Date of Award
2016
Document Type
Open Access Dissertation
Department
Mechanical Engineering
Sub-Department
College of Engineering and Computing
First Advisor
Victor Giurgiutiu
Abstract
Guided waves based damage detection techniques are popular for their ease of generation and detection along with their ability to travel long distances. Accurate and efficient modeling is a key for successful implementation of guided waves for NDE/SHM. However, efficient prediction of scattering from various damage is challenging due to the complex nature of these guided waves.
This dissertation presents as physics based efficient and accurate modeling techniques to predict ultrasonic wave propagation and their interaction with various damage. Detection and characterization of damage in structures can typically be divided into two categories, active and passive. This research is aimed towards detection and characterization of damage in thin walled structures. Therefore, the type of guided wave that we discussed is plate guided waves. For active detection, our focus is to develop an efficient analytical predictive simulation of scattered wave field and extract the damage characteristics based on physics of Lamb wave propagation. For passive characterization, our focus is on detection of acoustic emission caused by fatigue-crack growth. The scope of this research is to develop a predictive simulation method for acoustic emission signals and extract the damage related information from acoustic emission signals based on physics of material. This approach is in contrast with the traditional approach involving statistics of acoustic emissions and their relation with damage criticality. We present our unique method to extract fatigue crack length information from acoustic emission signals recorded during fatigue crack growth.
Rights
© 2016, Banibrata Poddar
Recommended Citation
Poddar, B.(2016). Physics Based Modeling Of Guided Waves For Detection And Characterization Of Structural Damage In NDE and SHM. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/3923