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Record W4408813992 · doi:10.58837/chula.the.2000.1660

Development of a method to measure the moderator temperature distribution in a CANDU reactor

2000· dissertation· en· W4408813992 on OpenAlexaboutno aff
Maytinee Vatanakul

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsModerationMeasure (data warehouse)Nuclear engineeringDistribution (mathematics)Materials scienceComputer scienceEngineeringStatisticsMathematicsData miningMathematical analysis

Abstract

fetched live from OpenAlex

CANDU, Canada Deuterium Uranium, represents the power reactor system using natural uranium as a fuel, and heavy water as a coolant and a moderator of the reactor. The moderator is used to slow neutrons to increase fission probability in the fuel, and act as a heat sink for reactor accident situation. Due to irradiative heating of any material present, the measurement of the temperature ability in reactor operation is difficult. The Vertical Flux Detector assembly (VFD) could be used to measure the moderator temperature. The experiments were carried out to determine the heat transfer characteristics of the VFD by using the test cell duplicated from a small section of the VFD with electrical heating to simulate the irradiative heating in the reactor core. A number of parameters were analyzed to determine their effects on the measurement technique. A numerical calculation using FLUENT was applied to determine the temperature profile in the VFD correlated with its heat transport characteristics. It was found that the temperature difference between the moderator and the positions in the detector wells of the electrical heaters was directly proportional to the internal heat generation rate. The temperature distribution along the detector wells could be used to determine the irradiative heating in the station reactor moderator.

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 score0.477
Threshold uncertainty score0.677

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 head0.008
GPT teacher head0.224
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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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