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Record W1964754293 · doi:10.1115/icone17-75601

Heat Transfer Assessment of Calandria Tube Enhancement Design Options

2009· article· en· W1964754293 on OpenAlexaffabout
Daniel M. Hobbs, Allan M. Freeburn, Richard C. Scrannage, Farshad Sani, Glenn Harvel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLoss-of-coolant accidentNuclear engineeringHeat transferCoolantHeat sinkMaterials scienceHeat exchangerDecay heatTube (container)Environmental scienceMechanical engineeringMechanicsEngineeringComposite material

Abstract

fetched live from OpenAlex

The moderator in the Calandria of a CANada Deuterium Uranium (CANDU) reactor serves as a backup heat sink in a Loss of Coolant Accident (LOCA) combined with a Loss of Emergency Coolant Injection (LOECI) accident scenario. This requires that the heat from the fuel be transferred through the ballooned/sagged Pressure Tube and the contacted Calandria Tube (CT) to the moderator. Increasing the heat transfer to the moderator will enhance the safety of the reactor by increasing the margin to Calandria Tube Rupture and ensuring the fuel is cooled. Conceptual designs were assessed to determine their potential for increasing heat transfer to the moderator in accident scenarios. Three different design options for a CT have been investigated using numerical simulation techniques. The CT design options include fins or ribs of different sizes and combinations. The fins and ribs provide improved heat transfer. Analyses results have shown that the design options yield an increased heat transfer with a minimal impact on moderator flow. The analysis has determined that the ribbed design option is superior in terms of heat transfer enhancement.

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: none
Teacher disagreement score0.867
Threshold uncertainty score0.318

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 head0.014
GPT teacher head0.230
Teacher spread0.217 · 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
Published2009
Admission routes2
Has abstractyes

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