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Record W4283163669 · doi:10.1115/1.4054841

Physics Assessment of the Impact of Modified End Pellets on Axial Power Peaking for Advanced/Nonconventional Uranium-Based Fuels in Pressure Tube Heavy Water Reactors

2022· article· en· W4283163669 on OpenAlexafffund
Huiping V. Yan, Blair P. Bromley

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2022
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsCanadian Nuclear Laboratories
FundersAtomic Energy of Canada Limited
KeywordsBurnupNuclear engineeringPelletsMaterials scienceSpent nuclear fuelNeutron fluxNuclear fuelThorium fuel cycleDilutionBundlePower densityUraniumEnvironmental scienceNeutronNuclear physicsPower (physics)PhysicsMOX fuelThermodynamicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Axial power peaking is a phenomenon with safety implications for pressure tube heavy water reactors (PT-HWRs). Since PT-HWRs use shorter (∼50 cm) bundles, there are small axial gaps, which expose the ends of the fuel elements to more neutron flux, and therefore results in higher power density levels occurring in the ends of the fuel elements. Power peaking has the potential to cause fuel damage and failure, if the local linear element rating (LER) exceeds 57 kW/m, and may be of greater concern for advanced, higher burnup fuels. Earlier studies have been done using three-dimensional mcnp models of a PT-HWR fuel bundle with slightly enriched uranium; they demonstrated that ThO2 could be used to reduce axial power peaking in fresh fuel. This result was achieved by replacing some of the UO2 with ThO2 in the last 3 cm of fuel pellets at each end of a fuel bundle. This study extends the previous work by performing 3D neutronics and burnup calculations using serpent, to evaluate how power peaking changes with burnup. In addition, alternative dilution materials (such as depleted UO2, ZrO2, and MgO) were also evaluated. It was found that axial power peaking can be significantly reduced by using the ThO2 dilution material for fresh fuel, while ZrO2 or MgO are even more effective at higher burnup levels. Dilution materials have little impact (less than 2%) on the exit burnup of the fuel.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.296

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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
Published2022
Admission routes2
Has abstractyes

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