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ICONE19-43522 A Partial Factor-Based Approach for the Assessment of Nuclear Piping Vulnerable to Corrosion

2011· article· en· W2190832985 on OpenAlexafffundabout
Xufang Zhang, Mahesh D. Pandey

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2011
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringUniversity of Waterloo
KeywordsPipingReliability (semiconductor)CorrosionReliability engineeringService lifeFunction (biology)EngineeringNuclear engineeringStructural engineeringEnvironmental scienceComputer scienceMaterials scienceMechanical engineeringMetallurgyPhysics

Abstract

fetched live from OpenAlex

Flow accelerated corrosion (FAC) is a general wall thinning that can impair the integrity of reactor piping system. Extensive in-service piping inspections are undertaken to measure wall thickness, estimate the corrosion rate and predict the end of lifetime. The paper presents an efficient method for pipe reliability assessment based on concept of partial factors that are calibrated to specific target reliability. The key factor in the proposed methodology is to establish equations in terms of partial factor and reliability index. The paper describes the derivation of partial factors with reference to a defined failure function, also referred to as the limit state function. A practical case study is presented based on data collected from a Canadian plant. Using the wall thickness measurement data, the reliability of primary reactor piping system is assessed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.260
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes3
Has abstractyes

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