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ICONE23-1198 FUELLING STUDY USING BURNABLE NEUTRON ABSORBERS : MITIGATING FUELLING TRANSIENTS AND IMPROVING POWER COMPLIANCE MARGIN DURING REFUELLING

2015· article· en· W2915204566 on OpenAlexaff
Jason J. Song, Paul K. Chan, Hugues W. Bonin

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsNuclear engineeringEuropiumEnriched uraniumUranium oxideNeutron poisonMaterials scienceNeutron captureNeutronEnvironmental scienceNeutron temperatureUraniumNuclear physicsEngineeringPhysicsMetallurgy

Abstract

fetched live from OpenAlex

A fuelling study for CANDU reactors is conducted using natural uranium (NU) fuels doped with trace amounts of burnable neutron absorbers. The burnable absorbers of interest include gadolinium oxide (Gd_2O_3) and europium oxide (Eu_2O_3). The study incorporates fuel-lattice simulations as well as refuelling and core-following simulations to quantify the impact in the in-core behavior of the fuel. The fuel lattice simulations were conducted using the WIMS-AECL code while refuelling and core-following simulations were conducted using the Reactor Fuelling Simulation Program (RFSP). This paper presents the improvements in the safety margins gained by the use of burnable absorbers during normal operation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.736

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 science0.0010.000
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 head0.043
GPT teacher head0.250
Teacher spread0.207 · 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 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
Published2015
Admission routes1
Has abstractyes

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