Work

Stochastic filter-based fatigue crack growth prediction for pipelines considering unknown model parameters and measurement uncertainty

Year: 2021

Type: article

Abstract: In this study a methodology is developed and implemented in Python for fatigue crack growth prediction in pipelines, by leveraging measurement data and fatigue growth model predictions. Specifically, Particle Filter (PF) algorithm, Paris law, and the stress intensity factor (SIF) model in API 579 are integrated into a tool to use noisy crack size measurements for estimating the current crack size and fatigue model parameters, also known as joint state-parameter estimation. For illustration purpose, pseudo-data set for crack size measurements is generated considering additive Gaussian white noise of two different noise levels, aiming to mimic crack size data obtained from In-line Inspection (ILI) tools. It is found that the crack state can be reliably estimated compared to noisy measurements and initial model predictions, and the true model parameters can be updated with good accuracy. As such, the current crack size estimated and model parameters updated can be used in the fatigue growth model (i.e., Paris law) to predict the future trajectory of the fatigue crack growth. As more measurement data becomes available, the developed tool more reliably estimates the future crack growth trajectory.

Source: Journal of Pipeline Science and Engineering

DOI: 10.1016/j.jpse.2021.11.005

Authors: Durlabh Bartaula, Samer Adeeb, Yong Li

Institution: University of Alberta

Language: English

Topic: Fatigue and fracture mechanics

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Mitacs