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Record W2557997083 · doi:10.1115/pvp2016-63989

Fitness-for-Service Assessment of Calandria Tube to Liquid Injection Shutdown System Nozzle Contact in a CANDU Reactor

2016· article· en· W2557997083 on OpenAlexaff
Cheng Liu, Eric Tulk, Douglas A. Scarth, Larry Micuda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsBruce Power (Canada)Kinectrics (Canada)
Fundersnot available
KeywordsNozzleNuclear engineeringTube (container)Mechanical engineeringMaterials scienceReliability engineeringEngineering

Abstract

fetched live from OpenAlex

In CANDU1 reactors, calandria tubes are used to separate the fuel channels from the moderator. The Liquid Injection Shutdown System (LISS) nozzles, which contain holes for discharging neutron-absorbing liquid into the moderator, are arranged perpendicularly in the gap between adjacent rows of calandria tubes. Both calandria tubes and LISS nozzles sag during service due to creep, with the more heavily loaded calandria tubes sagging relatively more. When contact between a calandria tube and a LISS nozzle has been detected by in-service inspection, or is predicted to occur, a fitness-for-service assessment is permitted by CSA Standard N285.4 to demonstrate acceptability of continued operation until the end of the next periodic inspection interval, provided that the fitness-for-service assessment is acceptable to the Regulatory Authority. A fitness-for-service assessment has been recently performed for a calandria tube and a LISS nozzle that were predicted to contact at a future time. The assessment has demonstrated that for an evaluation period of three years after the predicted contact time, the structural integrity of the calandria tube and the LISS nozzle is maintained, both components will continue to function in accordance with their design requirements, and their contact will not result in contact between the calandria tube and a pressure tube.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2016
Admission routes1
Has abstractyes

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