Data-Driven Identification of Rayleigh-Damped Second-Order Systems

Igor Pontes Duff (), Pawan Goyal () and Peter Benner ()
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Igor Pontes Duff: Max Planck Institute for Dynamics of Complex Technical Systems
Pawan Goyal: Max Planck Institute for Dynamics of Complex Technical Systems
Peter Benner: Max Planck Institute for Dynamics of Complex Technical Systems

A chapter in Realization and Model Reduction of Dynamical Systems, 2022, pp 255-272 from Springer

Abstract: Abstract In this paper, we present a data-driven approach to identify second-order systems, having internal Rayleigh damping. This means that the damping matrix is given as a linear combination of the mass and stiffness matrices. These systems typically appear when performing various engineering studies, e.g., vibrational and structural analysis. In an experimental set-up, the frequency response of a system can be measured via various approaches, for instance, by measuring the vibrations using an accelerometer. As a consequence, given frequency samples, the identification of the underlying system relies on rational approximation. To that aim, we propose an identification of the corresponding second-order system, extending the Loewner framework for this class of systems. The efficiency of the proposed method is demonstrated by means of various numerical benchmarks.

Keywords: Data-driven modeling; Second-order systems; Model reduction; Loewner framework; Mechanical systems (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-95157-3_14

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DOI: 10.1007/978-3-030-95157-3_14

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