from a novel ROM to form the model. A finite-element model of the structure is built to simulate an experiment, and the identification is performed. It is shown how the ROM-inspired model in the system identification improves the accuracy of the predicted response, in comparison with a standard nonlinear model. As the data are gathered from simulations, system identification is first demonstrated on the high-fidelity data, and then, the fidelity of data is reduced to represent a more realistic experiment. A good response agreement is achieved when using the ROM-inspired model, which accounts for the kinetic energy of un-modelled modes. The estimated parameters of this model are also shown to be more robust and rely on the underlying physics of the system..

Medienart:	Artikel

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	Zur Gesamtaufnahme - volume:111
Enthalten in:	Nonlinear dynamics - 111(2023), 19 vom: 21. Aug., Seite 17887-17907

Sprache:	Englisch

Beteiligte Personen:	Ahmadi, M. Wasi [VerfasserIn] Hill, Thomas L. [VerfasserIn] Jiang, Jason Z. [VerfasserIn] Neild, Simon A. [VerfasserIn]

Links:	Volltext [lizenzpflichtig]

Themen:	Model uncertainty Nonlinear dynamics Reduced-order modelling Structural dynamics System identification

Anmerkungen:	© The Author(s) 2023

doi:	10.1007/s11071-023-08813-z

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	OLC2145309047