Uncertainty Quantification in Fatigue Crack Growth Prognosis
This paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the complicated geometry and calculate the stress inten...
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The Prognostics and Health Management Society
2011-01-01
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doaj-1531083ab13c4f9f9a60097bb68a631f2021-07-02T02:33:40ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482011-01-0121115Uncertainty Quantification in Fatigue Crack Growth PrognosisShankar SankararamanYou LingChristopher ShantzSankaran MahadevanThis paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. Crack growth under variable amplitude loading is modeled using a modified Paris law that includes retardation effects. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis. The effect of different types of uncertainty – physical variability, data uncertainty and modeling errors – on crack growth prediction is investigated. The various sources of uncertainty include, but not limited to, variability in loading conditions, material parameters, experimental data, model uncertainty, etc. Three different types of modeling errors – crack growth model error, discretization error and surrogate model error – are included in analysis. The different types of uncertainty are incorporated into the crack growth prediction methodology to predict the probability distribution of crack size as a function of number of load cycles. The proposed method is illustrated using an application problem, surface cracking in a cylindrical structure.http://www.phmsociety.org/sites/all/modules/pubdlcnt/pubdlcnt.php?file=http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2010/ijPHM_11_001.pdf&nid=287model-based methodsFatigue PrognosisUncertainty QuantificationNatural VariabilityData UncertaintyModel ErrorFracture MechanicsCrack Growth |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shankar Sankararaman You Ling Christopher Shantz Sankaran Mahadevan |
spellingShingle |
Shankar Sankararaman You Ling Christopher Shantz Sankaran Mahadevan Uncertainty Quantification in Fatigue Crack Growth Prognosis International Journal of Prognostics and Health Management model-based methods Fatigue Prognosis Uncertainty Quantification Natural Variability Data Uncertainty Model Error Fracture Mechanics Crack Growth |
author_facet |
Shankar Sankararaman You Ling Christopher Shantz Sankaran Mahadevan |
author_sort |
Shankar Sankararaman |
title |
Uncertainty Quantification in Fatigue Crack Growth Prognosis |
title_short |
Uncertainty Quantification in Fatigue Crack Growth Prognosis |
title_full |
Uncertainty Quantification in Fatigue Crack Growth Prognosis |
title_fullStr |
Uncertainty Quantification in Fatigue Crack Growth Prognosis |
title_full_unstemmed |
Uncertainty Quantification in Fatigue Crack Growth Prognosis |
title_sort |
uncertainty quantification in fatigue crack growth prognosis |
publisher |
The Prognostics and Health Management Society |
series |
International Journal of Prognostics and Health Management |
issn |
2153-2648 |
publishDate |
2011-01-01 |
description |
This paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. Crack growth under variable amplitude loading is modeled using a modified Paris law that includes retardation effects. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis. The effect of different types of uncertainty – physical variability, data uncertainty and modeling errors – on crack growth prediction is investigated. The various sources of uncertainty include, but not limited to, variability in loading conditions, material parameters, experimental data, model uncertainty, etc. Three different types of modeling errors – crack growth model error, discretization error and surrogate model error – are included in analysis. The different types of uncertainty are incorporated into the crack growth prediction methodology to predict the probability distribution of crack size as a function of number of load cycles. The proposed method is illustrated using an application problem, surface cracking in a cylindrical structure. |
topic |
model-based methods Fatigue Prognosis Uncertainty Quantification Natural Variability Data Uncertainty Model Error Fracture Mechanics Crack Growth |
url |
http://www.phmsociety.org/sites/all/modules/pubdlcnt/pubdlcnt.php?file=http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2010/ijPHM_11_001.pdf&nid=287 |
work_keys_str_mv |
AT shankarsankararaman uncertaintyquantificationinfatiguecrackgrowthprognosis AT youling uncertaintyquantificationinfatiguecrackgrowthprognosis AT christophershantz uncertaintyquantificationinfatiguecrackgrowthprognosis AT sankaranmahadevan uncertaintyquantificationinfatiguecrackgrowthprognosis |
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