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Record W2041461186 · doi:10.1115/smr2011-6548

A Supercritical Water-Cooled Small Modular Reactor

2011· article· en· W2041461186 on OpenAlexaffabout
Romney B. Duffey, L.K.H. Leung, B. Sur, M. Yetisir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsSupercritical fluidNuclear engineeringTurbineModular designMaterials scienceNuclear reactor coreSubcoolingReactor pressure vesselEnvironmental scienceMechanical engineeringEngineeringBoilingComputer scienceChemistry

Abstract

fetched live from OpenAlex

A new small modular reactor (SMR) is proposed for a 300 MW(e) nuclear generating station. It is referred as the SuperSafe© Reactor (SSR) and is a scaled down version of the Canadian supercritical water-cooled reactor (SCWR), which is designed to operate at supercritical conditions (pressure of 25 MPa and fluid temperature of 625°C) at the turbine inlet with high cycle efficiencies (∼45%). The supercritical turbine technology and associated components used in the balance of plant (BOP) are similar to those in existing supercritical fossil-fired plants. The reactor core consists of fuel channels, which are submerged in a subcooled heavy-water moderator pool inside a low-pressure calandria vessel. Each fuel channel consists of a pressure tube and a ceramic insulator enclosed inside a porous stainless steel sleeve. The moderator provides cooling to fuel channels under normal operation and postulated accident scenarios. This design feature enables the use of a flash-driven passive moderator cooling — an inherent safety feature of the proposed design. A major safety goal is to achieve a passive “no core melt” configuration for the channels and 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.033
GPT teacher head0.185
Teacher spread0.152 · 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 designBench or experimental
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

Citations5
Published2011
Admission routes2
Has abstractyes

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