KEYWORDS: Sensors, Actuators, Frequency response, Simulations, Wave propagation, Reflection, Data modeling, Structural health monitoring, Waveguides, Finite element methods
Ultrasonic Guided Waves (UGWs) are particularly suited for monitoring applications as their high frequency allows them to interact with small defects while traveling long distances. For defect localization in plate structures, Lamb waves are generated and exploited in the UGW sense. While data-driven methods, exclusively driven from the collected time series have proven adept for various damage identification tasks, a more refined characterization calls for additional use of physics-based models. In this work, we demonstrate efficient fusion of UGW data with numerical models of plate structures, which are obtained from high-fidelity spectral element simulations. A major bottleneck associated with such a hybrid modeling scheme lies in the excessive computational cost associated with simulations of high–frequency Lamb waves through plate structures. This is due to their short wavelength and short period, which demands a fine discretization in both space and time. To avoid repeated evaluations of prohibitively expensive computational models, model order reduction methods or surrogates can be adopted. A surrogate model should be based on mechanical information, to reduce the amount of training data required. For practical reasons, surrogate models should further be flexible, allowing for assimilation of multiple defect locations, as well as the simulation of more complex geometrical features, such as rivet holes or boundaries. We show steps toward construction of such a surrogate, which draws its construct from the concept of Frequency Response Functions (FRFs), or in other words, the representation of a system in the frequency domain.
KEYWORDS: Education and training, Sensors, Structural health monitoring, General packet radio service, Data modeling, Frequency response, Actuators, Wave propagation, Waveguides, Ultrasonics, Damage detection
Structural Health Monitoring (SHM) using Ultrasonic Guided Waves (UGWs) offers great potential in detection of minor flaws, due to the employed short wavelengths. A bottleneck in UGWs-based schemes lies in the extensive computational costs for evaluating the associated wave propagation models. Such detailed models form though a necessity to reach higher levels of SHM, e.g. localization and assessment of flaws. Reduced Order Models (ROMs) and surrogate models allow for lowering the substantial numerical costs for SHM applications, especially if they are parameterized with respect to the characteristics of different flaw configurations. Machine Learning (ML) algorithms can be trained for this purpose, however, in the case of black box ML algorithms, this comes with the drawback of the requirement for substantial data availability for the purpose of training. Such, training data, which are typically derived from full order numerical simulations, are computationally costly to obtain. To reduce the amount of training data, known information on the mechanical behavior can be harnessed and inserted into the estimation process. In the present work, a method is introduced that exploits the properties of the interaction of UGWs with flaws in the frequency domain. It can be shown that the frequency domain response is characterized by periodic features that are linked to the flaw location. An ML model based on this knowledge can be trained with less training data. The potential of this approach for damage localization in the context of SHM is illustrated in a simulated example of a composite plate.
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