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    A Nonstationary Uncertainty Model and Bayesian Calibration of Strain-Life Models

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2021:;volume( 006 ):;issue: 001::page 011002-1
    Author:
    Dourado, Arinan
    ,
    Irmak, Firat
    ,
    Viana, Felipe A. C.
    ,
    Gordon, Ali P.
    DOI: 10.1115/1.4049324
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The Coffin–Manson–Basquin–Haford (CMBH) model is a well-accepted strain-life relationship to model fatigue life as a function of applied strain. In this paper, we propose a nonstationary uncertainty model for the CMBH model, alongside a Bayesian framework for model calibration and estimation of confidence and prediction intervals. Using Inconel 617 coupon test data, we compared our approach to traditional stationary variance models. The proposed uncertainty model successfully captures the fact that the variance of strain amplitude decreases with increasing fatigue life. Additionally, a discussion on how to use the proposed Bayesian framework to compensate for the lack of data by using prior information coming from similar alloys is also presented considering Hastealloy-X and Inconel 617 coupon data.
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      A Nonstationary Uncertainty Model and Bayesian Calibration of Strain-Life Models

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    contributor authorDourado, Arinan
    contributor authorIrmak, Firat
    contributor authorViana, Felipe A. C.
    contributor authorGordon, Ali P.
    date accessioned2022-02-05T22:11:38Z
    date available2022-02-05T22:11:38Z
    date copyright1/7/2021 12:00:00 AM
    date issued2021
    identifier issn2377-2158
    identifier othervvuq_006_01_011002.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4277095
    description abstractThe Coffin–Manson–Basquin–Haford (CMBH) model is a well-accepted strain-life relationship to model fatigue life as a function of applied strain. In this paper, we propose a nonstationary uncertainty model for the CMBH model, alongside a Bayesian framework for model calibration and estimation of confidence and prediction intervals. Using Inconel 617 coupon test data, we compared our approach to traditional stationary variance models. The proposed uncertainty model successfully captures the fact that the variance of strain amplitude decreases with increasing fatigue life. Additionally, a discussion on how to use the proposed Bayesian framework to compensate for the lack of data by using prior information coming from similar alloys is also presented considering Hastealloy-X and Inconel 617 coupon data.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Nonstationary Uncertainty Model and Bayesian Calibration of Strain-Life Models
    typeJournal Paper
    journal volume6
    journal issue1
    journal titleJournal of Verification, Validation and Uncertainty Quantification
    identifier doi10.1115/1.4049324
    journal fristpage011002-1
    journal lastpage011002-8
    page8
    treeJournal of Verification, Validation and Uncertainty Quantification:;2021:;volume( 006 ):;issue: 001
    contenttypeFulltext
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