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Record W2752203539 · doi:10.1115/1.4037807

Thermal-Hydraulics Program in Support of Canadian SCWR Concept Development

2017· article· en· W2752203539 on OpenAlexafffundabout
Laurence K.H. Leung, A. Nava-Dominguez

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsCanadian Nuclear Laboratories
FundersCanadian Nuclear Laboratories
KeywordsThermal hydraulicsSupercritical fluidHeat transferHydraulicsRefrigerantMaterials scienceCritical heat fluxMechanicsNozzleFlow (mathematics)ThermalNuclear engineeringMechanical engineeringThermodynamicsHeat transfer coefficientHeat exchangerEngineeringPhysics

Abstract

fetched live from OpenAlex

The thermal-hydraulics program in support of the development of the Canadian supercritical water-cooled reactor (SCWR) concept has undergone several phases. It focused on key parameters such as heat transfer, critical flow, and stability of fluids at supercritical pressures. Heat-transfer experiments were performed with tubes, annuli, and bundles in water, carbon dioxide (CO2), or refrigerant flows. Data from these experiments have led to enhancement in understanding of the phenomena, improved prediction methods, and verified analytical tools. In addition, these experiments facilitated the investigation of separate effects on heat transfer (such as geometry, diameter, spacing device, and transient). Chocking flow characteristics were studied experimentally with sharp-edged nozzles of two different sizes of opening. Experimental data have been applied in improving the critical-flow correlation in support of accident analyses. A one-dimensional (1D) analytical model for instability phenomena has been developed and assessed against the latest experimental data for quantifying the prediction capability and applicability.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.209

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 designObservational
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
Published2017
Admission routes3
Has abstractyes

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