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Record W2291049590 · doi:10.1115/1.4032874

Computational Fluid Dynamic Simulation of the Moderator Flow in CANDU-6 Nuclear Reactors

2016· article· en· W2291049590 on OpenAlexaffabout
Foad Mehdi Zadeh, Stéphane Étienne, A. Teyssedou

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsThermal hydraulicsMechanicsComputational fluid dynamicsFlow (mathematics)Nuclear engineeringNuclear reactorNuclear reactor coreHeavy waterFluid dynamicsMaterials scienceEnvironmental sciencePhysicsNuclear physicsDeuteriumHeat transferEngineering

Abstract

fetched live from OpenAlex

For CANada Deuterium Uranium (CANDU) nuclear reactors, the characterization of the moderator thermal-hydraulic behavior under both normal and abnormal operating conditions constitutes an important safety issue. For normal operating conditions, the flow temperature distribution may produce changes on the heavy-water mass density, which in turn may affect the neutron moderation rate. Consequently, these variations influence the thermal neutron flux distribution in the reactor core. Therefore, it is fundamental to know all possible moderator flow configurations as well as the corresponding temperature distributions. In particular, any possibility of a dryout at the external wall of the Calandria tubes and consequently excessive temperature excursions must be prevented. Within this framework, this paper presents detailed two-dimensional (2D) numerical steady-state simulations for a wide range of flow conditions. Both the accuracy of the numerical approximations and the validity of some physical models used in computational fluid dynamic (CFD) codes are assessed. The numerical results are then used to construct a cartographical representation of moderator flows in CANDU-6 reactors. To support the existence of coherent flow asymmetries and eventually flow-structure oscillations, the present numerical results are also compared with the previous ones obtained using a porous medium-modeling approach.

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 score0.089
Threshold uncertainty score0.214

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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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