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Record W2041172136 · doi:10.1155/2013/352757

Simulation Strategy for the Evaluation of Neutronic Properties of a Canadian SCWR Fuel Channel

2013· article· en· W2041172136 on OpenAlexafffundabout
G. Harrisson, G. Marleau

Bibliographic record

VenueScience and Technology of Nuclear Installations · 2013
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsSupercritical fluidChannel (broadcasting)Nuclear engineeringReflector (photography)Neutron transportCoupling (piping)CoolantMechanicsMaterials scienceEngineeringMechanical engineeringNeutronNuclear physicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The Canadian Supercritical-Water-Cooled Reactor (SCWR) is a vertical pressure tube reactor cooled with supercritical light water and moderated with heavy water. For normal operation, the local conditions of the coolant (density and temperature) and fuel (temperature) vary substantially along the channel. This means that to simulate adequately the behavior of the core under operating conditions or for anticipated accident scenario, expensive 3D transport calculations for a complete fuel channel are required. Here, we propose a simulation strategy that takes into account axial variations of the local conditions and avoids 3D transport calculations. This strategy consists in replacing the 3D simulation by a series of isolated 2D calculations followed by a single 1D simulation. It is shown that this strategy is efficient because the axial coupling along the fuel channel is relatively weak. In addition, the neutronic properties of a channel with axial reflector can be modeled using a simplified 3D transport calculation.

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 score0.730
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.240
Teacher spread0.189 · 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
Published2013
Admission routes3
Has abstractyes

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