TY - JOUR
T1 - Estimating experimental dispersion curves from steady-state frequency response measurements
AU - Malladi, Vijaya V.N.Sriram
AU - Albakri, Mohammad I.
AU - Krishnan, Manu
AU - Gugercin, Serkan
AU - Tarazaga, Pablo A.
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - Data-driven models
KW - Dispersion curves
KW - Least-squares
KW - Longitudinal and flexural models
KW - Vector-fitting algorithm
UR - http://www.scopus.com/inward/record.url?scp=85111342134&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2021.108218
DO - 10.1016/j.ymssp.2021.108218
M3 - Article
AN - SCOPUS:85111342134
SN - 0888-3270
VL - 164
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 108218
ER -