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Record W2397828612 · doi:10.12943/cnr.2015.00061

POWER LEVEL EFFECTS ON THORIUM-BASED FUELS IN PRESSURE-TUBE HEAVY WATER REACTORS

2016· article· en· W2397828612 on OpenAlexaffvenue
Blair P. Bromley, Geoffrey W.R. Edwards, Pranavan Sambavalingam

Bibliographic record

VenueCNL Nuclear Review · 2016
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech UniversityCanadian Nuclear Laboratories
Fundersnot available
KeywordsBurnupNuclear engineeringFissile materialNeutron fluxEnvironmental scienceThorium fuel cycleThoriumNuclear physicsNeutronPhysicsEngineeringUranium

Abstract

fetched live from OpenAlex

Lattice and core physics modeling and calculations have been performed to quantify the impact of power/flux levels on the reactivity and achievable burnup for 35-element fuel bundles made with Pu/Th or U-233/Th. The fissile content in these bundles has been adjusted to produce on the order of 20 MWd/kg burnup in homogeneous cores in a 700 MWe-class pressure-tube heavy water reactor, operating on a once-through thorium cycle. Results demonstrate that the impact of the power/flux level is modest for Pu/Th fuels but significant for U-233/Th fuels. In particular, high power/flux reduces the breeding and burnup potential of U-233/Th fuels. Thus, there may be an incentive to operate reactors with U-233/Th fuels at a lower power density or to develop alternative refueling schemes that will lower the time-average specific power, thereby increasing burnup.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.210
Teacher spread0.197 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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