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Record W110641822 · doi:10.13182/nt06-a3687

Progress of the DUPIC Fuel Compatibility Analysis - I: Reactor Physics

2006· article· en· W110641822 on OpenAlexaboutno aff
Hangbok Choi, Chang Joon Jeong, Gyu Hong Roh, Bo Wook Rhee, Myung Seung Yang

Bibliographic record

VenueNuclear Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear engineeringFissile materialFuel element failureThorium fuel cycleDecay heatSpent nuclear fuelNuclear fuel cycleNuclear reactor coreNuclear fuelFuel cycleNuclear chemistryMOX fuelPhysicsEngineeringNuclear physicsChemistryPlutoniumNeutron

Abstract

fetched live from OpenAlex

Since 1992, the direct use of spent pressurized water reactor fuel in Canada deuterium uranium (CANDU) reactors (DUPIC) has been studied as an alternative to the once-through fuel cycle. The DUPIC fuel cycle study is focused on the analysis of technical feasibility, the fabrication of DUPIC fuels for irradiation tests, and the demonstration of the DUPIC fuel performance. The feasibility analysis was conducted for the compatibility of the DUPIC fuel with existing CANDU-6 reactors from the viewpoints of reactor physics, reactor safety, fuel cycle economics, etc. This study has summarized the intermediate results of the DUPIC fuel compatibility analysis, which includes the CANDU reactor physics design requirements, DUPIC fuel core physics design method, performance of the DUPIC fuel core, and the regional overpower trip setpoint. The physics analysis showed the CANDU-6 reactor can accommodate the DUPIC fuel without deteriorating the physics design requirements by adjusting the fuel management scheme if the fissile content of the DUPIC fuel is tightly controlled.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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
Published2006
Admission routes1
Has abstractyes

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