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    Numerical modeling with experimental verification investigating the effect of various nonlinearities on the sideband peak count-index technique

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    1-s2.0-S0041624X24000210-main.pdf (8.175Mb)
    Date
    2024-03-31
    Author
    Zhang, Guangdong
    Hu, Bo
    Alnuaimi, Hamad
    Amjad, Umar
    Kundu, Tribikram
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    Abstract
    A newly developed nonlinear ultrasonic (NLU) technique called sideband band peak count-index (or SPC-I) measures the degree of nonlinearity in materials by counting the sideband peaks above a moving threshold line – larger the SPC-I values, higher is the material nonlinearity. In various published papers, the SPC-I technique has shown its effectiveness in structural health monitoring (SHM) applications. However, the effects of different types of nonlinear phenomenon on the sideband peak generation is yet to be investigated in depth. This work addresses this knowledge gap and investigates the effects of different types of nonlinearity on the SPC-I technique. Three types of nonlinearity (material nonlinearity, structural nonlinearity and contact nonlinearity) are investigated separately through numerical modeling. In this investigation the material nonlinearity and the contact nonlinearity are modeled by finite element method (FEM) using the commercial Abaqus/CAE software. The structural nonlinearity arising from stationary cracks is modeled using nonlocal peridynamics based peri-ultrasound modeling technique. Numerical modeling shows that the sideband peak values do not increase proportional to the input signal strength thus indicating nonlinear response, and different types of nonlinearities affect the SPC-I measurements differently. For the experimental verification a composite plate with impact-induced damage is considered for investigating the material nonlinearity and structural nonlinearity while a linear elastic aluminum plate is used to examine the contact nonlinearity between the transducers and the plate. The trends observed in the experimental observations matched with the numerical model predictions.
    URI
    https://www.sciencedirect.com/science/article/pii/S0041624X24000210
    DOI/handle
    http://dx.doi.org/10.1016/j.ultras.2024.107259
    http://hdl.handle.net/10576/64339
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    • Center for Advanced Materials Research [‎1482‎ items ]
    • Civil and Environmental Engineering [‎862‎ items ]

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