Predictive modelling of long term creep performance of critical plant components

Brear, J. M.; Aplin, P. F.

doi:10.1049/cp:19941102

Cited by 2 publications

(2 citation statements)

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“…Several models have been proposed to describe the primary, secondary, or tertiary phase of creep (Naumenko and Altenbach 2007). Some of these models have been proposed to describe two phases in a unified model (Brear and Aplin 1994). The most appropriate model for each phase of creep depends on the material properties and environmental conditions.…”

Section: Assessment Using Synthetic Datamentioning

confidence: 99%

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Ramuhalli¹,

Roy²,

Hirt³

et al. 2015

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Prognostic health management technologies are expected to play a vital role in the deployment and safe, cost-effective operation of advanced reactors by providing the technical means for lifetime management of significant passive components and reactor internals. This report describes a Bayesian methodology for the prediction of remaining life of materials and passive AR components. This approach, previously applied to predict time-to-failure of materials subjected to localized aging and degradation, is adapted for component-level prognostics. The Bayesian framework for component-level prognostics incorporates the ability to fuse information from multiple sources, including information on localized degradation and component-level condition indicators. The ability to switch between multiple models of degradation accumulation rate and/or multiple models of measurement physics becomes important in this context, and a reversible jump Markov chain Monte Carlo methodology has been developed for this purpose. Evaluations of the Bayesian framework and the model switching and selection methodology were performed using synthetic data as well as experimental measurements on a high-temperature creep testbed. Results to date indicate that the feasibility of the proposed Bayesian framework for prognostics, though an improvement over previous methods' accuracy, will require the ability to quantify sources of uncertainties within the various models used in the prognostic framework. Ongoing efforts are focused on sensing and measurement (particularly in-situ measurements) that would be applied as inputs to the prognostics framework, with the objective of identifying measurement methods that can provide early indicators of material degradation and quantifying the reliability and sensitivity of these measurement methods.

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Section: Assessment Using Synthetic Datamentioning

confidence: 99%

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Ramuhalli¹,

Roy²,

Hirt³

et al. 2015

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show abstract

“…Several models have been proposed to describe the primary, secondary, or tertiary phase of creep [17]. Some of these models have been proposed to describe two phases in a unified model [18]. The most appropriate model for each phase of creep depends on the material properties and environmental conditions.…”

Section: Prognostics For High-temperature Creepmentioning

confidence: 99%

Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation

Roy

Dib

Ramuhalli

et al. 2015

2015 IEEE Conference on Prognostics and Health Management (PHM)

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This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

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Predictive modelling of long term creep performance of critical plant components

Cited by 2 publications

References 0 publications

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation

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Predictive modelling of long term creep performance of critical plant components

Cited by 2 publications

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Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Progress towards prognostic health management of passive components in advanced reactors &#x2014; Model selection and evaluation

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Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation