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Record W123889794 · doi:10.13182/nse05-a2531

Refueling Simulation Strategy of a CANDU Reactor Based on Optimum Zone Controller Water Levels

2005· article· en· W123889794 on OpenAlexaboutno aff
Hangbok Choi, Do Heon Kim

Bibliographic record

VenueNuclear Science and Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsController (irrigation)Environmental scienceNuclear engineeringRange (aeronautics)BundleControl theory (sociology)Computer scienceEngineeringMaterials scienceControl (management)

Abstract

fetched live from OpenAlex

An optimum refueling simulation method was developed for application to a Canada deuterium uranium 713-MW(electric) (CANDU-6) reactor. The objective of the optimization was to maintain the operating range of the zone controller unit (ZCU) water level so that the reference zone power distribution is reproduced following the refueling operation. The zone controller level on the refueling operation was estimated by the generalized perturbation method, which provides sensitivities of the zone power to an individual refueling operation and the zone controller level. By constructing a system equation of the zone power, the zone controller level was obtained, which was used to find the most suitable combination of the refueling channels. The 250-full-power-day refueling simulations showed that the channel and bundle powers are well controlled below the license limits when the ZCU water level remains in the typical operating range.

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)0.000
metaresearch head score (Gemma)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.207
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

Citations4
Published2005
Admission routes1
Has abstractyes

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