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Record W3014903481 · doi:10.1115/1.4046783

Evaluation of Friction-Factor Correlations at Supercritical Water Conditions in Support of the Canadian SCWR

2020· article· en· W3014903481 on OpenAlexaffabout
A. Nava-Dominguez

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsSupercritical fluidPressure dropFriction factorNuclear engineeringMaterials scienceMechanical engineeringEnvironmental scienceMechanicsEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract Canada is participating in the Generation IV International Forum with the main focus on the pressure-tube-type supercritical water-cooled reactor (SCWR) concept. The Canadian SCWR concept is a heavy-water moderated and light-water-cooled reactor. The R&D framework for the development of the Canadian SCWR fuel-assembly concept includes experiments and analyses, including subchannel code development and applications. This paper focuses on the modeling of the hydraulic resistance under supercritical conditions, with or without the wire-wrap spacers. More specifically, it presents an assessment of three friction factor correlations developed for supercritical conditions. A literature survey of wire-wrap hydraulic models is presented. The assessment of the supercritical friction factor correlations and wire-wrap hydraulic models is carried out using three pressure drop experimental datasets, using the subchannel code ASSERT-PV V3.2m2.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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