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• W4292114850 abstract "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 nonlinear structures. 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 to a standard nonlinear model. As the data is gathered from simulations, system identification is first demonstrated on the high fidelity data, 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 unmodelled modes. The estimated parameters of this model are also demonstrated to be more robust and rely on the underlying physics of the system." @default.
• W4292114850 created "2022-08-17" @default.
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• W4292114850 date "2022-08-17" @default.
• W4292114850 modified "2023-09-27" @default.
• W4292114850 title "Reduced order model-inspired system identification of geometrically nonlinear structures" @default.
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• W4292114850 doi "https://doi.org/10.21203/rs.3.rs-1912317/v1" @default.
• W4292114850 hasPublicationYear "2022" @default.
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