scholarly journals Stochastic Filter-based Fatigue Crack Growth Prediction for Pipelines considering Unknown Model Parameters and Measurement Uncertainty

2021 ◽  
Author(s):  
Durlabh Bartaula ◽  
Samer Adeeb ◽  
Yong Li
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Measurement Uncertainty ◽  
Crack Growth ◽  
Model Parameters ◽  
Growth Prediction ◽  
Stochastic Filter

An Integrated Prognostics Approach for Pipeline Fatigue Crack Growth Prediction Utilizing Inline Inspection Data

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Mingjiang Xie ◽  
Steven Bott ◽  
Aaron Sutton ◽  
Alex Nemeth ◽  
Zhigang Tian
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Measurement Uncertainty ◽  
Stress Analysis ◽  
Crack Growth ◽  
Integrated Approach ◽  
Model Parameters ◽  
Growth Prediction ◽  
Depth Measurement ◽  
Crack Growth Model

Fatigue cracking is a key type of defect for liquid pipelines, and managing fatigue cracks has been a top priority and a big challenge for liquid pipeline operators. The existing inline inspection (ILI) tools for pipeline defect evaluation have large fatigue crack measurement uncertainties. Furthermore, the current physics-based methods are mainly used for fatigue crack growth prediction, where the same or a small range of fixed model parameters is used for all pipes. They result in uncertainty that is managed through the use of conservative safety factors such as adding depth uncertainty to the measured depth in deciding integrity management and risk mitigation strategies. In this study, an integrated approach is proposed for pipeline fatigue crack growth prediction utilizing ILI data including consideration of crack depth measurement uncertainty. This approach is done by integrating the physical models, including the stress analysis models, the crack growth model governed by the Paris’ law, and the ILI data. With the proposed integrated approach, the finite element (FE) model of a cracked pipe is built and the stress analysis is performed. ILI data are utilized to update the uncertain physical parameters for the individual pipe being considered so that a more accurate fatigue crack growth prediction can be achieved. Time-varying loading conditions are considered in the proposed integrated method by using rainflow counting method. The proposed integrated prognostics approach is compared with the existing physics-based method using examples based on simulated data. Field data provided by a Canadian pipeline operator are also employed for the validation of the proposed method. The examples and case studies in this paper demonstrate the limitations of the existing physics-based method, and the promise of the proposed method for achieving accurate fatigue crack growth prediction as continuous improvement of ILI technologies further reduces ILI measurement uncertainty.

scholarly journals MCMC-Based Fatigue Crack Growth Prediction on 2024-T6 Aluminum Alloy

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Du ◽  
Yu-ting He ◽  
Chao Gao ◽  
Kai Liu ◽  
Teng Zhang ◽  
...  
Keyword(s):  
Experimental Data ◽  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Growth Behavior ◽  
Crack Growth Behavior ◽  
Model Parameters ◽  
Growth Prediction ◽  
Growth Test

This work aims to make the crack growth prediction on 2024-T6 aluminum alloy by using Markov chain Monte Carlo (MCMC). The fatigue crack growth test is conducted on the 2024-T62 aluminum alloy standard specimens, and the scatter of fatigue crack growth behavior was analyzed by using experimental data based on mathematical statistics. An empirical analytical solution of Paris’ crack growth model was introduced to describe the crack growth behavior of 2024-T62 aluminum alloy. The crack growth test results were set as prior information, and prior distributions of model parameters were obtained by MCMC using OpenBUGS package. In the additional crack growth test, the first test point data was regarded as experimental data and the posterior distribution of model parameters was obtained based on prior distributions combined with experimental data by using the Bayesian updating. At last, the veracity and superiority of the proposed method were verified by additional crack growth test.

scholarly journals Determining finite-width-correction factors for fatigue crack growth prediction in GLARE using the equivalent compliance method

2019 ◽  
Vol 127 ◽  
pp. 74-81 ◽  
Author(s):  
Yuan Zhao ◽  
René Alderliesten ◽  
Zengwen Wu ◽  
Zhengong Zhou ◽  
Guodong Fang ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Finite Width ◽  
Correction Factors ◽  
Growth Prediction

Fatigue crack growth prediction of welded joints in low-cycle fatigue region

2017 ◽  
Vol 61 (6) ◽  
pp. 1189-1197
Author(s):  
Takeshi Hanji ◽  
Kazuo Tateishi ◽  
Nao Terao ◽  
Masaru Shimizu
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Welded Joints ◽  
Low Cycle Fatigue ◽  
Cycle Fatigue ◽  
Growth Prediction

scholarly journals Structural Reliability Prediction Using Acoustic Emission-Based Modeling of Fatigue Crack Growth

2018 ◽  
Vol 8 (8) ◽  
pp. 1225 ◽  
Author(s):  
Azadeh Keshtgar ◽  
Christine Sauerbrunn ◽  
Mohammad Modarres
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Titanium Alloys ◽  
Crack Growth ◽  
Structural Reliability ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Loading Frequency ◽  
Proposed Model ◽  
Crack Growth Model

In this paper, AE signals collected during fatigue crack-growth of aluminum and titanium alloys (Al7075-T6 and Ti-6Al-4V) were analyzed and compared. Both the aluminum and titanium alloys used in this study are prevalent materials in aerospace structures, which prompted this current investigation. The effect of different loading conditions and loading frequencies on a proposed AE-based crack-growth model were studied. The results suggest that the linear model used to relate AE and crack growth is independent of the loading condition and loading frequency. Also, the model initially developed for the aluminum alloy proves to hold true for the titanium alloy while, as expected, the model parameters are material dependent. The model parameters and their distributions were estimated using a Bayesian regression technique. The proposed model was developed and validated based on post processing and Bayesian analysis of experimental data.

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