Estimating experimental dispersion curves from steady-state frequency response measurements

Vijaya V.N.Sriram Malladi*, Mohammad I. Albakri, Manu Krishnan, Serkan Gugercin, Pablo A. Tarazaga

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Dispersion curves characterize the frequency dependence of the phase and the group velocities of propagating elastic waves. Many analytical and numerical techniques produce dispersion curves from physics-based models. However, it is often challenging to accurately model engineering structures with intricate geometric features and inhomogeneous material properties. For such cases, this paper proposes a novel method to estimate group velocities from experimental data-driven models. Experimental frequency response functions (FRFs) are used to develop data-driven models, which are then used to estimate dispersion curves. The advantages of this approach over other traditionally used transient techniques stem from the need to conduct only steady-state experiments. In comparison, transient experiments often need a higher-sampling rate for wave-propagation applications and are more susceptible to noise. The vector-fitting (VF) algorithm is adopted to develop data-driven models from experimental in-plane and out-of-plane FRFs of a one-dimensional structure. The quality of the corresponding data-driven estimates is evaluated using an analytical Timoshenko beam as a baseline. The data-driven model (using the out-of-plane FRFs) estimates the anti-symmetric (A0) group velocity with a maximum error of 4% over a 40 kHz frequency band. In contrast, group velocities estimated from transient experiments resulted in a maximum error of 6% over the same frequency band.

Original languageEnglish
Article number108218
Number of pages14
JournalMechanical Systems and Signal Processing
Volume164
DOIs
Publication statusPublished - 1 Feb 2022
Externally publishedYes

Keywords

  • Data-driven models
  • Dispersion curves
  • Least-squares
  • Longitudinal and flexural models
  • Vector-fitting algorithm

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