Estimating experimental dispersion curves from steady-state frequency response measurements. (1st February 2022)

• Record Type: Journal Article
• Authors: Malladi, Vijaya V.N. Sriram; Albakri, Mohammad I.; Krishnan, Manu; Gugercin, Serkan; Tarazaga, Pablo A.
• Is Part Of: Mechanical systems and signal processing. Volume 164(2022)
• Publication Date: 2022-02-01
• Subjects: Data-driven models -- Dispersion curves -- Least-squares -- Vector-fitting algorithm -- Longitudinal and flexural models; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108218
• Languages: English
• ISSNs: 0888-3270

Abstract

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 ( A 0 ) 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. Highlights: Experimental validation of data-driven approach to estimate dispersion curves from experimental frequency response functions. Discussion of practical implementation of the Vector Fitting algorithm over a wide frequency band details. Challenges of practical wave-modes etc in a practical application. Superiority to classical transient approach.