YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Pressure Vessel Technology
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Pressure Vessel Technology
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    A Probabilistic Environmentally Assisted Cracking Model for Steam Generator Tubes

    Source: Journal of Pressure Vessel Technology:;2015:;volume( 137 ):;issue: 002::page 21204
    Author:
    Hyun Lee, Tae
    ,
    Young Yoon, Jae
    ,
    On Nam, Hyo
    ,
    Soon Hwang, Il
    DOI: 10.1115/1.4027641
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A probabilistic environmentally assisted cracking (PEAC) model was developed to describe the propagation of primary water stress corrosion cracking for Alloy 600 in rolltransition region of steam generator (SG), a severe environmentally assisted cracking problem in pressurized water reactors (PWRs). In the PEAC model, crack growth rate (CGR) and probability of failure (POF) were obtained by adopting a Bayesian inference that decreases the uncertainties of unknown parameters and their distributions in theoretical equations. The CGR is mainly dependent on three factors: probability of detection (POD), initial crack size distribution, and stress distribution. The POD, which is a logistic link was updated with Bayesian inference based on SG inspection data. The crack size distribution, which is relative to initiation time expressed by a Weibull function, was also updated with Bayesian inference using POD. The stress distribution caused by mechanical rolling is considered to be a major contributing factor along the SG tube. It based on finite element analysis is deterministic model unlike POD and initial crack distribution. According to this model, the uncertainty of hyperparameters in the CGR which are parameters of a prior distribution was reduced, and the appropriate level of confidence was achieved by utilizing the available data. Moreover, a benchmark study for the SG tube was performed to evaluate reliability of Alloy 600 SG components in nuclear power plants. The POF was estimated from the developed PEAC model and failure criteria by taking into account the effects of inspection and repair of defective tubes. The results from this study are applied to demonstrate risk reduction in PWRs by adopting riskinformed inservice inspection.
    • Download: (1.521Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      A Probabilistic Environmentally Assisted Cracking Model for Steam Generator Tubes

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/159440
    Collections
    • Journal of Pressure Vessel Technology

    Show full item record

    contributor authorHyun Lee, Tae
    contributor authorYoung Yoon, Jae
    contributor authorOn Nam, Hyo
    contributor authorSoon Hwang, Il
    date accessioned2017-05-09T01:22:57Z
    date available2017-05-09T01:22:57Z
    date issued2015
    identifier issn0094-9930
    identifier otherpvt_137_02_021204.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/159440
    description abstractA probabilistic environmentally assisted cracking (PEAC) model was developed to describe the propagation of primary water stress corrosion cracking for Alloy 600 in rolltransition region of steam generator (SG), a severe environmentally assisted cracking problem in pressurized water reactors (PWRs). In the PEAC model, crack growth rate (CGR) and probability of failure (POF) were obtained by adopting a Bayesian inference that decreases the uncertainties of unknown parameters and their distributions in theoretical equations. The CGR is mainly dependent on three factors: probability of detection (POD), initial crack size distribution, and stress distribution. The POD, which is a logistic link was updated with Bayesian inference based on SG inspection data. The crack size distribution, which is relative to initiation time expressed by a Weibull function, was also updated with Bayesian inference using POD. The stress distribution caused by mechanical rolling is considered to be a major contributing factor along the SG tube. It based on finite element analysis is deterministic model unlike POD and initial crack distribution. According to this model, the uncertainty of hyperparameters in the CGR which are parameters of a prior distribution was reduced, and the appropriate level of confidence was achieved by utilizing the available data. Moreover, a benchmark study for the SG tube was performed to evaluate reliability of Alloy 600 SG components in nuclear power plants. The POF was estimated from the developed PEAC model and failure criteria by taking into account the effects of inspection and repair of defective tubes. The results from this study are applied to demonstrate risk reduction in PWRs by adopting riskinformed inservice inspection.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Probabilistic Environmentally Assisted Cracking Model for Steam Generator Tubes
    typeJournal Paper
    journal volume137
    journal issue2
    journal titleJournal of Pressure Vessel Technology
    identifier doi10.1115/1.4027641
    journal fristpage21204
    journal lastpage21204
    identifier eissn1528-8978
    treeJournal of Pressure Vessel Technology:;2015:;volume( 137 ):;issue: 002
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian