Uncertainty Quantification in Fatigue Crack Growth Prognosis

Shankar Sankararaman, You Ling, Christopher Shantz, and Sankaran Mahadevan
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA AMES Research Center, Federal Aviation Administration William J. Hughes Technical Center
AttachmentSizeTimestamp
ijPHM_11_001.pdf370.82 KBJuly 25, 2011 - 10:01am

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.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
001
Page Count: 
15
Submission Keywords: 
model-based methods
Fatigue Prognosis
Uncertainty Quantification
Natural Variability
Data Uncertainty
Model Error
Fracture Mechanics
Crack Growth
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Structural health monitoring
Submitted by: 
  
 
 
 

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