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Record W2914611465 · doi:10.1115/1.4042597

Analysis of Shutdown System Effectiveness in the Canadian Super Critical Water Reactor Using Coupled Thermal Hydraulics and Three-Dimensional Neutron Kinetics

2019· article· en· W2914611465 on OpenAlexafffundabout
David Hummel, D. R. Novog

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsMcMaster University
FundersOffice of Energy Research and Development
KeywordsShutdownCoolantNuclear engineeringThermal hydraulicsDelayed neutronMechanicsNuclear reactor coreHydraulicsHeavy waterNeutron transportNeutronMaterials scienceSupercritical fluidEnvironmental scienceNeutron temperatureNuclear physicsHeat transferPhysicsThermodynamicsDeuteriumEngineering

Abstract

fetched live from OpenAlex

Abstract The Canadian supercritical water-cooled reactor concept features a re-entrant fuel channel wherein coolant first travels down a center flow tube and then up around the fuel elements. Previous work demonstrated that in cases of sudden coolant flow reduction or reversal (such as that which would result from a large pipe break near the core inlet), the coolant density reduction around the fuel has a positive reactivity effect that results in a power excursion. Such a transient is inherently self-terminating since the inevitable density reduction in the center flow tube has a very large negative reactivity effect. Nevertheless, a brief power pulse would ensue. In this work, the possibility of mitigating the power pulse with a fast-acting shutdown system was explored. The shutdown system model, consisting of bottom-inserted neutron absorbing blades and realistic estimates of insertion rates and trip conditions, was added to a full-core coupled spatial neutron kinetics and thermal-hydraulics model. It was demonstrated that such a system can effectively mitigate both the peak magnitude of the power excursion and its duration.

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)0.001
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 score0.706
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.220
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 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
Published2019
Admission routes3
Has abstractyes

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