Finite Element Analysis of Periodic Structures and their Application for Structural Health Monitoring

2009 ◽  
Author(s):  
W.J. Zhou ◽  
M.N. Ichchou
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Periodic Structures ◽  
Element Analysis ◽  
Structural Health

scholarly journals Multiphysics Finite Element Analysis of In-Pile Sensors for Advanced Nuclear Reactors

2020 ◽  
Author(s):  
Takoda Linn Bingham
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Structural Health Monitoring ◽  
Acoustic Wave ◽  
Health Monitoring ◽  
Nuclear Reactors ◽  
Laser Doppler Vibrometer ◽  
Acoustic Sensors ◽  
Element Analysis ◽  
Structural Health

Nuclear reactors have large needs for in-pile sensors that are durable in high temperature, radioactive, and corrosive environments. With the use of multiphysics finite element analysis (FEA) researchers can speed up sensor prototyping. FEA also allows for a better fundamental understanding of sensors and enables sensor optimization. This research focuses on three types of in-pile sensors developed at Idaho National Laboratory: acoustic sensors, linear variable differential transformers (LVDT), and capacitance based strain gauges (CSG). Two acoustic sensors, magnetostrictive waveguides and piezoelectric surface acoustic wave (SAW) sensors were first modeled. These models showed the acoustic wave patterns and estimated the speed of sound. The modeling results were compared to results from laser Doppler vibrometer testing. The model was implemented to enhance the performance of the sensor designs. This research then modeled a LVDT sensor used to measure fuel rod deformation and structural health monitoring. A parametric FEA study was completed for the purpose of sensor miniaturization. The FEA model was also used to investigate the potential of adding a fiber optic cable through the LVDT core. This research eventually modeled CSGs used in nondestructive structural health monitoring. Multiphysics models were used to investigate the discrepancies in experiments and previous analytical models.

scholarly journals Direct Calculation of the Group Velocity for Two-Dimensional Complex, Composite and Periodic Structures Using a Wave and Finite Element Scheme

2021 ◽  
Vol 11 (10) ◽  
pp. 4319
Author(s):  
Muhammad Khalid Malik ◽  
Dimitrios Chronopoulos ◽  
Francesco Ciampa
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Group Velocity ◽  
Health Monitoring ◽  
Periodic Structures ◽  
Velocity Profiles ◽  
Guided Wave ◽  
Finite Element Scheme ◽  
Element Scheme ◽  
Structural Health

Guided waves have immense potential for structural health monitoring applications in numerous industries including aerospace. It is necessary to evaluate guided wave dispersion characteristics, i.e., group velocity and phase velocity profiles, for using them effectively. For complex structures, the profiles can have highly irregular shapes. In this work, a direct method for calculating the group velocity profiles for complex, composite, and periodic structures using a wave and finite element scheme is presented. The group velocity calculation technique is easy to implement, highly computationally efficient, and works with the standard finite element formulation. The major contribution is summarised in the form of a comprehensive algorithm for calculating the group velocity profiles. The method is compared with the existing analytical and numerical methods for calculation of dispersion curves. Finally, an experimental study in a multilayered composite plate is conducted and the results are found to be in good agreement. The technique is suitable to be used in all guided wave application areas such as material characterisation, non-destructive testing, and structural health monitoring.

Structural Health Monitoring using deep learning with optimal finite element model generated data

2020 ◽  
Vol 145 ◽  
pp. 106972 ◽  
Author(s):  
Panagiotis Seventekidis ◽  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Olga Markogiannaki
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Element Model ◽  
Structural Health

scholarly journals Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

2020 ◽  
Vol 10 (3) ◽  
pp. 839 ◽  
Author(s):  
Tzu-Kang Lin ◽  
Yu-Ching Chen
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Confusion Matrix ◽  
Sample Entropy ◽  
Ambient Vibrations ◽  
Analysis Software ◽  
Element Analysis ◽  
Structural Health ◽  
Training Samples

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.

scholarly journals Toward identifying crack-length-related resonances in acoustic emission waveforms for structural health monitoring applications

2017 ◽  
Vol 17 (3) ◽  
pp. 577-585 ◽  
Author(s):  
Md Yeasin Bhuiyan ◽  
Jingjing Bao ◽  
Banibrata Poddar ◽  
Victor Giurgiutiu
Keyword(s):  
Finite Element ◽  
Acoustic Emission ◽  
Fatigue Crack ◽  
Structural Health Monitoring ◽  
Crack Growth ◽  
Health Monitoring ◽  
Resonance Phenomenon ◽  
Vibration Modes ◽  
Local Vibration ◽  
Structural Health

In this study, we focus on analyzing the acoustic emission waveforms of the fatigue crack growth despite the conventional statistics-based analysis of acoustic emission. The acoustic emission monitoring technique is a well-known approach in the non-destructive evaluation/structural health monitoring research field. The growth of the fatigue crack causes the acoustic emission in the material that propagates in the structure. The acoustic emission happens not only from the crack growth but also from the interaction of the crack tips during the fatigue loading in the structure. The acoustic emission waveforms are generated from the acoustic emission events; they propagate and create local vibration modes along the crack faces (crack resonance). In-situ fatigue and acoustic emission experiments were conducted to monitor the acoustic emission waveforms from the fatigue cracks. Several test specimens were used in the fatigue experiments, and corresponding acoustic emission waveforms were captured. The acoustic emission waveforms were analyzed and distinguished into three types based on the similar nature in both time and frequency domains. Three-dimensional harmonic finite element analyses were performed to identify the local vibration modes. The local crack resonance phenomenon has been observed from the finite element simulation that could potentially give the geometric information of the crack. The laser Doppler vibrometry experiment was performed to identify the crack resonance phenomenon, and the experimental results were used to verify the simulated results.

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