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Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Numerical Modelling of Stochastic Fatigue Damage Accumulation in Thick Composites

Authors : Richard Loendersloot, M. Ehsani, N. Sepehry, M. Shamshirsaz

Published in: European Workshop on Structural Health Monitoring

Publisher: Springer International Publishing

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Abstract

In an earlier research, experimental evidence was given on the ability to use Piezo Wafer Active Sensors and acousto-ultrasonics to monitor the accumulation of fatigue damage in a thick composite structure. As a next step, numerical models are investigated as they aid in the further understanding of the governing phenomena and a quantification of the accumulated damage. However, they suffer from high computational demands, due to a high mesh density, the stochastic nature of crack initiation and the combination of initiation and propagation of cracks.
The Polynomial Chaos Expansion (PCE) method is employed to efficiently make meta models and, with these models, account for the stochastic behaviour of crack initiation and formation of delaminations. The meta models thus allow predicting the overall effect of damage accumulations within certain bounds of uncertainty. This aids in the quantification of damage accumulation, hence allowing for a damage severity estimation based on the experimental results.
The input for the PCE method is a 2D Finite Element (FE) model. Cracks and delaminations are generated using Random Variables (RV) describing the geometrical position and length and orientation. Moreover, the number of cracks and delaminations is randomized as well. The necessary remeshing is done automatically, allowing for a completely automated simulation for a large number of FE simulations to feed the PCE model.
Several Quantities of Interests (QoI) are defined and tested against their sensitivity to the increasing amount of damage accumulation. A global sensitivity analysis is used to identify the importance of each of the Random Variables. Random variables with a low sensitivity can be eliminated from the analysis, improving the efficiency.

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previous chapter Automatic Fault Detection and Classification in Lift Door Systems Using Vibration Signal Features
next chapter Optimal Finite Difference Schemes for Multiple Damage Identification in Beams
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Metadata
Title
Numerical Modelling of Stochastic Fatigue Damage Accumulation in Thick Composites
Authors
Richard Loendersloot
M. Ehsani
N. Sepehry
M. Shamshirsaz
Copyright Year
2021
Book
European Workshop on Structural Health Monitoring

Print ISBN: 978-3-030-64907-4

Electronic ISBN: 978-3-030-64908-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-64908-1_72