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Record W2045202391 · doi:10.1115/1.4002870

CANDU Reactor Space-Time Kinetic Model for Load Following Studies

2010· article· en· W2045202391 on OpenAlexafffundabout
Lingzhi Xia, Jin Jiang

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2010
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringMcMaster University
KeywordsNuclear engineeringNeutron fluxNeutron transportMATLABNeutronDelayed neutronFlux (metallurgy)Nuclear reactorDiffusionPhysicsComputer scienceControl theory (sociology)MechanicsNuclear physicsNeutron temperatureMaterials scienceEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract This paper presents the development of a three-dimensional space-time neutronic kinetic model of a Canadian deuterium uranium (CANDU) reactor using a modal method. In this method, the reactor space-time neutron flux is synthesized by a time-weighted series of precalculated neutron flux modes. The modes are eigenfunctions of the governing neutron diffusion equation during reference steady-state operation. The xenon effect has also been considered. The reactor model is then implemented within a simulation platform of a CANDU6 reactor regulating system in MATLAB/SIMULINK. A nondimensionalized SIMULINK representation of the reactor kinetic model is established. The behavior of the reactor during load following transients has been simulated using the developed reactor-modeling module. The simulation results prove the efficiency of the model. A three-dimensional neutron flux distribution during transients is represented.

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)0.000
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: none
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations6
Published2010
Admission routes3
Has abstractyes

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