Materials Degradation and Detection (MD2): Deep Dive Final Report

McCloy, John S.; Montgomery, Robert; Ramuhalli, Pradeep; Meyer, Ryan M.; Hu, Shenyang; Li, Yulan; Henager, Charles H.; Johnson, Bradley R.

doi:10.2172/1072906

Cited by 4 publications

(2 citation statements)

References 196 publications

(192 reference statements)

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“…By detecting the presence of material degradation mechanisms early in the process, better insights are gained about the state of the material that can be used to understand the precise margins to failure. A brief stateof-the-art assessment for real-time monitoring of early degradation in materials used in the production of nuclear power, including creep measurement techniques is covered in McCloy et al (2013).…”

Section: A2 Very-high-temperature Gas Reactorsmentioning

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: A2 Very-high-temperature Gas Reactorsmentioning

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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“…Microstructure effects on MBN emission and on first-order reversal curve (FORC) analysis was studied in ferritic/martensitic alloy (HT-9), which can be interesting for the experts in nuclear materials [84]. It was found that MBN emission and reversible component of magnetization, determined from the FORC data, decreased with increasing mechanical hardness.…”

Section: Barkhausen Emission Measurementsmentioning

confidence: 99%

Evaluation of the Embrittlement in RPV Steels by Means of a Hybrid NDE Approach

Vértesy,

Rabung,

Gasparics

et al. 2023

Preprint

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Nondestructive determination of the neutron irradiation induced embrittlement of nuclear reactor pressure vessel steel is a very important and recent problem. In the frame of the so called NOMAD project, funded by the Euratom research and training program, novel nondestructive electromagnetic testing methods were applied for inspection of irradiated reactor pressure vessel steel. In this review the most important results of this project are summarized. Different methods were used and compared with each other. Measurement results were compared with the destructively determined ductile to brittle transition temperature (DBTT) values. Three magnetic methods: 3MA (micromagnetic, multiparameter, microstructure and stress analysis), MAT (magnetic adaptive testing) and Barkhausen noise technique (MBN) were found as the most promising techniques. The results of these methods were in good confidence with each other. Good correlation was found between the magnetic parameters and the DBTT values. The basic idea of the NOMAD project is to use a multi-method/multi-parameter approach and to focus on their synergies that allows to recognise the side effects therefore supressing them at the same time. Different types of Machine Learning (ML) algorithms were tested in competition and their performances were evaluated. The important outcome of the ML technique is that not only one, but several different ML techniques could reach required precision and reliability, i.e. to keep the DBTT prediction error lower than +/-25°C threshold, which was previously not possible for any of the NDE methods as single entities. A calibration/training procedure was carried out on the merged outcome of testing methods with excellent results to predict transition temperature, yield strength and mechanical hardness for all investigated materials. Our results, achieved within NOMAD project can be useful for the future potential introduction of this (and in general, any) nondestructive evolution method.

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Technical Needs for Prototypic Prognostic Technique Demonstration for Advanced Small Modular Reactor Passive Components

Meyer¹,

Coble²,

Hirt³

et al. 2013

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A key national energy priority to promote energy security is sustainable nuclear power. Nuclear energy currently contributes approximately 20% of baseload electrical needs in the United States and is considered a reliable generation source to meet future electricity needs. Advanced small modular reactors (AdvSMRs) using non-light-water reactor coolants such as liquid metal, helium, or liquid salt are promising mid-to long-term options being explored for added functionality and affordability in future reliable nuclear power deployment. AdvSMRs can offer potential advantages over more conventional technologies in the areas of safety and reliability, sustainability, affordability, functionality, and proliferation resistance. However, a number of technical challenges will need to be addressed before AdvSMRs are ready for deployment, given their potential for remote deployment with minimal staffing, longer operating cycles between planned refueling and maintenance outages, and support for multiple energy applications. In addition, AdvSMRs (like SMRs based on more conventional light-water reactor technologies) will have reduced economy-of-scale savings when compared to current generation lightwater reactors (LWRs). Issues related to AdvSMR deployment can be addressed through cross-cutting RD&D involving instrumentation, controls, and human-machine interface (ICHMI) technologies. Specifically, diagnostics and prognostics technologies provide a mechanism for improving safety and reliability of AdvSMRs through integrated health management of passive components. This report identifies activities and develops an outline of a research plan to address the high-priority technical needs for demonstrating prototypic prognostic techniques to manage degradation of passive AdvSMR components. Concepts for AdvSMRs span a wide range of design maturity, specificity, and concepts of operation, including multi-unit, multi-product-stream generating stations. Key to the development and deployment of AdvSMRs will be the ability to ensure safe and affordable operation of these reactor designs. AdvSMR designs generally place more emphasis on passive systems to assure safety. However, degradation in all passive components will need to be well-managed to maximize safety, operational lifetimes, and plant reliability while minimizing maintenance demands, if reduced economies-of-scale are to be overcome. Traditional approaches such as periodic in-service nondestructive inspections are likely to have limited applicability to AdvSMRs, given the expectation of longer operating periods and potential difficulties with inspection access to critical components. Advanced instrumentation and control (I&C) technologies can provide a mechanism for achieving these goals. However, the significant technology and environmental differences between AdvSMRs and conventional LWRs and the potential for modularized deployment result in unique challenges and needs for advanced ICHMI applications in AdvSMRs. v prognostics is also documented. This assessment, combined wi...

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Materials Degradation and Detection (MD2): Deep Dive Final Report

Cited by 4 publications

References 196 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

Evaluation of the Embrittlement in RPV Steels by Means of a Hybrid NDE Approach

Technical Needs for Prototypic Prognostic Technique Demonstration for Advanced Small Modular Reactor Passive Components

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