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

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
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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