Probabilistic models for fatigue crack growth prediction
department or school: Applied Science, Faculty of
department or school: Graduate Studies, College of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Wang, Teng
issued: 2023-06-29T18:11:42Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Fatigue cracks pose a critical threat to the structural integrity of mechanical and structural components. In damage tolerance design philosophy, it is allowed that an in-service component has growing fatigue cracks, as long as the cracks can be reliably detected during scheduled inspections, and will not cause the failure of the component in a catastrophic manner. In this context, the accurate prediction of fatigue crack growth is crucial to ensure the reliability of in-service components while reducing maintenance costs. This thesis research aims to develop a set of probabilistic models for fatigue crack growth prediction considering a variety of uncertainty factors. First, to model the material uncertainty of individual structure, a probabilistic model for the fatigue crack growth prediction is proposed based on a closed-form solution. In this model, the stress intensity factor is related to the crack length and load condition through the finite element model. The material parameters and model error are regarded as random variables. Based on conjugate Bayesian analysis, a closed-form solution is derived to update the posterior distribution of the material parameters according to the crack growth observation. Given the posterior distribution, a modified Paris–Erdogan equation is adopted to predict the crack growth in a probabilistic view. The effectiveness of the model is validated based on the fatigue experimental data of a set of aluminum alloy specimens. Second, to consider multi-source prior knowledge, a probabilistic model for the fatigue crack growth prediction with hybrid prior is proposed. This model comprises two inference steps. In the first inference step, a set of candidate priors are input. Then, the Monte Carlo integration is adopted in the calculation of the posterior belief of each candidate prior. In the second inference step, the particle filter is extended to conduct the Bayesian inference with a hybrid prior. Numerical studies on the spur gear …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85176
note: Probabilistic Models for Fatigue CrackGrowth PredictionbyTeng WangB.Eng., Hefei University of Technology, 2016M.Eng., Shandong University, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2023© Teng Wang, 2023The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, the dissertation entitled:PROBABILISTIC MODELS FOR FATIGUE CRACK GROWTH PRE-DICTIONsubmitted by Teng Wang in partial fulfilment of the requirements of thedegree of Doctor of PhilosophyDr. Zheng Liu, School of EngineeringSupervisorDr. Min Liao, National Research Council CanadaCo-SupervisorDr. Rudolf Seethaler, School of EngineeringSupervisory Committee MemberDr. Ahmad Al-Dabbagh, School of EngineeringSupervisory Committee MemberDr. Lukas Bichler, School of EngineeringUniversity ExaminerDr. Lalita Udpa, Michigan State UniversityExternal ExamineriiAbstractFatigue cracks pose a critical threat to the structural integrity of mechanicaland structural components. In damage tolerance design philosophy, it isallowed that an in-service component has growing fatigue cracks, as long asthe cracks can be reliably detected during scheduled inspections, and willnot cause the failure of the component in a catastrophic manner. In thiscontext, the accurate prediction of fatigue crack growth is crucial to ensurethe reliability of in-service components while reducing maintenance costs.This thesis research aims to develop a set of probabilistic models for fatiguecrack growth prediction considering a variety of uncertainty factors.First, to model the material uncertainty of individual structure, a prob-abilistic model for the fatigue crack growth prediction is proposed based ona closed-form solution. In this model, the stress intensity factor is relatedto the crack length and load condition through the finite e…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0433823
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
contributor: Liao, Min
type: Text
identifier URI: http://hdl.handle.net/2429/85176