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Record W2898330389 · doi:10.1115/icone26-81461

Convective Heat Transfer in CANDU Spent Fuel Racks After a Loss of Coolant

2018· article· en· W2898330389 on OpenAlexaff
Derek Logtenberg, W. Grant, Paul K. Chan, E. C. Corcoran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Safety CommissionRoyal Military College of Canada
Fundersnot available
KeywordsCoolantNuclear engineeringEnvironmental scienceThermal hydraulicsSpent fuel poolHeat transferDecay heatSpent nuclear fuelBundleLoss-of-coolant accidentFuel element failureWaste managementMaterials scienceMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The event at the Fukushima Daiichi Spent Fuel Pools (SFPs) has renewed interest in quantifying the safety margins related to loss of coolant accidents in Irradiated Fuel Bays (IFBs). Thermal-hydraulic analyses of exposed spent CANDU fuel has been limited to a small number of bundles due to its complex bundle geometry and open rack design. This paper presents a process to predict the steady state temperature and velocity of air as it passes through a rack of spent fuel using analytical models and Computational Fluid Dynamics (CFDs) techniques. The scenario acts as lower bound estimate for the effectiveness of convection during a complete loss of coolant in a fuel bay by examining the heat-up of a stand-alone rack without flow resistance of the bundles. The correct incorporation of flow resistance is a necessary step before conclusions are made about the available safety margins of irradiated fuel bays.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.227
Teacher spread0.214 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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