Modelling of Pencil-Lead Break Acoustic Emission Sources using the Time Reversal Technique

Falcetelli, Francesco (2018) Modelling of Pencil-Lead Break Acoustic Emission Sources using the Time Reversal Technique. [Laurea magistrale], Università di Bologna, Corso di Studio in Aerospace engineering / ingegneria aerospaziale [LM-DM270] - Forli'
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Abstract

In Acoustic Emissions (AE), Hsu-Nielsen Pencil-Lead Breaks (PLB) are used to generate sound waves enabling the characterization of acoustic wave speed in complex structures. The broadband signal of a PLB represents a repeatable emission, which can be applied at different regions of the structure, and therefore can be used to calibrate the localization algorithms of the AE system. In recent years, the use of Finite Element Method (FEM) has flourished for modelling acoustic Lamb wave propagation, which is present in thin plate-like structures. The primary challenge faced by the AE community is the lack of a well-known mathematical function of a PLB signal that can be applied in numerical simulations. This study makes use of a Time Reversal (TR) approach to identify the emission source of the PLB on a 7075-T651 aluminum plate. An ABAQUS CAETM model with piezoelectric actuators and sensors was developed. In order to avoid edge reflections, absorbing boundaries based on the Stiffness Reduction Method (SRM) were considered. The captured PLB signals were used as input to the FEM and was time-reversed. Furthermore, a band-limited white noise signal was used to calibrate the contribution of the broadband frequencies found in the transmitted wave packet. Preliminary results indicate that the TR approach can be used to understand the shape and function of the original transmitted signal.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Falcetelli, Francesco
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Acoustic emission, Lamb waves, time reversal, piezoelectric transducer, structural health monitoring
Data di discussione della Tesi
4 Ottobre 2018
URI

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