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Record W3000234952 · doi:10.12943/cnr.2018.00005

THERMAL BEHAVIOUR CORRELATION FOR THERMAL COLUMN IN THE NATIONAL RESEARCH UNIVERSAL (NRU) REACTOR

2020· article· en· W3000234952 on OpenAlexaffvenue
Chukwudi Azih, H. Mazhar, J.J. Baschuk, Thambiayah Nitheanandan

Bibliographic record

VenueCNL Nuclear Review · 2020
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsThermocoupleResearch reactorGraphiteNuclear engineeringNuclear reactor coreNuclear graphiteThermalNuclear reactorColumn (typography)Neutron temperatureMaterials scienceNeutronMechanicsNuclear physicsThermodynamicsComposite materialPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The National Research Universal (NRU) reactor is a major research facility that provides a beam of slow neutrons with a minimum of gamma rays and other types of radiation for experimental purposes. The thermal column consists of 5 graphite radial sections separated with an air gap for cooling. The graphite components require continuous monitoring to ascertain that temperatures are controlled within safe margins. Wall temperatures of the graphite sections are obtained via thermocouples affixed to the column walls. The safety margins for operation of the thermal column are driven by the temperatures of the closest radial section to the reactor core (HG1). Although, most of the thermocouples in HG1 are no longer functional, the thermocouples are functional in the adjacent graphite section (HG2). This study relied on the historical data of the graphite temperatures over a few years to develop an empirical correlation that relates temperatures in HG1 to those of HG2. The correlation sets limits on the functional thermocouples in HG2 to ensure HG1 remains within the prescribed limits (149–232 °C). Correlations were developed using statistical analysis of the historical data. A control band of approximately 40 °C for HG2 with confidence levels of 68% and 95%, respectively, were established.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.080
GPT teacher head0.296
Teacher spread0.216 · 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 designNot applicable
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
Published2020
Admission routes2
Has abstractyes

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